Issue 408: ARMA and ARIMA models #438

seabbs · 2024-08-27T21:08:04Z

This PR closes #407 and starts towards adding submodels everywhere (#372). To close this out I need to:

Remove all standard deviation priors from latent models
Use HierarchicalNormal as the default residual process
Decide if we need helper constructors for our current models (i.e do we want AR(std_prior)) to work)
Write arma and arima helper functions. Deciding how much flexibility to expose (plan is to restrict to just p,q,r priors and the standard deviation prior of the residuals).
Review and improve tests

github-actions · 2024-08-27T21:08:30Z

Try this Pull Request!

Open Julia and type:

import Pkg
Pkg.activate(temp=true)
Pkg.add(url="https://github.com/CDCgov/Rt-without-renewal", rev="issue408", subdir="EpiAware")
using EpiAware

SamuelBrand1 · 2024-09-20T09:06:40Z

Hey do you need any help with this one?

seabbs · 2024-09-20T09:48:03Z

no its okay I just need to get off primarycensoreddist and then can get this unstuck

SamuelBrand1 · 2024-10-11T10:46:54Z

Seeing updates 👍

seabbs · 2024-10-11T11:19:43Z

ha yes this PR has got really out of hand - just doing some lsthm admin stuff then back on this

* Patch: Switch to fork of benchmarkCI (#520) * patch to fork of benchmarkCI * put fork version of BenchmarkCI in [sources] * swap order * add EpiAware [source] * fix path * rm benchmarkCI from project * Patch fix: add `Manifest.toml` to benchmarking (#524) * trigger * Update benchmark.yaml * Update benchmark.yaml * commit benchmark Manifest * try alternate approach * Update benchmark.yaml * Update EpiMethod.jl * Update benchmark.yaml * change baseline to origin/main * remove trigger * rm other trigger * Issue 465: Add an infection generating model for ODE problems (#510) * CompatHelper: bump compat for Turing to 0.35 for package EpiAware, (drop existing compat) (#516) * CompatHelper: bump compat for Turing to 0.35 for package EpiAware, (drop existing compat) * Update Project.toml * fix Project.toml --------- Co-authored-by: CompatHelper Julia <[email protected]> Co-authored-by: Sam Abbott <[email protected]> Co-authored-by: Samuel Brand <[email protected]> Co-authored-by: Samuel Brand <[email protected]> * CompatHelper: bump compat for DynamicPPL to 0.30 for package EpiAware, (drop existing compat) (#528) Co-authored-by: CompatHelper Julia <[email protected]> * rename IDD -> IID * rename test file * Issue 529: Create null Latent model (#530) * Null Latent model * Null Latent model * fix doctest * fix generate_epiaware unit tests New usage of RW * fix turing method test underlying std of step size changed name * fix broadcast test Underlying std param changed name * fix HN unit test Default std prior had changed * fix AR unit tests --------- Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> Co-authored-by: CompatHelper Julia <[email protected]> Co-authored-by: Sam Abbott <[email protected]>

SamuelBrand1 · 2024-12-10T12:36:32Z

I think a field name change in AR has affected the notebook again.

… into issue408

SamuelBrand1 · 2024-12-10T12:48:25Z

NB: I did quick fix of the prior ordering on top of the mishra note fix of @seabbs

github-actions · 2024-12-10T14:09:06Z

Benchmark result

Judge result

Benchmark Report for /home/runner/work/Rt-without-renewal/Rt-without-renewal

Job Properties

Time of benchmarks:
- Target: 10 Dec 2024 - 13:34
- Baseline: 10 Dec 2024 - 14:08
Package commits:
- Target: bacd87
- Baseline: 8dc444
Julia commits:
- Target: 5e9a32
- Baseline: 5e9a32
Julia command flags:
- Target: None
- Baseline: None
Environment variables:
- Target: None
- Baseline: None

Results

A ratio greater than 1.0 denotes a possible regression (marked with ❌), while a ratio less
than 1.0 denotes a possible improvement (marked with ✅). Only significant results - results
that indicate possible regressions or improvements - are shown below (thus, an empty table means that all
benchmark results remained invariant between builds).

ID	time ratio	memory ratio
`["EpiLatentModels", "AR", "evaluation", "linked"]`	2.69 (5%) ❌	2.01 (1%) ❌
`["EpiLatentModels", "AR", "evaluation", "standard"]`	2.74 (5%) ❌	1.80 (1%) ❌
`["EpiLatentModels", "AR", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"]`	2.27 (5%) ❌	1.35 (1%) ❌
`["EpiLatentModels", "AR", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"]`	2.34 (5%) ❌	1.24 (1%) ❌
`["EpiLatentModels", "AR", "gradient", "ADTypes.AutoReverseDiff()", "linked"]`	1.14 (5%) ❌	1.09 (1%) ❌
`["EpiLatentModels", "AR", "gradient", "ADTypes.AutoReverseDiff()", "standard"]`	1.17 (5%) ❌	1.08 (1%) ❌
`["EpiLatentModels", "BroadcastLatentModel", "evaluation", "linked"]`	1.56 (5%) ❌	1.59 (1%) ❌
`["EpiLatentModels", "BroadcastLatentModel", "evaluation", "standard"]`	1.47 (5%) ❌	1.39 (1%) ❌
`["EpiLatentModels", "BroadcastLatentModel", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"]`	1.46 (5%) ❌	1.33 (1%) ❌
`["EpiLatentModels", "BroadcastLatentModel", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"]`	1.28 (5%) ❌	1.18 (1%) ❌
`["EpiLatentModels", "BroadcastLatentModel", "gradient", "ADTypes.AutoReverseDiff()", "linked"]`	1.04 (5%)	1.09 (1%) ❌
`["EpiLatentModels", "BroadcastLatentModel", "gradient", "ADTypes.AutoReverseDiff()", "standard"]`	1.04 (5%)	1.07 (1%) ❌
`["EpiLatentModels", "BroadcastLatentModel", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	0.95 (5%)	0.85 (1%) ✅
`["EpiLatentModels", "BroadcastLatentModel", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	0.95 (5%) ✅	0.85 (1%) ✅
`["EpiLatentModels", "CombineLatentModels", "evaluation", "linked"]`	1.14 (5%) ❌	1.11 (1%) ❌
`["EpiLatentModels", "CombineLatentModels", "evaluation", "standard"]`	1.16 (5%) ❌	1.07 (1%) ❌
`["EpiLatentModels", "CombineLatentModels", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"]`	1.14 (5%) ❌	1.09 (1%) ❌
`["EpiLatentModels", "CombineLatentModels", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"]`	1.14 (5%) ❌	1.06 (1%) ❌
`["EpiLatentModels", "CombineLatentModels", "gradient", "ADTypes.AutoReverseDiff()", "linked"]`	1.06 (5%) ❌	1.06 (1%) ❌
`["EpiLatentModels", "CombineLatentModels", "gradient", "ADTypes.AutoReverseDiff()", "standard"]`	1.05 (5%) ❌	1.04 (1%) ❌
`["EpiLatentModels", "ConcatLatentModels", "evaluation", "linked"]`	1.22 (5%) ❌	1.12 (1%) ❌
`["EpiLatentModels", "ConcatLatentModels", "evaluation", "standard"]`	1.22 (5%) ❌	1.10 (1%) ❌
`["EpiLatentModels", "ConcatLatentModels", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"]`	1.22 (5%) ❌	1.11 (1%) ❌
`["EpiLatentModels", "ConcatLatentModels", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"]`	1.22 (5%) ❌	1.08 (1%) ❌
`["EpiLatentModels", "ConcatLatentModels", "gradient", "ADTypes.AutoReverseDiff()", "linked"]`	1.10 (5%) ❌	1.07 (1%) ❌
`["EpiLatentModels", "ConcatLatentModels", "gradient", "ADTypes.AutoReverseDiff()", "standard"]`	1.10 (5%) ❌	1.06 (1%) ❌
`["EpiLatentModels", "ConcatLatentModels", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	0.96 (5%)	0.92 (1%) ✅
`["EpiLatentModels", "ConcatLatentModels", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	0.96 (5%)	0.92 (1%) ✅
`["EpiLatentModels", "DiffLatentModel", "evaluation", "linked"]`	1.99 (5%) ❌	1.76 (1%) ❌
`["EpiLatentModels", "DiffLatentModel", "evaluation", "standard"]`	1.99 (5%) ❌	1.66 (1%) ❌
`["EpiLatentModels", "DiffLatentModel", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"]`	1.92 (5%) ❌	1.30 (1%) ❌
`["EpiLatentModels", "DiffLatentModel", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"]`	1.91 (5%) ❌	1.20 (1%) ❌
`["EpiLatentModels", "DiffLatentModel", "gradient", "ADTypes.AutoReverseDiff()", "linked"]`	1.14 (5%) ❌	1.10 (1%) ❌
`["EpiLatentModels", "DiffLatentModel", "gradient", "ADTypes.AutoReverseDiff()", "standard"]`	1.14 (5%) ❌	1.07 (1%) ❌
`["EpiLatentModels", "DiffLatentModel", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	1.00 (5%)	0.91 (1%) ✅
`["EpiLatentModels", "DiffLatentModel", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	0.99 (5%)	0.91 (1%) ✅
`["EpiLatentModels", "HierarchicalNormal", "evaluation", "linked"]`	1.38 (5%) ❌	1.45 (1%) ❌
`["EpiLatentModels", "HierarchicalNormal", "evaluation", "standard"]`	1.41 (5%) ❌	1.36 (1%) ❌
`["EpiLatentModels", "HierarchicalNormal", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"]`	1.16 (5%) ❌	1.09 (1%) ❌
`["EpiLatentModels", "HierarchicalNormal", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"]`	1.23 (5%) ❌	1.06 (1%) ❌
`["EpiLatentModels", "HierarchicalNormal", "gradient", "ADTypes.AutoReverseDiff()", "linked"]`	0.83 (5%) ✅	0.90 (1%) ✅
`["EpiLatentModels", "HierarchicalNormal", "gradient", "ADTypes.AutoReverseDiff()", "standard"]`	0.74 (5%) ✅	0.85 (1%) ✅
`["EpiLatentModels", "HierarchicalNormal", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	0.86 (5%) ✅	0.29 (1%) ✅
`["EpiLatentModels", "HierarchicalNormal", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	0.84 (5%) ✅	0.29 (1%) ✅
`["EpiLatentModels", "PrefixLatentModel", "evaluation", "linked"]`	1.09 (5%) ❌	1.14 (1%) ❌
`["EpiLatentModels", "PrefixLatentModel", "evaluation", "standard"]`	1.10 (5%) ❌	1.11 (1%) ❌
`["EpiLatentModels", "PrefixLatentModel", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"]`	1.11 (5%) ❌	1.06 (1%) ❌
`["EpiLatentModels", "PrefixLatentModel", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"]`	1.12 (5%) ❌	1.05 (1%) ❌
`["EpiLatentModels", "PrefixLatentModel", "gradient", "ADTypes.AutoReverseDiff()", "linked"]`	0.86 (5%) ✅	0.91 (1%) ✅
`["EpiLatentModels", "PrefixLatentModel", "gradient", "ADTypes.AutoReverseDiff()", "standard"]`	0.76 (5%) ✅	0.87 (1%) ✅
`["EpiLatentModels", "PrefixLatentModel", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	0.85 (5%) ✅	0.29 (1%) ✅
`["EpiLatentModels", "PrefixLatentModel", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	0.83 (5%) ✅	0.29 (1%) ✅
`["EpiLatentModels", "RandomWalk", "evaluation", "linked"]`	1.80 (5%) ❌	1.94 (1%) ❌
`["EpiLatentModels", "RandomWalk", "evaluation", "standard"]`	1.64 (5%) ❌	1.57 (1%) ❌
`["EpiLatentModels", "RandomWalk", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"]`	1.30 (5%) ❌	1.19 (1%) ❌
`["EpiLatentModels", "RandomWalk", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"]`	1.23 (5%) ❌	1.08 (1%) ❌
`["EpiLatentModels", "RandomWalk", "gradient", "ADTypes.AutoReverseDiff()", "linked"]`	0.98 (5%)	1.03 (1%) ❌
`["EpiLatentModels", "RandomWalk", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	0.89 (5%) ✅	0.84 (1%) ✅
`["EpiLatentModels", "RandomWalk", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	0.90 (5%) ✅	0.84 (1%) ✅
`["EpiLatentModels", "RecordExpectedLatent", "evaluation", "linked"]`	1.30 (5%) ❌	1.38 (1%) ❌
`["EpiLatentModels", "RecordExpectedLatent", "evaluation", "standard"]`	1.29 (5%) ❌	1.33 (1%) ❌
`["EpiLatentModels", "RecordExpectedLatent", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"]`	1.34 (5%) ❌	1.26 (1%) ❌
`["EpiLatentModels", "RecordExpectedLatent", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"]`	1.34 (5%) ❌	1.21 (1%) ❌
`["EpiLatentModels", "RecordExpectedLatent", "gradient", "ADTypes.AutoReverseDiff()", "linked"]`	0.82 (5%) ✅	0.91 (1%) ✅
`["EpiLatentModels", "RecordExpectedLatent", "gradient", "ADTypes.AutoReverseDiff()", "standard"]`	0.76 (5%) ✅	0.86 (1%) ✅
`["EpiLatentModels", "RecordExpectedLatent", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	0.85 (5%) ✅	0.32 (1%) ✅
`["EpiLatentModels", "RecordExpectedLatent", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	0.85 (5%) ✅	0.32 (1%) ✅
`["EpiLatentModels", "broadcast_dayofweek", "evaluation", "linked"]`	1.47 (5%) ❌	1.44 (1%) ❌
`["EpiLatentModels", "broadcast_dayofweek", "evaluation", "standard"]`	1.37 (5%) ❌	1.31 (1%) ❌
`["EpiLatentModels", "broadcast_dayofweek", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"]`	1.25 (5%) ❌	1.16 (1%) ❌
`["EpiLatentModels", "broadcast_dayofweek", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"]`	1.16 (5%) ❌	1.08 (1%) ❌
`["EpiLatentModels", "broadcast_dayofweek", "gradient", "ADTypes.AutoReverseDiff()", "linked"]`	1.00 (5%)	1.04 (1%) ❌
`["EpiLatentModels", "broadcast_dayofweek", "gradient", "ADTypes.AutoReverseDiff()", "standard"]`	0.98 (5%)	1.02 (1%) ❌
`["EpiLatentModels", "broadcast_dayofweek", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	0.95 (5%) ✅	0.84 (1%) ✅
`["EpiLatentModels", "broadcast_dayofweek", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	0.95 (5%) ✅	0.84 (1%) ✅
`["EpiLatentModels", "broadcast_weekly", "evaluation", "linked"]`	2.01 (5%) ❌	1.74 (1%) ❌
`["EpiLatentModels", "broadcast_weekly", "evaluation", "standard"]`	2.07 (5%) ❌	1.65 (1%) ❌
`["EpiLatentModels", "broadcast_weekly", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"]`	1.94 (5%) ❌	1.47 (1%) ❌
`["EpiLatentModels", "broadcast_weekly", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"]`	1.93 (5%) ❌	1.38 (1%) ❌
`["EpiLatentModels", "broadcast_weekly", "gradient", "ADTypes.AutoReverseDiff()", "linked"]`	1.19 (5%) ❌	1.12 (1%) ❌
`["EpiLatentModels", "broadcast_weekly", "gradient", "ADTypes.AutoReverseDiff()", "standard"]`	1.21 (5%) ❌	1.11 (1%) ❌
`["EpiObsModels", "Ascertainment", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"]`	1.05 (5%) ❌	1.00 (1%)
`["EpiObsModels", "ascertainment_dayofweek", "evaluation", "linked"]`	1.07 (5%) ❌	1.05 (1%) ❌
`["EpiObsModels", "ascertainment_dayofweek", "evaluation", "standard"]`	1.06 (5%) ❌	1.04 (1%) ❌
`["EpiObsModels", "ascertainment_dayofweek", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"]`	1.06 (5%) ❌	1.03 (1%) ❌
`["EpiObsModels", "ascertainment_dayofweek", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"]`	1.05 (5%)	1.02 (1%) ❌
`["EpiObsModels", "ascertainment_dayofweek", "gradient", "ADTypes.AutoReverseDiff()", "linked"]`	0.94 (5%) ✅	0.97 (1%) ✅
`["EpiObsModels", "ascertainment_dayofweek", "gradient", "ADTypes.AutoReverseDiff()", "standard"]`	0.92 (5%) ✅	0.96 (1%) ✅
`["EpiObsModels", "ascertainment_dayofweek", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	0.95 (5%) ✅	0.32 (1%) ✅
`["EpiObsModels", "ascertainment_dayofweek", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	0.96 (5%)	0.32 (1%) ✅
`["EpiObsModels", "observation_error", "missing obs", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	1.24 (5%) ❌	1.00 (1%)
`["EpiObsModels", "observation_error", "missing obs", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	1.21 (5%) ❌	1.00 (1%)
`["EpiObsModels", "observation_error", "no missing obs", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"]`	1.05 (5%) ❌	1.00 (1%)
`["EpiObsModels", "observation_error", "no missing obs", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	1.24 (5%) ❌	1.00 (1%)
`["EpiObsModels", "observation_error", "no missing obs", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	1.20 (5%) ❌	1.00 (1%)
`["EpiObsModels", "observation_error", "partially missing obs", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	1.30 (5%) ❌	1.00 (1%)
`["EpiObsModels", "observation_error", "partially missing obs", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	1.21 (5%) ❌	1.00 (1%)

Benchmark Group List

Here's a list of all the benchmark groups executed by this job:

["EpiAwareUtils"]
["EpiInfModels", "DirectInfections", "evaluation"]
["EpiInfModels", "DirectInfections", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)"]
["EpiInfModels", "DirectInfections", "gradient", "ADTypes.AutoReverseDiff()"]
["EpiInfModels", "DirectInfections", "gradient", "ADTypes.AutoReverseDiff(compile=true)"]
["EpiInfModels", "ExpGrowthRate", "evaluation"]
["EpiInfModels", "ExpGrowthRate", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)"]
["EpiInfModels", "ExpGrowthRate", "gradient", "ADTypes.AutoReverseDiff()"]
["EpiInfModels", "ExpGrowthRate", "gradient", "ADTypes.AutoReverseDiff(compile=true)"]
["EpiLatentModels", "AR", "evaluation"]
["EpiLatentModels", "AR", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)"]
["EpiLatentModels", "AR", "gradient", "ADTypes.AutoReverseDiff()"]
["EpiLatentModels", "AR", "gradient", "ADTypes.AutoReverseDiff(compile=true)"]
["EpiLatentModels", "BroadcastLatentModel", "evaluation"]
["EpiLatentModels", "BroadcastLatentModel", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)"]
["EpiLatentModels", "BroadcastLatentModel", "gradient", "ADTypes.AutoReverseDiff()"]
["EpiLatentModels", "BroadcastLatentModel", "gradient", "ADTypes.AutoReverseDiff(compile=true)"]
["EpiLatentModels", "CombineLatentModels", "evaluation"]
["EpiLatentModels", "CombineLatentModels", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)"]
["EpiLatentModels", "CombineLatentModels", "gradient", "ADTypes.AutoReverseDiff()"]
["EpiLatentModels", "CombineLatentModels", "gradient", "ADTypes.AutoReverseDiff(compile=true)"]
["EpiLatentModels", "ConcatLatentModels", "evaluation"]
["EpiLatentModels", "ConcatLatentModels", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)"]
["EpiLatentModels", "ConcatLatentModels", "gradient", "ADTypes.AutoReverseDiff()"]
["EpiLatentModels", "ConcatLatentModels", "gradient", "ADTypes.AutoReverseDiff(compile=true)"]
["EpiLatentModels", "DiffLatentModel", "evaluation"]
["EpiLatentModels", "DiffLatentModel", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)"]
["EpiLatentModels", "DiffLatentModel", "gradient", "ADTypes.AutoReverseDiff()"]
["EpiLatentModels", "DiffLatentModel", "gradient", "ADTypes.AutoReverseDiff(compile=true)"]
["EpiLatentModels", "HierarchicalNormal", "evaluation"]
["EpiLatentModels", "HierarchicalNormal", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)"]
["EpiLatentModels", "HierarchicalNormal", "gradient", "ADTypes.AutoReverseDiff()"]
["EpiLatentModels", "HierarchicalNormal", "gradient", "ADTypes.AutoReverseDiff(compile=true)"]
["EpiLatentModels", "Intercept", "evaluation"]
["EpiLatentModels", "Intercept", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)"]
["EpiLatentModels", "Intercept", "gradient", "ADTypes.AutoReverseDiff()"]
["EpiLatentModels", "Intercept", "gradient", "ADTypes.AutoReverseDiff(compile=true)"]
["EpiLatentModels", "PrefixLatentModel", "evaluation"]
["EpiLatentModels", "PrefixLatentModel", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)"]
["EpiLatentModels", "PrefixLatentModel", "gradient", "ADTypes.AutoReverseDiff()"]
["EpiLatentModels", "PrefixLatentModel", "gradient", "ADTypes.AutoReverseDiff(compile=true)"]
["EpiLatentModels", "RandomWalk", "evaluation"]
["EpiLatentModels", "RandomWalk", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)"]
["EpiLatentModels", "RandomWalk", "gradient", "ADTypes.AutoReverseDiff()"]
["EpiLatentModels", "RandomWalk", "gradient", "ADTypes.AutoReverseDiff(compile=true)"]
["EpiLatentModels", "RecordExpectedLatent", "evaluation"]
["EpiLatentModels", "RecordExpectedLatent", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)"]
["EpiLatentModels", "RecordExpectedLatent", "gradient", "ADTypes.AutoReverseDiff()"]
["EpiLatentModels", "RecordExpectedLatent", "gradient", "ADTypes.AutoReverseDiff(compile=true)"]
["EpiLatentModels", "TransformLatentModel", "evaluation"]
["EpiLatentModels", "TransformLatentModel", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)"]
["EpiLatentModels", "TransformLatentModel", "gradient", "ADTypes.AutoReverseDiff()"]
["EpiLatentModels", "TransformLatentModel", "gradient", "ADTypes.AutoReverseDiff(compile=true)"]
["EpiLatentModels", "broadcast_dayofweek", "evaluation"]
["EpiLatentModels", "broadcast_dayofweek", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)"]
["EpiLatentModels", "broadcast_dayofweek", "gradient", "ADTypes.AutoReverseDiff()"]
["EpiLatentModels", "broadcast_dayofweek", "gradient", "ADTypes.AutoReverseDiff(compile=true)"]
["EpiLatentModels", "broadcast_weekly", "evaluation"]
["EpiLatentModels", "broadcast_weekly", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)"]
["EpiLatentModels", "broadcast_weekly", "gradient", "ADTypes.AutoReverseDiff()"]
["EpiLatentModels", "broadcast_weekly", "gradient", "ADTypes.AutoReverseDiff(compile=true)"]
["EpiObsModels", "Ascertainment", "evaluation"]
["EpiObsModels", "Ascertainment", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)"]
["EpiObsModels", "Ascertainment", "gradient", "ADTypes.AutoReverseDiff()"]
["EpiObsModels", "Ascertainment", "gradient", "ADTypes.AutoReverseDiff(compile=true)"]
["EpiObsModels", "LatentDelay", "evaluation"]
["EpiObsModels", "LatentDelay", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)"]
["EpiObsModels", "LatentDelay", "gradient", "ADTypes.AutoReverseDiff()"]
["EpiObsModels", "LatentDelay", "gradient", "ADTypes.AutoReverseDiff(compile=true)"]
["EpiObsModels", "NegativeBinomialError", "evaluation"]
["EpiObsModels", "NegativeBinomialError", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)"]
["EpiObsModels", "NegativeBinomialError", "gradient", "ADTypes.AutoReverseDiff()"]
["EpiObsModels", "NegativeBinomialError", "gradient", "ADTypes.AutoReverseDiff(compile=true)"]
["EpiObsModels", "PoissonError", "evaluation"]
["EpiObsModels", "PoissonError", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)"]
["EpiObsModels", "PoissonError", "gradient", "ADTypes.AutoReverseDiff()"]
["EpiObsModels", "PoissonError", "gradient", "ADTypes.AutoReverseDiff(compile=true)"]
["EpiObsModels", "PrefixObservationModel", "evaluation"]
["EpiObsModels", "PrefixObservationModel", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)"]
["EpiObsModels", "PrefixObservationModel", "gradient", "ADTypes.AutoReverseDiff()"]
["EpiObsModels", "PrefixObservationModel", "gradient", "ADTypes.AutoReverseDiff(compile=true)"]
["EpiObsModels", "RecordExpectedObs", "evaluation"]
["EpiObsModels", "RecordExpectedObs", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)"]
["EpiObsModels", "RecordExpectedObs", "gradient", "ADTypes.AutoReverseDiff()"]
["EpiObsModels", "RecordExpectedObs", "gradient", "ADTypes.AutoReverseDiff(compile=true)"]
["EpiObsModels", "StackObservationModels", "evaluation"]
["EpiObsModels", "StackObservationModels", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)"]
["EpiObsModels", "StackObservationModels", "gradient", "ADTypes.AutoReverseDiff()"]
["EpiObsModels", "StackObservationModels", "gradient", "ADTypes.AutoReverseDiff(compile=true)"]
["EpiObsModels", "TransformObservationModel", "evaluation"]
["EpiObsModels", "TransformObservationModel", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)"]
["EpiObsModels", "TransformObservationModel", "gradient", "ADTypes.AutoReverseDiff()"]
["EpiObsModels", "TransformObservationModel", "gradient", "ADTypes.AutoReverseDiff(compile=true)"]
["EpiObsModels", "ascertainment_dayofweek", "evaluation"]
["EpiObsModels", "ascertainment_dayofweek", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)"]
["EpiObsModels", "ascertainment_dayofweek", "gradient", "ADTypes.AutoReverseDiff()"]
["EpiObsModels", "ascertainment_dayofweek", "gradient", "ADTypes.AutoReverseDiff(compile=true)"]
["EpiObsModels", "observation_error", "missing obs", "evaluation"]
["EpiObsModels", "observation_error", "missing obs", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)"]
["EpiObsModels", "observation_error", "missing obs", "gradient", "ADTypes.AutoReverseDiff()"]
["EpiObsModels", "observation_error", "missing obs", "gradient", "ADTypes.AutoReverseDiff(compile=true)"]
["EpiObsModels", "observation_error", "no missing obs", "evaluation"]
["EpiObsModels", "observation_error", "no missing obs", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)"]
["EpiObsModels", "observation_error", "no missing obs", "gradient", "ADTypes.AutoReverseDiff()"]
["EpiObsModels", "observation_error", "no missing obs", "gradient", "ADTypes.AutoReverseDiff(compile=true)"]
["EpiObsModels", "observation_error", "partially missing obs", "evaluation"]
["EpiObsModels", "observation_error", "partially missing obs", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)"]
["EpiObsModels", "observation_error", "partially missing obs", "gradient", "ADTypes.AutoReverseDiff()"]
["EpiObsModels", "observation_error", "partially missing obs", "gradient", "ADTypes.AutoReverseDiff(compile=true)"]

Julia versioninfo

Target

Julia Version 1.11.2
Commit 5e9a32e7af2 (2024-12-01 20:02 UTC)
Build Info:
  Official https://julialang.org/ release
Platform Info:
  OS: Linux (x86_64-linux-gnu)
      Ubuntu 22.04.5 LTS
  uname: Linux 6.5.0-1025-azure #26~22.04.1-Ubuntu SMP Thu Jul 11 22:33:04 UTC 2024 x86_64 x86_64
  CPU: AMD EPYC 7763 64-Core Processor: 
              speed         user         nice          sys         idle          irq
       #1     0 MHz      10728 s          0 s        821 s      17369 s          0 s
       #2     0 MHz      10339 s          0 s        801 s      17785 s          0 s
       #3     0 MHz      10015 s          0 s        816 s      18071 s          0 s
       #4     0 MHz       9858 s          0 s        757 s      18299 s          0 s
  Memory: 15.606491088867188 GB (12870.91796875 MB free)
  Uptime: 2898.04 sec
  Load Avg:  1.09  1.06  1.14
  WORD_SIZE: 64
  LLVM: libLLVM-16.0.6 (ORCJIT, znver3)
Threads: 1 default, 0 interactive, 1 GC (on 4 virtual cores)

Baseline

Julia Version 1.11.2
Commit 5e9a32e7af2 (2024-12-01 20:02 UTC)
Build Info:
  Official https://julialang.org/ release
Platform Info:
  OS: Linux (x86_64-linux-gnu)
      Ubuntu 22.04.5 LTS
  uname: Linux 6.5.0-1025-azure #26~22.04.1-Ubuntu SMP Thu Jul 11 22:33:04 UTC 2024 x86_64 x86_64
  CPU: AMD EPYC 7763 64-Core Processor: 
              speed         user         nice          sys         idle          irq
       #1     0 MHz      15804 s          0 s       1220 s      32233 s          0 s
       #2     0 MHz      15608 s          0 s       1254 s      32405 s          0 s
       #3     0 MHz      15156 s          0 s       1254 s      32835 s          0 s
       #4     0 MHz      13905 s          0 s       1212 s      34137 s          0 s
  Memory: 15.606491088867188 GB (12734.84765625 MB free)
  Uptime: 4935.9 sec
  Load Avg:  1.16  1.04  1.01
  WORD_SIZE: 64
  LLVM: libLLVM-16.0.6 (ORCJIT, znver3)
Threads: 1 default, 0 interactive, 1 GC (on 4 virtual cores)

Target result

Benchmark Report for /home/runner/work/Rt-without-renewal/Rt-without-renewal

Job Properties

Time of benchmark: 10 Dec 2024 - 13:34
Package commit: bacd87
Julia commit: 5e9a32
Julia command flags: None
Environment variables: None

Results

Below is a table of this job's results, obtained by running the benchmarks.
The values listed in the ID column have the structure [parent_group, child_group, ..., key], and can be used to
index into the BaseBenchmarks suite to retrieve the corresponding benchmarks.
The percentages accompanying time and memory values in the below table are noise tolerances. The "true"
time/memory value for a given benchmark is expected to fall within this percentage of the reported value.
An empty cell means that the value was zero.

ID	time	memory	allocations
`["EpiAwareUtils", "censored_pmf"]`	2.101 μs (5%)	416 bytes (1%)	10
`["EpiInfModels", "DirectInfections", "evaluation", "linked"]`	279.713 ns (5%)	464 bytes (1%)	10
`["EpiInfModels", "DirectInfections", "evaluation", "standard"]`	277.399 ns (5%)	464 bytes (1%)	10
`["EpiInfModels", "DirectInfections", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"]`	410.770 ns (5%)	816 bytes (1%)	18
`["EpiInfModels", "DirectInfections", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"]`	405.710 ns (5%)	816 bytes (1%)	18
`["EpiInfModels", "DirectInfections", "gradient", "ADTypes.AutoReverseDiff()", "linked"]`	10.108 μs (5%)	5.55 KiB (1%)	125
`["EpiInfModels", "DirectInfections", "gradient", "ADTypes.AutoReverseDiff()", "standard"]`	10.129 μs (5%)	5.55 KiB (1%)	125
`["EpiInfModels", "DirectInfections", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	483.779 ns (5%)	256 bytes (1%)	7
`["EpiInfModels", "DirectInfections", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	487.477 ns (5%)	256 bytes (1%)	7
`["EpiInfModels", "ExpGrowthRate", "evaluation", "linked"]`	178.163 ns (5%)	288 bytes (1%)	8
`["EpiInfModels", "ExpGrowthRate", "evaluation", "standard"]`	172.969 ns (5%)	288 bytes (1%)	8
`["EpiInfModels", "ExpGrowthRate", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"]`	269.720 ns (5%)	544 bytes (1%)	15
`["EpiInfModels", "ExpGrowthRate", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"]`	257.444 ns (5%)	544 bytes (1%)	15
`["EpiInfModels", "ExpGrowthRate", "gradient", "ADTypes.AutoReverseDiff()", "linked"]`	10.169 μs (5%)	5.53 KiB (1%)	124
`["EpiInfModels", "ExpGrowthRate", "gradient", "ADTypes.AutoReverseDiff()", "standard"]`	10.179 μs (5%)	5.53 KiB (1%)	124
`["EpiInfModels", "ExpGrowthRate", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	499.485 ns (5%)	256 bytes (1%)	7
`["EpiInfModels", "ExpGrowthRate", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	489.585 ns (5%)	256 bytes (1%)	7
`["EpiLatentModels", "AR", "evaluation", "linked"]`	13.215 μs (5%)	8.44 KiB (1%)	171
`["EpiLatentModels", "AR", "evaluation", "standard"]`	12.423 μs (5%)	5.77 KiB (1%)	158
`["EpiLatentModels", "AR", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"]`	14.427 μs (5%)	16.45 KiB (1%)	188
`["EpiLatentModels", "AR", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"]`	13.635 μs (5%)	13.20 KiB (1%)	171
`["EpiLatentModels", "AR", "gradient", "ADTypes.AutoReverseDiff()", "linked"]`	145.202 μs (5%)	59.31 KiB (1%)	1296
`["EpiLatentModels", "AR", "gradient", "ADTypes.AutoReverseDiff()", "standard"]`	98.835 μs (5%)	43.33 KiB (1%)	960
`["EpiLatentModels", "AR", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	19.195 μs (5%)	7.81 KiB (1%)	224
`["EpiLatentModels", "AR", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	17.603 μs (5%)	6.69 KiB (1%)	188
`["EpiLatentModels", "BroadcastLatentModel", "evaluation", "linked"]`	1.459 μs (5%)	4.36 KiB (1%)	48
`["EpiLatentModels", "BroadcastLatentModel", "evaluation", "standard"]`	994.900 ns (5%)	2.61 KiB (1%)	40
`["EpiLatentModels", "BroadcastLatentModel", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"]`	1.925 μs (5%)	6.42 KiB (1%)	59
`["EpiLatentModels", "BroadcastLatentModel", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"]`	1.345 μs (5%)	4.67 KiB (1%)	51
`["EpiLatentModels", "BroadcastLatentModel", "gradient", "ADTypes.AutoReverseDiff()", "linked"]`	50.584 μs (5%)	25.59 KiB (1%)	465
`["EpiLatentModels", "BroadcastLatentModel", "gradient", "ADTypes.AutoReverseDiff()", "standard"]`	31.608 μs (5%)	17.64 KiB (1%)	353
`["EpiLatentModels", "BroadcastLatentModel", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	2.927 μs (5%)	848 bytes (1%)	24
`["EpiLatentModels", "BroadcastLatentModel", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	2.706 μs (5%)	848 bytes (1%)	24
`["EpiLatentModels", "CombineLatentModels", "evaluation", "linked"]`	25.107 μs (5%)	50.69 KiB (1%)	433
`["EpiLatentModels", "CombineLatentModels", "evaluation", "standard"]`	21.731 μs (5%)	33.30 KiB (1%)	381
`["EpiLatentModels", "CombineLatentModels", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"]`	56.546 μs (5%)	115.75 KiB (1%)	905
`["EpiLatentModels", "CombineLatentModels", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"]`	48.150 μs (5%)	80.12 KiB (1%)	793
`["EpiLatentModels", "CombineLatentModels", "gradient", "ADTypes.AutoReverseDiff()", "linked"]`	168.294 μs (5%)	105.61 KiB (1%)	1626
`["EpiLatentModels", "CombineLatentModels", "gradient", "ADTypes.AutoReverseDiff()", "standard"]`	118.662 μs (5%)	74.91 KiB (1%)	1251
`["EpiLatentModels", "CombineLatentModels", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	19.626 μs (5%)	7.92 KiB (1%)	225
`["EpiLatentModels", "CombineLatentModels", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	18.344 μs (5%)	6.80 KiB (1%)	189
`["EpiLatentModels", "ConcatLatentModels", "evaluation", "linked"]`	52.779 μs (5%)	41.75 KiB (1%)	576
`["EpiLatentModels", "ConcatLatentModels", "evaluation", "standard"]`	49.582 μs (5%)	31.62 KiB (1%)	540
`["EpiLatentModels", "ConcatLatentModels", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"]`	54.031 μs (5%)	45.27 KiB (1%)	591
`["EpiLatentModels", "ConcatLatentModels", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"]`	50.915 μs (5%)	35.14 KiB (1%)	555
`["EpiLatentModels", "ConcatLatentModels", "gradient", "ADTypes.AutoReverseDiff()", "linked"]`	130.795 μs (5%)	67.14 KiB (1%)	1077
`["EpiLatentModels", "ConcatLatentModels", "gradient", "ADTypes.AutoReverseDiff()", "standard"]`	106.829 μs (5%)	52.27 KiB (1%)	938
`["EpiLatentModels", "ConcatLatentModels", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	5.305 μs (5%)	1.89 KiB (1%)	46
`["EpiLatentModels", "ConcatLatentModels", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	5.093 μs (5%)	1.89 KiB (1%)	46
`["EpiLatentModels", "DiffLatentModel", "evaluation", "linked"]`	16.301 μs (5%)	9.78 KiB (1%)	182
`["EpiLatentModels", "DiffLatentModel", "evaluation", "standard"]`	15.449 μs (5%)	6.41 KiB (1%)	170
`["EpiLatentModels", "DiffLatentModel", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"]`	17.873 μs (5%)	18.09 KiB (1%)	195
`["EpiLatentModels", "DiffLatentModel", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"]`	16.651 μs (5%)	14.72 KiB (1%)	183
`["EpiLatentModels", "DiffLatentModel", "gradient", "ADTypes.AutoReverseDiff()", "linked"]`	99.957 μs (5%)	43.50 KiB (1%)	877
`["EpiLatentModels", "DiffLatentModel", "gradient", "ADTypes.AutoReverseDiff()", "standard"]`	78.437 μs (5%)	35.38 KiB (1%)	762
`["EpiLatentModels", "DiffLatentModel", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	6.958 μs (5%)	1.94 KiB (1%)	45
`["EpiLatentModels", "DiffLatentModel", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	6.723 μs (5%)	1.94 KiB (1%)	45
`["EpiLatentModels", "HierarchicalNormal", "evaluation", "linked"]`	554.561 ns (5%)	1.50 KiB (1%)	20
`["EpiLatentModels", "HierarchicalNormal", "evaluation", "standard"]`	429.799 ns (5%)	1.19 KiB (1%)	18
`["EpiLatentModels", "HierarchicalNormal", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"]`	1.275 μs (5%)	5.59 KiB (1%)	29
`["EpiLatentModels", "HierarchicalNormal", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"]`	1.070 μs (5%)	5.28 KiB (1%)	27
`["EpiLatentModels", "HierarchicalNormal", "gradient", "ADTypes.AutoReverseDiff()", "linked"]`	36.709 μs (5%)	17.12 KiB (1%)	338
`["EpiLatentModels", "HierarchicalNormal", "gradient", "ADTypes.AutoReverseDiff()", "standard"]`	20.839 μs (5%)	12.06 KiB (1%)	233
`["EpiLatentModels", "HierarchicalNormal", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	954.435 ns (5%)	176 bytes (1%)	3
`["EpiLatentModels", "HierarchicalNormal", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	785.252 ns (5%)	176 bytes (1%)	3
`["EpiLatentModels", "IID", "evaluation", "linked"]`	201.134 ns (5%)	560 bytes (1%)	10
`["EpiLatentModels", "IID", "evaluation", "standard"]`	193.198 ns (5%)	560 bytes (1%)	10
`["EpiLatentModels", "IID", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"]`	529.085 ns (5%)	3.59 KiB (1%)	17
`["EpiLatentModels", "IID", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"]`	512.439 ns (5%)	3.59 KiB (1%)	17
`["EpiLatentModels", "IID", "gradient", "ADTypes.AutoReverseDiff()", "linked"]`	13.686 μs (5%)	7.58 KiB (1%)	153
`["EpiLatentModels", "IID", "gradient", "ADTypes.AutoReverseDiff()", "standard"]`	13.616 μs (5%)	7.58 KiB (1%)	153
`["EpiLatentModels", "IID", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	407.665 ns (5%)	176 bytes (1%)	3
`["EpiLatentModels", "IID", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	413.325 ns (5%)	176 bytes (1%)	3
`["EpiLatentModels", "Intercept", "evaluation", "linked"]`	211.307 ns (5%)	400 bytes (1%)	10
`["EpiLatentModels", "Intercept", "evaluation", "standard"]`	194.209 ns (5%)	400 bytes (1%)	10
`["EpiLatentModels", "Intercept", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"]`	289.419 ns (5%)	704 bytes (1%)	17
`["EpiLatentModels", "Intercept", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"]`	280.070 ns (5%)	704 bytes (1%)	17
`["EpiLatentModels", "Intercept", "gradient", "ADTypes.AutoReverseDiff()", "linked"]`	5.559 μs (5%)	3.62 KiB (1%)	87
`["EpiLatentModels", "Intercept", "gradient", "ADTypes.AutoReverseDiff()", "standard"]`	5.470 μs (5%)	3.62 KiB (1%)	87
`["EpiLatentModels", "Intercept", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	405.155 ns (5%)	208 bytes (1%)	4
`["EpiLatentModels", "Intercept", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	411.220 ns (5%)	208 bytes (1%)	4
`["EpiLatentModels", "MA", "evaluation", "linked"]`	1.657 μs (5%)	4.70 KiB (1%)	72
`["EpiLatentModels", "MA", "evaluation", "standard"]`	1.180 μs (5%)	3.25 KiB (1%)	62
`["EpiLatentModels", "MA", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"]`	3.073 μs (5%)	14.38 KiB (1%)	85
`["EpiLatentModels", "MA", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"]`	2.463 μs (5%)	12.48 KiB (1%)	73
`["EpiLatentModels", "MA", "gradient", "ADTypes.AutoReverseDiff()", "linked"]`	107.561 μs (5%)	49.22 KiB (1%)	1076
`["EpiLatentModels", "MA", "gradient", "ADTypes.AutoReverseDiff()", "standard"]`	65.031 μs (5%)	34.45 KiB (1%)	743
`["EpiLatentModels", "MA", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	18.404 μs (5%)	7.81 KiB (1%)	224
`["EpiLatentModels", "MA", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	17.092 μs (5%)	6.69 KiB (1%)	188
`["EpiLatentModels", "PrefixLatentModel", "evaluation", "linked"]`	2.062 μs (5%)	4.05 KiB (1%)	46
`["EpiLatentModels", "PrefixLatentModel", "evaluation", "standard"]`	1.818 μs (5%)	3.42 KiB (1%)	42
`["EpiLatentModels", "PrefixLatentModel", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"]`	2.931 μs (5%)	8.14 KiB (1%)	55
`["EpiLatentModels", "PrefixLatentModel", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"]`	2.615 μs (5%)	7.52 KiB (1%)	51
`["EpiLatentModels", "PrefixLatentModel", "gradient", "ADTypes.AutoReverseDiff()", "linked"]`	40.485 μs (5%)	19.53 KiB (1%)	363
`["EpiLatentModels", "PrefixLatentModel", "gradient", "ADTypes.AutoReverseDiff()", "standard"]`	23.434 μs (5%)	14.16 KiB (1%)	256
`["EpiLatentModels", "PrefixLatentModel", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	942.519 ns (5%)	176 bytes (1%)	3
`["EpiLatentModels", "PrefixLatentModel", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	785.125 ns (5%)	176 bytes (1%)	3
`["EpiLatentModels", "RandomWalk", "evaluation", "linked"]`	1.049 μs (5%)	3.33 KiB (1%)	35
`["EpiLatentModels", "RandomWalk", "evaluation", "standard"]`	716.080 ns (5%)	2.02 KiB (1%)	29
`["EpiLatentModels", "RandomWalk", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"]`	1.965 μs (5%)	9.62 KiB (1%)	46
`["EpiLatentModels", "RandomWalk", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"]`	1.589 μs (5%)	8.31 KiB (1%)	40
`["EpiLatentModels", "RandomWalk", "gradient", "ADTypes.AutoReverseDiff()", "linked"]`	52.077 μs (5%)	26.28 KiB (1%)	480
`["EpiLatentModels", "RandomWalk", "gradient", "ADTypes.AutoReverseDiff()", "standard"]`	34.304 μs (5%)	20.22 KiB (1%)	371
`["EpiLatentModels", "RandomWalk", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	3.272 μs (5%)	1.08 KiB (1%)	24
`["EpiLatentModels", "RandomWalk", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	3.108 μs (5%)	1.08 KiB (1%)	24
`["EpiLatentModels", "RecordExpectedLatent", "evaluation", "linked"]`	709.066 ns (5%)	1.72 KiB (1%)	26
`["EpiLatentModels", "RecordExpectedLatent", "evaluation", "standard"]`	527.458 ns (5%)	1.25 KiB (1%)	23
`["EpiLatentModels", "RecordExpectedLatent", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"]`	950.629 ns (5%)	2.25 KiB (1%)	35
`["EpiLatentModels", "RecordExpectedLatent", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"]`	753.649 ns (5%)	1.78 KiB (1%)	32
`["EpiLatentModels", "RecordExpectedLatent", "gradient", "ADTypes.AutoReverseDiff()", "linked"]`	36.207 μs (5%)	16.59 KiB (1%)	344
`["EpiLatentModels", "RecordExpectedLatent", "gradient", "ADTypes.AutoReverseDiff()", "standard"]`	20.910 μs (5%)	11.38 KiB (1%)	238
`["EpiLatentModels", "RecordExpectedLatent", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	881.093 ns (5%)	112 bytes (1%)	3
`["EpiLatentModels", "RecordExpectedLatent", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	717.147 ns (5%)	112 bytes (1%)	3
`["EpiLatentModels", "TransformLatentModel", "evaluation", "linked"]`	253.455 ns (5%)	448 bytes (1%)	12
`["EpiLatentModels", "TransformLatentModel", "evaluation", "standard"]`	239.518 ns (5%)	448 bytes (1%)	12
`["EpiLatentModels", "TransformLatentModel", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"]`	331.023 ns (5%)	768 bytes (1%)	19
`["EpiLatentModels", "TransformLatentModel", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"]`	318.638 ns (5%)	768 bytes (1%)	19
`["EpiLatentModels", "TransformLatentModel", "gradient", "ADTypes.AutoReverseDiff()", "linked"]`	5.817 μs (5%)	3.93 KiB (1%)	95
`["EpiLatentModels", "TransformLatentModel", "gradient", "ADTypes.AutoReverseDiff()", "standard"]`	5.817 μs (5%)	3.93 KiB (1%)	95
`["EpiLatentModels", "TransformLatentModel", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	471.852 ns (5%)	160 bytes (1%)	4
`["EpiLatentModels", "TransformLatentModel", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	469.092 ns (5%)	160 bytes (1%)	4
`["EpiLatentModels", "broadcast_dayofweek", "evaluation", "linked"]`	1.767 μs (5%)	5.36 KiB (1%)	57
`["EpiLatentModels", "broadcast_dayofweek", "evaluation", "standard"]`	1.232 μs (5%)	3.17 KiB (1%)	47
`["EpiLatentModels", "broadcast_dayofweek", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"]`	2.676 μs (5%)	11.38 KiB (1%)	68
`["EpiLatentModels", "broadcast_dayofweek", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"]`	2.052 μs (5%)	9.19 KiB (1%)	58
`["EpiLatentModels", "broadcast_dayofweek", "gradient", "ADTypes.AutoReverseDiff()", "linked"]`	70.632 μs (5%)	35.97 KiB (1%)	690
`["EpiLatentModels", "broadcast_dayofweek", "gradient", "ADTypes.AutoReverseDiff()", "standard"]`	50.775 μs (5%)	29.03 KiB (1%)	577
`["EpiLatentModels", "broadcast_dayofweek", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	4.783 μs (5%)	1.00 KiB (1%)	24
`["EpiLatentModels", "broadcast_dayofweek", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	4.623 μs (5%)	1.00 KiB (1%)	24
`["EpiLatentModels", "broadcast_weekly", "evaluation", "linked"]`	17.022 μs (5%)	10.03 KiB (1%)	193
`["EpiLatentModels", "broadcast_weekly", "evaluation", "standard"]`	16.060 μs (5%)	6.52 KiB (1%)	177
`["EpiLatentModels", "broadcast_weekly", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"]`	17.492 μs (5%)	13.23 KiB (1%)	210
`["EpiLatentModels", "broadcast_weekly", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"]`	16.260 μs (5%)	9.39 KiB (1%)	190
`["EpiLatentModels", "broadcast_weekly", "gradient", "ADTypes.AutoReverseDiff()", "linked"]`	122.188 μs (5%)	47.12 KiB (1%)	934
`["EpiLatentModels", "broadcast_weekly", "gradient", "ADTypes.AutoReverseDiff()", "standard"]`	77.636 μs (5%)	32.80 KiB (1%)	675
`["EpiLatentModels", "broadcast_weekly", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	5.747 μs (5%)	1.69 KiB (1%)	48
`["EpiLatentModels", "broadcast_weekly", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	5.111 μs (5%)	1.56 KiB (1%)	44
`["EpiObsModels", "Ascertainment", "evaluation", "linked"]`	3.299 μs (5%)	3.58 KiB (1%)	63
`["EpiObsModels", "Ascertainment", "evaluation", "standard"]`	3.230 μs (5%)	3.58 KiB (1%)	63
`["EpiObsModels", "Ascertainment", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"]`	4.261 μs (5%)	3.92 KiB (1%)	72
`["EpiObsModels", "Ascertainment", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"]`	4.132 μs (5%)	3.92 KiB (1%)	72
`["EpiObsModels", "Ascertainment", "gradient", "ADTypes.AutoReverseDiff()", "linked"]`	76.984 μs (5%)	38.77 KiB (1%)	918
`["EpiObsModels", "Ascertainment", "gradient", "ADTypes.AutoReverseDiff()", "standard"]`	61.154 μs (5%)	34.02 KiB (1%)	815
`["EpiObsModels", "Ascertainment", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	5.704 μs (5%)	96 bytes (1%)	3
`["EpiObsModels", "Ascertainment", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	5.527 μs (5%)	96 bytes (1%)	3
`["EpiObsModels", "LatentDelay", "evaluation", "linked"]`	14.127 μs (5%)	22.08 KiB (1%)	412
`["EpiObsModels", "LatentDelay", "evaluation", "standard"]`	14.156 μs (5%)	22.08 KiB (1%)	412
`["EpiObsModels", "LatentDelay", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"]`	17.863 μs (5%)	22.30 KiB (1%)	419
`["EpiObsModels", "LatentDelay", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"]`	18.344 μs (5%)	22.30 KiB (1%)	419
`["EpiObsModels", "LatentDelay", "gradient", "ADTypes.AutoReverseDiff()", "linked"]`	535.902 μs (5%)	293.05 KiB (1%)	7011
`["EpiObsModels", "LatentDelay", "gradient", "ADTypes.AutoReverseDiff()", "standard"]`	517.999 μs (5%)	288.30 KiB (1%)	6908
`["EpiObsModels", "LatentDelay", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	50.615 μs (5%)	96 bytes (1%)	3
`["EpiObsModels", "LatentDelay", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	50.995 μs (5%)	96 bytes (1%)	3
`["EpiObsModels", "NegativeBinomialError", "evaluation", "linked"]`	1.114 μs (5%)	400 bytes (1%)	10
`["EpiObsModels", "NegativeBinomialError", "evaluation", "standard"]`	1.071 μs (5%)	400 bytes (1%)	10
`["EpiObsModels", "NegativeBinomialError", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"]`	1.606 μs (5%)	624 bytes (1%)	17
`["EpiObsModels", "NegativeBinomialError", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"]`	1.533 μs (5%)	624 bytes (1%)	17
`["EpiObsModels", "NegativeBinomialError", "gradient", "ADTypes.AutoReverseDiff()", "linked"]`	72.445 μs (5%)	35.95 KiB (1%)	903
`["EpiObsModels", "NegativeBinomialError", "gradient", "ADTypes.AutoReverseDiff()", "standard"]`	58.420 μs (5%)	31.20 KiB (1%)	800
`["EpiObsModels", "NegativeBinomialError", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	5.716 μs (5%)	96 bytes (1%)	3
`["EpiObsModels", "NegativeBinomialError", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	5.490 μs (5%)	96 bytes (1%)	3
`["EpiObsModels", "PoissonError", "evaluation", "linked"]`	1.355 μs (5%)	1.83 KiB (1%)	30
`["EpiObsModels", "PoissonError", "evaluation", "standard"]`	1.037 μs (5%)	1.47 KiB (1%)	26
`["EpiObsModels", "PoissonError", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"]`	3.110 μs (5%)	7.78 KiB (1%)	43
`["EpiObsModels", "PoissonError", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"]`	2.451 μs (5%)	4.70 KiB (1%)	35
`["EpiObsModels", "PoissonError", "gradient", "ADTypes.AutoReverseDiff()", "linked"]`	167.503 μs (5%)	86.44 KiB (1%)	1870
`["EpiObsModels", "PoissonError", "gradient", "ADTypes.AutoReverseDiff()", "standard"]`	48.851 μs (5%)	29.38 KiB (1%)	727
`["EpiObsModels", "PoissonError", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	6.193 μs (5%)	176 bytes (1%)	3
`["EpiObsModels", "PoissonError", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	4.102 μs (5%)	176 bytes (1%)	3
`["EpiObsModels", "PrefixObservationModel", "evaluation", "linked"]`	1.671 μs (5%)	1.56 KiB (1%)	34
`["EpiObsModels", "PrefixObservationModel", "evaluation", "standard"]`	1.607 μs (5%)	1.56 KiB (1%)	34
`["EpiObsModels", "PrefixObservationModel", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"]`	1.807 μs (5%)	1.78 KiB (1%)	41
`["EpiObsModels", "PrefixObservationModel", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"]`	1.729 μs (5%)	1.78 KiB (1%)	41
`["EpiObsModels", "PrefixObservationModel", "gradient", "ADTypes.AutoReverseDiff()", "linked"]`	26.600 μs (5%)	12.59 KiB (1%)	290
`["EpiObsModels", "PrefixObservationModel", "gradient", "ADTypes.AutoReverseDiff()", "standard"]`	13.385 μs (5%)	7.84 KiB (1%)	187
`["EpiObsModels", "PrefixObservationModel", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	1.105 μs (5%)	96 bytes (1%)	3
`["EpiObsModels", "PrefixObservationModel", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	970.357 ns (5%)	96 bytes (1%)	3
`["EpiObsModels", "RecordExpectedObs", "evaluation", "linked"]`	756.585 ns (5%)	480 bytes (1%)	14
`["EpiObsModels", "RecordExpectedObs", "evaluation", "standard"]`	722.925 ns (5%)	480 bytes (1%)	14
`["EpiObsModels", "RecordExpectedObs", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"]`	1.030 μs (5%)	704 bytes (1%)	21
`["EpiObsModels", "RecordExpectedObs", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"]`	1.008 μs (5%)	704 bytes (1%)	21
`["EpiObsModels", "RecordExpectedObs", "gradient", "ADTypes.AutoReverseDiff()", "linked"]`	46.086 μs (5%)	22.98 KiB (1%)	537
`["EpiObsModels", "RecordExpectedObs", "gradient", "ADTypes.AutoReverseDiff()", "standard"]`	32.561 μs (5%)	18.23 KiB (1%)	434
`["EpiObsModels", "RecordExpectedObs", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	3.142 μs (5%)	96 bytes (1%)	3
`["EpiObsModels", "RecordExpectedObs", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	2.994 μs (5%)	96 bytes (1%)	3
`["EpiObsModels", "StackObservationModels", "evaluation", "linked"]`	6.827 μs (5%)	5.81 KiB (1%)	117
`["EpiObsModels", "StackObservationModels", "evaluation", "standard"]`	6.686 μs (5%)	5.81 KiB (1%)	117
`["EpiObsModels", "StackObservationModels", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"]`	7.664 μs (5%)	6.16 KiB (1%)	126
`["EpiObsModels", "StackObservationModels", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"]`	7.554 μs (5%)	6.16 KiB (1%)	126
`["EpiObsModels", "StackObservationModels", "gradient", "ADTypes.AutoReverseDiff()", "linked"]`	89.427 μs (5%)	49.09 KiB (1%)	1044
`["EpiObsModels", "StackObservationModels", "gradient", "ADTypes.AutoReverseDiff()", "standard"]`	71.504 μs (5%)	44.34 KiB (1%)	941
`["EpiObsModels", "StackObservationModels", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	6.063 μs (5%)	96 bytes (1%)	3
`["EpiObsModels", "StackObservationModels", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	6.017 μs (5%)	96 bytes (1%)	3
`["EpiObsModels", "TransformObservationModel", "evaluation", "linked"]`	1.407 μs (5%)	672 bytes (1%)	16
`["EpiObsModels", "TransformObservationModel", "evaluation", "standard"]`	1.361 μs (5%)	672 bytes (1%)	16
`["EpiObsModels", "TransformObservationModel", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"]`	1.941 μs (5%)	896 bytes (1%)	23
`["EpiObsModels", "TransformObservationModel", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"]`	1.799 μs (5%)	896 bytes (1%)	23
`["EpiObsModels", "TransformObservationModel", "gradient", "ADTypes.AutoReverseDiff()", "linked"]`	73.207 μs (5%)	35.59 KiB (1%)	869
`["EpiObsModels", "TransformObservationModel", "gradient", "ADTypes.AutoReverseDiff()", "standard"]`	59.061 μs (5%)	30.84 KiB (1%)	766
`["EpiObsModels", "TransformObservationModel", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	5.776 μs (5%)	96 bytes (1%)	3
`["EpiObsModels", "TransformObservationModel", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	5.554 μs (5%)	96 bytes (1%)	3
`["EpiObsModels", "ascertainment_dayofweek", "evaluation", "linked"]`	4.455 μs (5%)	9.59 KiB (1%)	106
`["EpiObsModels", "ascertainment_dayofweek", "evaluation", "standard"]`	3.899 μs (5%)	8.19 KiB (1%)	97
`["EpiObsModels", "ascertainment_dayofweek", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"]`	6.346 μs (5%)	16.72 KiB (1%)	117
`["EpiObsModels", "ascertainment_dayofweek", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"]`	5.747 μs (5%)	15.31 KiB (1%)	108
`["EpiObsModels", "ascertainment_dayofweek", "gradient", "ADTypes.AutoReverseDiff()", "linked"]`	99.967 μs (5%)	58.03 KiB (1%)	1121
`["EpiObsModels", "ascertainment_dayofweek", "gradient", "ADTypes.AutoReverseDiff()", "standard"]`	80.982 μs (5%)	51.88 KiB (1%)	1009
`["EpiObsModels", "ascertainment_dayofweek", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	5.237 μs (5%)	160 bytes (1%)	3
`["EpiObsModels", "ascertainment_dayofweek", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	5.083 μs (5%)	160 bytes (1%)	3
`["EpiObsModels", "observation_error", "missing obs", "evaluation", "linked"]`	1.407 μs (5%)	3.05 KiB (1%)	38
`["EpiObsModels", "observation_error", "missing obs", "evaluation", "standard"]`	889.667 ns (5%)	1.48 KiB (1%)	28
`["EpiObsModels", "observation_error", "missing obs", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"]`	1.813 μs (5%)	4.11 KiB (1%)	48
`["EpiObsModels", "observation_error", "missing obs", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"]`	1.246 μs (5%)	2.55 KiB (1%)	38
`["EpiObsModels", "observation_error", "missing obs", "gradient", "ADTypes.AutoReverseDiff()", "linked"]`	41.006 μs (5%)	25.78 KiB (1%)	533
`["EpiObsModels", "observation_error", "missing obs", "gradient", "ADTypes.AutoReverseDiff()", "standard"]`	24.165 μs (5%)	18.80 KiB (1%)	401
`["EpiObsModels", "observation_error", "missing obs", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	2.706 μs (5%)	144 bytes (1%)	3
`["EpiObsModels", "observation_error", "missing obs", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	2.383 μs (5%)	144 bytes (1%)	3
`["EpiObsModels", "observation_error", "no missing obs", "evaluation", "linked"]`	409.265 ns (5%)	352 bytes (1%)	10
`["EpiObsModels", "observation_error", "no missing obs", "evaluation", "standard"]`	350.981 ns (5%)	352 bytes (1%)	10
`["EpiObsModels", "observation_error", "no missing obs", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"]`	556.826 ns (5%)	576 bytes (1%)	17
`["EpiObsModels", "observation_error", "no missing obs", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"]`	490.051 ns (5%)	576 bytes (1%)	17
`["EpiObsModels", "observation_error", "no missing obs", "gradient", "ADTypes.AutoReverseDiff()", "linked"]`	33.472 μs (5%)	19.64 KiB (1%)	446
`["EpiObsModels", "observation_error", "no missing obs", "gradient", "ADTypes.AutoReverseDiff()", "standard"]`	18.876 μs (5%)	14.22 KiB (1%)	324
`["EpiObsModels", "observation_error", "no missing obs", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	2.309 μs (5%)	96 bytes (1%)	3
`["EpiObsModels", "observation_error", "no missing obs", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	1.996 μs (5%)	96 bytes (1%)	3
`["EpiObsModels", "observation_error", "partially missing obs", "evaluation", "linked"]`	31.900 μs (5%)	3.02 KiB (1%)	64
`["EpiObsModels", "observation_error", "partially missing obs", "evaluation", "standard"]`	31.739 μs (5%)	2.70 KiB (1%)	62
`["EpiObsModels", "observation_error", "partially missing obs", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"]`	21.240 μs (5%)	2.73 KiB (1%)	51
`["EpiObsModels", "observation_error", "partially missing obs", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"]`	20.949 μs (5%)	2.42 KiB (1%)	49
`["EpiObsModels", "observation_error", "partially missing obs", "gradient", "ADTypes.AutoReverseDiff()", "linked"]`	65.763 μs (5%)	26.11 KiB (1%)	555
`["EpiObsModels", "observation_error", "partially missing obs", "gradient", "ADTypes.AutoReverseDiff()", "standard"]`	45.435 μs (5%)	20.38 KiB (1%)	431
`["EpiObsModels", "observation_error", "partially missing obs", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	2.884 μs (5%)	112 bytes (1%)	3
`["EpiObsModels", "observation_error", "partially missing obs", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	2.409 μs (5%)	112 bytes (1%)	3

Benchmark Group List

Here's a list of all the benchmark groups executed by this job:

["EpiAwareUtils"]
["EpiInfModels", "DirectInfections", "evaluation"]
["EpiInfModels", "DirectInfections", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)"]
["EpiInfModels", "DirectInfections", "gradient", "ADTypes.AutoReverseDiff()"]
["EpiInfModels", "DirectInfections", "gradient", "ADTypes.AutoReverseDiff(compile=true)"]
["EpiInfModels", "ExpGrowthRate", "evaluation"]
["EpiInfModels", "ExpGrowthRate", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)"]
["EpiInfModels", "ExpGrowthRate", "gradient", "ADTypes.AutoReverseDiff()"]
["EpiInfModels", "ExpGrowthRate", "gradient", "ADTypes.AutoReverseDiff(compile=true)"]
["EpiLatentModels", "AR", "evaluation"]
["EpiLatentModels", "AR", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)"]
["EpiLatentModels", "AR", "gradient", "ADTypes.AutoReverseDiff()"]
["EpiLatentModels", "AR", "gradient", "ADTypes.AutoReverseDiff(compile=true)"]
["EpiLatentModels", "BroadcastLatentModel", "evaluation"]
["EpiLatentModels", "BroadcastLatentModel", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)"]
["EpiLatentModels", "BroadcastLatentModel", "gradient", "ADTypes.AutoReverseDiff()"]
["EpiLatentModels", "BroadcastLatentModel", "gradient", "ADTypes.AutoReverseDiff(compile=true)"]
["EpiLatentModels", "CombineLatentModels", "evaluation"]
["EpiLatentModels", "CombineLatentModels", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)"]
["EpiLatentModels", "CombineLatentModels", "gradient", "ADTypes.AutoReverseDiff()"]
["EpiLatentModels", "CombineLatentModels", "gradient", "ADTypes.AutoReverseDiff(compile=true)"]
["EpiLatentModels", "ConcatLatentModels", "evaluation"]
["EpiLatentModels", "ConcatLatentModels", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)"]
["EpiLatentModels", "ConcatLatentModels", "gradient", "ADTypes.AutoReverseDiff()"]
["EpiLatentModels", "ConcatLatentModels", "gradient", "ADTypes.AutoReverseDiff(compile=true)"]
["EpiLatentModels", "DiffLatentModel", "evaluation"]
["EpiLatentModels", "DiffLatentModel", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)"]
["EpiLatentModels", "DiffLatentModel", "gradient", "ADTypes.AutoReverseDiff()"]
["EpiLatentModels", "DiffLatentModel", "gradient", "ADTypes.AutoReverseDiff(compile=true)"]
["EpiLatentModels", "HierarchicalNormal", "evaluation"]
["EpiLatentModels", "HierarchicalNormal", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)"]
["EpiLatentModels", "HierarchicalNormal", "gradient", "ADTypes.AutoReverseDiff()"]
["EpiLatentModels", "HierarchicalNormal", "gradient", "ADTypes.AutoReverseDiff(compile=true)"]
["EpiLatentModels", "IID", "evaluation"]
["EpiLatentModels", "IID", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)"]
["EpiLatentModels", "IID", "gradient", "ADTypes.AutoReverseDiff()"]
["EpiLatentModels", "IID", "gradient", "ADTypes.AutoReverseDiff(compile=true)"]
["EpiLatentModels", "Intercept", "evaluation"]
["EpiLatentModels", "Intercept", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)"]
["EpiLatentModels", "Intercept", "gradient", "ADTypes.AutoReverseDiff()"]
["EpiLatentModels", "Intercept", "gradient", "ADTypes.AutoReverseDiff(compile=true)"]
["EpiLatentModels", "MA", "evaluation"]
["EpiLatentModels", "MA", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)"]
["EpiLatentModels", "MA", "gradient", "ADTypes.AutoReverseDiff()"]
["EpiLatentModels", "MA", "gradient", "ADTypes.AutoReverseDiff(compile=true)"]
["EpiLatentModels", "PrefixLatentModel", "evaluation"]
["EpiLatentModels", "PrefixLatentModel", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)"]
["EpiLatentModels", "PrefixLatentModel", "gradient", "ADTypes.AutoReverseDiff()"]
["EpiLatentModels", "PrefixLatentModel", "gradient", "ADTypes.AutoReverseDiff(compile=true)"]
["EpiLatentModels", "RandomWalk", "evaluation"]
["EpiLatentModels", "RandomWalk", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)"]
["EpiLatentModels", "RandomWalk", "gradient", "ADTypes.AutoReverseDiff()"]
["EpiLatentModels", "RandomWalk", "gradient", "ADTypes.AutoReverseDiff(compile=true)"]
["EpiLatentModels", "RecordExpectedLatent", "evaluation"]
["EpiLatentModels", "RecordExpectedLatent", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)"]
["EpiLatentModels", "RecordExpectedLatent", "gradient", "ADTypes.AutoReverseDiff()"]
["EpiLatentModels", "RecordExpectedLatent", "gradient", "ADTypes.AutoReverseDiff(compile=true)"]
["EpiLatentModels", "TransformLatentModel", "evaluation"]
["EpiLatentModels", "TransformLatentModel", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)"]
["EpiLatentModels", "TransformLatentModel", "gradient", "ADTypes.AutoReverseDiff()"]
["EpiLatentModels", "TransformLatentModel", "gradient", "ADTypes.AutoReverseDiff(compile=true)"]
["EpiLatentModels", "broadcast_dayofweek", "evaluation"]
["EpiLatentModels", "broadcast_dayofweek", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)"]
["EpiLatentModels", "broadcast_dayofweek", "gradient", "ADTypes.AutoReverseDiff()"]
["EpiLatentModels", "broadcast_dayofweek", "gradient", "ADTypes.AutoReverseDiff(compile=true)"]
["EpiLatentModels", "broadcast_weekly", "evaluation"]
["EpiLatentModels", "broadcast_weekly", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)"]
["EpiLatentModels", "broadcast_weekly", "gradient", "ADTypes.AutoReverseDiff()"]
["EpiLatentModels", "broadcast_weekly", "gradient", "ADTypes.AutoReverseDiff(compile=true)"]
["EpiObsModels", "Ascertainment", "evaluation"]
["EpiObsModels", "Ascertainment", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)"]
["EpiObsModels", "Ascertainment", "gradient", "ADTypes.AutoReverseDiff()"]
["EpiObsModels", "Ascertainment", "gradient", "ADTypes.AutoReverseDiff(compile=true)"]
["EpiObsModels", "LatentDelay", "evaluation"]
["EpiObsModels", "LatentDelay", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)"]
["EpiObsModels", "LatentDelay", "gradient", "ADTypes.AutoReverseDiff()"]
["EpiObsModels", "LatentDelay", "gradient", "ADTypes.AutoReverseDiff(compile=true)"]
["EpiObsModels", "NegativeBinomialError", "evaluation"]
["EpiObsModels", "NegativeBinomialError", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)"]
["EpiObsModels", "NegativeBinomialError", "gradient", "ADTypes.AutoReverseDiff()"]
["EpiObsModels", "NegativeBinomialError", "gradient", "ADTypes.AutoReverseDiff(compile=true)"]
["EpiObsModels", "PoissonError", "evaluation"]
["EpiObsModels", "PoissonError", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)"]
["EpiObsModels", "PoissonError", "gradient", "ADTypes.AutoReverseDiff()"]
["EpiObsModels", "PoissonError", "gradient", "ADTypes.AutoReverseDiff(compile=true)"]
["EpiObsModels", "PrefixObservationModel", "evaluation"]
["EpiObsModels", "PrefixObservationModel", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)"]
["EpiObsModels", "PrefixObservationModel", "gradient", "ADTypes.AutoReverseDiff()"]
["EpiObsModels", "PrefixObservationModel", "gradient", "ADTypes.AutoReverseDiff(compile=true)"]
["EpiObsModels", "RecordExpectedObs", "evaluation"]
["EpiObsModels", "RecordExpectedObs", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)"]
["EpiObsModels", "RecordExpectedObs", "gradient", "ADTypes.AutoReverseDiff()"]
["EpiObsModels", "RecordExpectedObs", "gradient", "ADTypes.AutoReverseDiff(compile=true)"]
["EpiObsModels", "StackObservationModels", "evaluation"]
["EpiObsModels", "StackObservationModels", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)"]
["EpiObsModels", "StackObservationModels", "gradient", "ADTypes.AutoReverseDiff()"]
["EpiObsModels", "StackObservationModels", "gradient", "ADTypes.AutoReverseDiff(compile=true)"]
["EpiObsModels", "TransformObservationModel", "evaluation"]
["EpiObsModels", "TransformObservationModel", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)"]
["EpiObsModels", "TransformObservationModel", "gradient", "ADTypes.AutoReverseDiff()"]
["EpiObsModels", "TransformObservationModel", "gradient", "ADTypes.AutoReverseDiff(compile=true)"]
["EpiObsModels", "ascertainment_dayofweek", "evaluation"]
["EpiObsModels", "ascertainment_dayofweek", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)"]
["EpiObsModels", "ascertainment_dayofweek", "gradient", "ADTypes.AutoReverseDiff()"]
["EpiObsModels", "ascertainment_dayofweek", "gradient", "ADTypes.AutoReverseDiff(compile=true)"]
["EpiObsModels", "observation_error", "missing obs", "evaluation"]
["EpiObsModels", "observation_error", "missing obs", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)"]
["EpiObsModels", "observation_error", "missing obs", "gradient", "ADTypes.AutoReverseDiff()"]
["EpiObsModels", "observation_error", "missing obs", "gradient", "ADTypes.AutoReverseDiff(compile=true)"]
["EpiObsModels", "observation_error", "no missing obs", "evaluation"]
["EpiObsModels", "observation_error", "no missing obs", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)"]
["EpiObsModels", "observation_error", "no missing obs", "gradient", "ADTypes.AutoReverseDiff()"]
["EpiObsModels", "observation_error", "no missing obs", "gradient", "ADTypes.AutoReverseDiff(compile=true)"]
["EpiObsModels", "observation_error", "partially missing obs", "evaluation"]
["EpiObsModels", "observation_error", "partially missing obs", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)"]
["EpiObsModels", "observation_error", "partially missing obs", "gradient", "ADTypes.AutoReverseDiff()"]
["EpiObsModels", "observation_error", "partially missing obs", "gradient", "ADTypes.AutoReverseDiff(compile=true)"]

Julia versioninfo

Julia Version 1.11.2
Commit 5e9a32e7af2 (2024-12-01 20:02 UTC)
Build Info:
  Official https://julialang.org/ release
Platform Info:
  OS: Linux (x86_64-linux-gnu)
      Ubuntu 22.04.5 LTS
  uname: Linux 6.5.0-1025-azure #26~22.04.1-Ubuntu SMP Thu Jul 11 22:33:04 UTC 2024 x86_64 x86_64
  CPU: AMD EPYC 7763 64-Core Processor: 
              speed         user         nice          sys         idle          irq
       #1     0 MHz      10728 s          0 s        821 s      17369 s          0 s
       #2     0 MHz      10339 s          0 s        801 s      17785 s          0 s
       #3     0 MHz      10015 s          0 s        816 s      18071 s          0 s
       #4     0 MHz       9858 s          0 s        757 s      18299 s          0 s
  Memory: 15.606491088867188 GB (12870.91796875 MB free)
  Uptime: 2898.04 sec
  Load Avg:  1.09  1.06  1.14
  WORD_SIZE: 64
  LLVM: libLLVM-16.0.6 (ORCJIT, znver3)
Threads: 1 default, 0 interactive, 1 GC (on 4 virtual cores)

Baseline result

Benchmark Report for /home/runner/work/Rt-without-renewal/Rt-without-renewal

Job Properties

Time of benchmark: 10 Dec 2024 - 14:8
Package commit: 8dc444
Julia commit: 5e9a32
Julia command flags: None
Environment variables: None

Results

Below is a table of this job's results, obtained by running the benchmarks.
The values listed in the ID column have the structure [parent_group, child_group, ..., key], and can be used to
index into the BaseBenchmarks suite to retrieve the corresponding benchmarks.
The percentages accompanying time and memory values in the below table are noise tolerances. The "true"
time/memory value for a given benchmark is expected to fall within this percentage of the reported value.
An empty cell means that the value was zero.

ID	time	memory	allocations
`["EpiAwareUtils", "censored_pmf"]`	2.108 μs (5%)	416 bytes (1%)	10
`["EpiInfModels", "DirectInfections", "evaluation", "linked"]`	275.370 ns (5%)	464 bytes (1%)	10
`["EpiInfModels", "DirectInfections", "evaluation", "standard"]`	272.462 ns (5%)	464 bytes (1%)	10
`["EpiInfModels", "DirectInfections", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"]`	400.050 ns (5%)	816 bytes (1%)	18
`["EpiInfModels", "DirectInfections", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"]`	398.145 ns (5%)	816 bytes (1%)	18
`["EpiInfModels", "DirectInfections", "gradient", "ADTypes.AutoReverseDiff()", "linked"]`	10.270 μs (5%)	5.55 KiB (1%)	125
`["EpiInfModels", "DirectInfections", "gradient", "ADTypes.AutoReverseDiff()", "standard"]`	10.230 μs (5%)	5.55 KiB (1%)	125
`["EpiInfModels", "DirectInfections", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	482.851 ns (5%)	256 bytes (1%)	7
`["EpiInfModels", "DirectInfections", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	490.557 ns (5%)	256 bytes (1%)	7
`["EpiInfModels", "ExpGrowthRate", "evaluation", "linked"]`	183.952 ns (5%)	288 bytes (1%)	8
`["EpiInfModels", "ExpGrowthRate", "evaluation", "standard"]`	173.293 ns (5%)	288 bytes (1%)	8
`["EpiInfModels", "ExpGrowthRate", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"]`	266.244 ns (5%)	544 bytes (1%)	15
`["EpiInfModels", "ExpGrowthRate", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"]`	259.448 ns (5%)	544 bytes (1%)	15
`["EpiInfModels", "ExpGrowthRate", "gradient", "ADTypes.AutoReverseDiff()", "linked"]`	10.148 μs (5%)	5.53 KiB (1%)	124
`["EpiInfModels", "ExpGrowthRate", "gradient", "ADTypes.AutoReverseDiff()", "standard"]`	10.099 μs (5%)	5.53 KiB (1%)	124
`["EpiInfModels", "ExpGrowthRate", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	488.554 ns (5%)	256 bytes (1%)	7
`["EpiInfModels", "ExpGrowthRate", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	493.500 ns (5%)	256 bytes (1%)	7
`["EpiLatentModels", "AR", "evaluation", "linked"]`	4.911 μs (5%)	4.19 KiB (1%)	91
`["EpiLatentModels", "AR", "evaluation", "standard"]`	4.528 μs (5%)	3.20 KiB (1%)	84
`["EpiLatentModels", "AR", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"]`	6.348 μs (5%)	12.20 KiB (1%)	108
`["EpiLatentModels", "AR", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"]`	5.836 μs (5%)	10.64 KiB (1%)	97
`["EpiLatentModels", "AR", "gradient", "ADTypes.AutoReverseDiff()", "linked"]`	127.347 μs (5%)	54.25 KiB (1%)	1214
`["EpiLatentModels", "AR", "gradient", "ADTypes.AutoReverseDiff()", "standard"]`	84.277 μs (5%)	39.95 KiB (1%)	884
`["EpiLatentModels", "AR", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	18.836 μs (5%)	7.81 KiB (1%)	224
`["EpiLatentModels", "AR", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	17.382 μs (5%)	6.69 KiB (1%)	188
`["EpiLatentModels", "BroadcastLatentModel", "evaluation", "linked"]`	934.909 ns (5%)	2.75 KiB (1%)	34
`["EpiLatentModels", "BroadcastLatentModel", "evaluation", "standard"]`	678.734 ns (5%)	1.88 KiB (1%)	30
`["EpiLatentModels", "BroadcastLatentModel", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"]`	1.319 μs (5%)	4.83 KiB (1%)	45
`["EpiLatentModels", "BroadcastLatentModel", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"]`	1.048 μs (5%)	3.95 KiB (1%)	41
`["EpiLatentModels", "BroadcastLatentModel", "gradient", "ADTypes.AutoReverseDiff()", "linked"]`	48.581 μs (5%)	23.58 KiB (1%)	458
`["EpiLatentModels", "BroadcastLatentModel", "gradient", "ADTypes.AutoReverseDiff()", "standard"]`	30.497 μs (5%)	16.50 KiB (1%)	350
`["EpiLatentModels", "BroadcastLatentModel", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	3.073 μs (5%)	992 bytes (1%)	29
`["EpiLatentModels", "BroadcastLatentModel", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	2.855 μs (5%)	992 bytes (1%)	29
`["EpiLatentModels", "CombineLatentModels", "evaluation", "linked"]`	22.041 μs (5%)	45.67 KiB (1%)	383
`["EpiLatentModels", "CombineLatentModels", "evaluation", "standard"]`	18.655 μs (5%)	31.16 KiB (1%)	339
`["EpiLatentModels", "CombineLatentModels", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"]`	49.493 μs (5%)	105.72 KiB (1%)	805
`["EpiLatentModels", "CombineLatentModels", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"]`	42.369 μs (5%)	75.84 KiB (1%)	709
`["EpiLatentModels", "CombineLatentModels", "gradient", "ADTypes.AutoReverseDiff()", "linked"]`	158.767 μs (5%)	99.81 KiB (1%)	1574
`["EpiLatentModels", "CombineLatentModels", "gradient", "ADTypes.AutoReverseDiff()", "standard"]`	112.581 μs (5%)	71.98 KiB (1%)	1207
`["EpiLatentModels", "CombineLatentModels", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	19.286 μs (5%)	7.92 KiB (1%)	225
`["EpiLatentModels", "CombineLatentModels", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	18.204 μs (5%)	6.80 KiB (1%)	189
`["EpiLatentModels", "ConcatLatentModels", "evaluation", "linked"]`	43.291 μs (5%)	37.27 KiB (1%)	496
`["EpiLatentModels", "ConcatLatentModels", "evaluation", "standard"]`	40.716 μs (5%)	28.83 KiB (1%)	466
`["EpiLatentModels", "ConcatLatentModels", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"]`	44.453 μs (5%)	40.86 KiB (1%)	511
`["EpiLatentModels", "ConcatLatentModels", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"]`	41.868 μs (5%)	32.42 KiB (1%)	481
`["EpiLatentModels", "ConcatLatentModels", "gradient", "ADTypes.AutoReverseDiff()", "linked"]`	119.093 μs (5%)	62.64 KiB (1%)	1012
`["EpiLatentModels", "ConcatLatentModels", "gradient", "ADTypes.AutoReverseDiff()", "standard"]`	97.302 μs (5%)	49.45 KiB (1%)	879
`["EpiLatentModels", "ConcatLatentModels", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	5.520 μs (5%)	2.06 KiB (1%)	51
`["EpiLatentModels", "ConcatLatentModels", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	5.313 μs (5%)	2.06 KiB (1%)	51
`["EpiLatentModels", "DiffLatentModel", "evaluation", "linked"]`	8.172 μs (5%)	5.55 KiB (1%)	102
`["EpiLatentModels", "DiffLatentModel", "evaluation", "standard"]`	7.747 μs (5%)	3.86 KiB (1%)	96
`["EpiLatentModels", "DiffLatentModel", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"]`	9.298 μs (5%)	13.92 KiB (1%)	115
`["EpiLatentModels", "DiffLatentModel", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"]`	8.706 μs (5%)	12.23 KiB (1%)	109
`["EpiLatentModels", "DiffLatentModel", "gradient", "ADTypes.AutoReverseDiff()", "linked"]`	87.313 μs (5%)	39.58 KiB (1%)	824
`["EpiLatentModels", "DiffLatentModel", "gradient", "ADTypes.AutoReverseDiff()", "standard"]`	68.859 μs (5%)	33.14 KiB (1%)	715
`["EpiLatentModels", "DiffLatentModel", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	6.955 μs (5%)	2.12 KiB (1%)	50
`["EpiLatentModels", "DiffLatentModel", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	6.767 μs (5%)	2.12 KiB (1%)	50
`["EpiLatentModels", "HierarchicalNormal", "evaluation", "linked"]`	402.355 ns (5%)	1.03 KiB (1%)	14
`["EpiLatentModels", "HierarchicalNormal", "evaluation", "standard"]`	305.697 ns (5%)	896 bytes (1%)	13
`["EpiLatentModels", "HierarchicalNormal", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"]`	1.096 μs (5%)	5.12 KiB (1%)	23
`["EpiLatentModels", "HierarchicalNormal", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"]`	872.700 ns (5%)	4.97 KiB (1%)	22
`["EpiLatentModels", "HierarchicalNormal", "gradient", "ADTypes.AutoReverseDiff()", "linked"]`	43.992 μs (5%)	19.03 KiB (1%)	381
`["EpiLatentModels", "HierarchicalNormal", "gradient", "ADTypes.AutoReverseDiff()", "standard"]`	28.063 μs (5%)	14.17 KiB (1%)	278
`["EpiLatentModels", "HierarchicalNormal", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	1.114 μs (5%)	608 bytes (1%)	9
`["EpiLatentModels", "HierarchicalNormal", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	932.500 ns (5%)	608 bytes (1%)	9
`["EpiLatentModels", "Intercept", "evaluation", "linked"]`	203.817 ns (5%)	400 bytes (1%)	10
`["EpiLatentModels", "Intercept", "evaluation", "standard"]`	191.531 ns (5%)	400 bytes (1%)	10
`["EpiLatentModels", "Intercept", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"]`	292.136 ns (5%)	704 bytes (1%)	17
`["EpiLatentModels", "Intercept", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"]`	285.104 ns (5%)	704 bytes (1%)	17
`["EpiLatentModels", "Intercept", "gradient", "ADTypes.AutoReverseDiff()", "linked"]`	5.499 μs (5%)	3.62 KiB (1%)	87
`["EpiLatentModels", "Intercept", "gradient", "ADTypes.AutoReverseDiff()", "standard"]`	5.470 μs (5%)	3.62 KiB (1%)	87
`["EpiLatentModels", "Intercept", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	391.723 ns (5%)	208 bytes (1%)	4
`["EpiLatentModels", "Intercept", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	405.105 ns (5%)	208 bytes (1%)	4
`["EpiLatentModels", "PrefixLatentModel", "evaluation", "linked"]`	1.887 μs (5%)	3.56 KiB (1%)	40
`["EpiLatentModels", "PrefixLatentModel", "evaluation", "standard"]`	1.649 μs (5%)	3.09 KiB (1%)	37
`["EpiLatentModels", "PrefixLatentModel", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"]`	2.642 μs (5%)	7.66 KiB (1%)	49
`["EpiLatentModels", "PrefixLatentModel", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"]`	2.342 μs (5%)	7.19 KiB (1%)	46
`["EpiLatentModels", "PrefixLatentModel", "gradient", "ADTypes.AutoReverseDiff()", "linked"]`	46.838 μs (5%)	21.42 KiB (1%)	406
`["EpiLatentModels", "PrefixLatentModel", "gradient", "ADTypes.AutoReverseDiff()", "standard"]`	30.918 μs (5%)	16.25 KiB (1%)	301
`["EpiLatentModels", "PrefixLatentModel", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	1.111 μs (5%)	608 bytes (1%)	9
`["EpiLatentModels", "PrefixLatentModel", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	941.385 ns (5%)	608 bytes (1%)	9
`["EpiLatentModels", "RandomWalk", "evaluation", "linked"]`	584.180 ns (5%)	1.72 KiB (1%)	21
`["EpiLatentModels", "RandomWalk", "evaluation", "standard"]`	437.737 ns (5%)	1.28 KiB (1%)	19
`["EpiLatentModels", "RandomWalk", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"]`	1.512 μs (5%)	8.11 KiB (1%)	32
`["EpiLatentModels", "RandomWalk", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"]`	1.289 μs (5%)	7.67 KiB (1%)	30
`["EpiLatentModels", "RandomWalk", "gradient", "ADTypes.AutoReverseDiff()", "linked"]`	53.410 μs (5%)	25.47 KiB (1%)	501
`["EpiLatentModels", "RandomWalk", "gradient", "ADTypes.AutoReverseDiff()", "standard"]`	35.807 μs (5%)	20.28 KiB (1%)	396
`["EpiLatentModels", "RandomWalk", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	3.668 μs (5%)	1.28 KiB (1%)	29
`["EpiLatentModels", "RandomWalk", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	3.453 μs (5%)	1.28 KiB (1%)	29
`["EpiLatentModels", "RecordExpectedLatent", "evaluation", "linked"]`	546.797 ns (5%)	1.25 KiB (1%)	20
`["EpiLatentModels", "RecordExpectedLatent", "evaluation", "standard"]`	409.465 ns (5%)	960 bytes (1%)	18
`["EpiLatentModels", "RecordExpectedLatent", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"]`	708.658 ns (5%)	1.78 KiB (1%)	29
`["EpiLatentModels", "RecordExpectedLatent", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"]`	561.870 ns (5%)	1.47 KiB (1%)	27
`["EpiLatentModels", "RecordExpectedLatent", "gradient", "ADTypes.AutoReverseDiff()", "linked"]`	44.082 μs (5%)	18.31 KiB (1%)	387
`["EpiLatentModels", "RecordExpectedLatent", "gradient", "ADTypes.AutoReverseDiff()", "standard"]`	27.692 μs (5%)	13.30 KiB (1%)	283
`["EpiLatentModels", "RecordExpectedLatent", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	1.035 μs (5%)	352 bytes (1%)	9
`["EpiLatentModels", "RecordExpectedLatent", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	847.192 ns (5%)	352 bytes (1%)	9
`["EpiLatentModels", "TransformLatentModel", "evaluation", "linked"]`	258.159 ns (5%)	448 bytes (1%)	12
`["EpiLatentModels", "TransformLatentModel", "evaluation", "standard"]`	248.649 ns (5%)	448 bytes (1%)	12
`["EpiLatentModels", "TransformLatentModel", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"]`	329.543 ns (5%)	768 bytes (1%)	19
`["EpiLatentModels", "TransformLatentModel", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"]`	317.616 ns (5%)	768 bytes (1%)	19
`["EpiLatentModels", "TransformLatentModel", "gradient", "ADTypes.AutoReverseDiff()", "linked"]`	5.781 μs (5%)	3.93 KiB (1%)	95
`["EpiLatentModels", "TransformLatentModel", "gradient", "ADTypes.AutoReverseDiff()", "standard"]`	5.707 μs (5%)	3.93 KiB (1%)	95
`["EpiLatentModels", "TransformLatentModel", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	474.204 ns (5%)	160 bytes (1%)	4
`["EpiLatentModels", "TransformLatentModel", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	482.536 ns (5%)	160 bytes (1%)	4
`["EpiLatentModels", "broadcast_dayofweek", "evaluation", "linked"]`	1.202 μs (5%)	3.73 KiB (1%)	43
`["EpiLatentModels", "broadcast_dayofweek", "evaluation", "standard"]`	899.500 ns (5%)	2.42 KiB (1%)	37
`["EpiLatentModels", "broadcast_dayofweek", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"]`	2.138 μs (5%)	9.80 KiB (1%)	54
`["EpiLatentModels", "broadcast_dayofweek", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"]`	1.771 μs (5%)	8.48 KiB (1%)	48
`["EpiLatentModels", "broadcast_dayofweek", "gradient", "ADTypes.AutoReverseDiff()", "linked"]`	70.432 μs (5%)	34.64 KiB (1%)	699
`["EpiLatentModels", "broadcast_dayofweek", "gradient", "ADTypes.AutoReverseDiff()", "standard"]`	51.586 μs (5%)	28.58 KiB (1%)	590
`["EpiLatentModels", "broadcast_dayofweek", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	5.038 μs (5%)	1.19 KiB (1%)	29
`["EpiLatentModels", "broadcast_dayofweek", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	4.881 μs (5%)	1.19 KiB (1%)	29
`["EpiLatentModels", "broadcast_weekly", "evaluation", "linked"]`	8.489 μs (5%)	5.78 KiB (1%)	113
`["EpiLatentModels", "broadcast_weekly", "evaluation", "standard"]`	7.777 μs (5%)	3.95 KiB (1%)	103
`["EpiLatentModels", "broadcast_weekly", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"]`	9.016 μs (5%)	8.98 KiB (1%)	130
`["EpiLatentModels", "broadcast_weekly", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"]`	8.426 μs (5%)	6.83 KiB (1%)	116
`["EpiLatentModels", "broadcast_weekly", "gradient", "ADTypes.AutoReverseDiff()", "linked"]`	102.782 μs (5%)	42.11 KiB (1%)	852
`["EpiLatentModels", "broadcast_weekly", "gradient", "ADTypes.AutoReverseDiff()", "standard"]`	64.070 μs (5%)	29.47 KiB (1%)	599
`["EpiLatentModels", "broadcast_weekly", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	5.659 μs (5%)	1.69 KiB (1%)	48
`["EpiLatentModels", "broadcast_weekly", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	5.073 μs (5%)	1.56 KiB (1%)	44
`["EpiObsModels", "Ascertainment", "evaluation", "linked"]`	3.250 μs (5%)	3.58 KiB (1%)	63
`["EpiObsModels", "Ascertainment", "evaluation", "standard"]`	3.174 μs (5%)	3.58 KiB (1%)	63
`["EpiObsModels", "Ascertainment", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"]`	4.049 μs (5%)	3.92 KiB (1%)	72
`["EpiObsModels", "Ascertainment", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"]`	3.999 μs (5%)	3.92 KiB (1%)	72
`["EpiObsModels", "Ascertainment", "gradient", "ADTypes.AutoReverseDiff()", "linked"]`	77.315 μs (5%)	38.77 KiB (1%)	918
`["EpiObsModels", "Ascertainment", "gradient", "ADTypes.AutoReverseDiff()", "standard"]`	61.735 μs (5%)	34.02 KiB (1%)	815
`["EpiObsModels", "Ascertainment", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	5.627 μs (5%)	96 bytes (1%)	3
`["EpiObsModels", "Ascertainment", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	5.503 μs (5%)	96 bytes (1%)	3
`["EpiObsModels", "LatentDelay", "evaluation", "linked"]`	14.246 μs (5%)	22.08 KiB (1%)	412
`["EpiObsModels", "LatentDelay", "evaluation", "standard"]`	14.217 μs (5%)	22.08 KiB (1%)	412
`["EpiObsModels", "LatentDelay", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"]`	18.104 μs (5%)	22.30 KiB (1%)	419
`["EpiObsModels", "LatentDelay", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"]`	17.573 μs (5%)	22.30 KiB (1%)	419
`["EpiObsModels", "LatentDelay", "gradient", "ADTypes.AutoReverseDiff()", "linked"]`	542.023 μs (5%)	293.05 KiB (1%)	7011
`["EpiObsModels", "LatentDelay", "gradient", "ADTypes.AutoReverseDiff()", "standard"]`	519.341 μs (5%)	288.30 KiB (1%)	6908
`["EpiObsModels", "LatentDelay", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	51.045 μs (5%)	96 bytes (1%)	3
`["EpiObsModels", "LatentDelay", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	50.804 μs (5%)	96 bytes (1%)	3
`["EpiObsModels", "NegativeBinomialError", "evaluation", "linked"]`	1.118 μs (5%)	400 bytes (1%)	10
`["EpiObsModels", "NegativeBinomialError", "evaluation", "standard"]`	1.075 μs (5%)	400 bytes (1%)	10
`["EpiObsModels", "NegativeBinomialError", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"]`	1.600 μs (5%)	624 bytes (1%)	17
`["EpiObsModels", "NegativeBinomialError", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"]`	1.531 μs (5%)	624 bytes (1%)	17
`["EpiObsModels", "NegativeBinomialError", "gradient", "ADTypes.AutoReverseDiff()", "linked"]`	73.337 μs (5%)	35.95 KiB (1%)	903
`["EpiObsModels", "NegativeBinomialError", "gradient", "ADTypes.AutoReverseDiff()", "standard"]`	58.650 μs (5%)	31.20 KiB (1%)	800
`["EpiObsModels", "NegativeBinomialError", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	5.655 μs (5%)	96 bytes (1%)	3
`["EpiObsModels", "NegativeBinomialError", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	5.482 μs (5%)	96 bytes (1%)	3
`["EpiObsModels", "PoissonError", "evaluation", "linked"]`	1.312 μs (5%)	1.83 KiB (1%)	30
`["EpiObsModels", "PoissonError", "evaluation", "standard"]`	990.800 ns (5%)	1.47 KiB (1%)	26
`["EpiObsModels", "PoissonError", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"]`	3.182 μs (5%)	7.78 KiB (1%)	43
`["EpiObsModels", "PoissonError", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"]`	2.433 μs (5%)	4.70 KiB (1%)	35
`["EpiObsModels", "PoissonError", "gradient", "ADTypes.AutoReverseDiff()", "linked"]`	166.541 μs (5%)	86.44 KiB (1%)	1870
`["EpiObsModels", "PoissonError", "gradient", "ADTypes.AutoReverseDiff()", "standard"]`	48.180 μs (5%)	29.38 KiB (1%)	727
`["EpiObsModels", "PoissonError", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	6.057 μs (5%)	176 bytes (1%)	3
`["EpiObsModels", "PoissonError", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	4.120 μs (5%)	176 bytes (1%)	3
`["EpiObsModels", "PrefixObservationModel", "evaluation", "linked"]`	1.653 μs (5%)	1.56 KiB (1%)	34
`["EpiObsModels", "PrefixObservationModel", "evaluation", "standard"]`	1.589 μs (5%)	1.56 KiB (1%)	34
`["EpiObsModels", "PrefixObservationModel", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"]`	1.795 μs (5%)	1.78 KiB (1%)	41
`["EpiObsModels", "PrefixObservationModel", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"]`	1.727 μs (5%)	1.78 KiB (1%)	41
`["EpiObsModels", "PrefixObservationModel", "gradient", "ADTypes.AutoReverseDiff()", "linked"]`	27.521 μs (5%)	12.59 KiB (1%)	290
`["EpiObsModels", "PrefixObservationModel", "gradient", "ADTypes.AutoReverseDiff()", "standard"]`	13.174 μs (5%)	7.84 KiB (1%)	187
`["EpiObsModels", "PrefixObservationModel", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	1.117 μs (5%)	96 bytes (1%)	3
`["EpiObsModels", "PrefixObservationModel", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	974.800 ns (5%)	96 bytes (1%)	3
`["EpiObsModels", "RecordExpectedObs", "evaluation", "linked"]`	758.373 ns (5%)	480 bytes (1%)	14
`["EpiObsModels", "RecordExpectedObs", "evaluation", "standard"]`	709.340 ns (5%)	480 bytes (1%)	14
`["EpiObsModels", "RecordExpectedObs", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"]`	1.081 μs (5%)	704 bytes (1%)	21
`["EpiObsModels", "RecordExpectedObs", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"]`	984.077 ns (5%)	704 bytes (1%)	21
`["EpiObsModels", "RecordExpectedObs", "gradient", "ADTypes.AutoReverseDiff()", "linked"]`	46.287 μs (5%)	22.98 KiB (1%)	537
`["EpiObsModels", "RecordExpectedObs", "gradient", "ADTypes.AutoReverseDiff()", "standard"]`	32.630 μs (5%)	18.23 KiB (1%)	434
`["EpiObsModels", "RecordExpectedObs", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	3.176 μs (5%)	96 bytes (1%)	3
`["EpiObsModels", "RecordExpectedObs", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	2.987 μs (5%)	96 bytes (1%)	3
`["EpiObsModels", "StackObservationModels", "evaluation", "linked"]`	6.793 μs (5%)	5.81 KiB (1%)	117
`["EpiObsModels", "StackObservationModels", "evaluation", "standard"]`	6.658 μs (5%)	5.81 KiB (1%)	117
`["EpiObsModels", "StackObservationModels", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"]`	7.704 μs (5%)	6.16 KiB (1%)	126
`["EpiObsModels", "StackObservationModels", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"]`	7.577 μs (5%)	6.16 KiB (1%)	126
`["EpiObsModels", "StackObservationModels", "gradient", "ADTypes.AutoReverseDiff()", "linked"]`	89.878 μs (5%)	49.09 KiB (1%)	1044
`["EpiObsModels", "StackObservationModels", "gradient", "ADTypes.AutoReverseDiff()", "standard"]`	71.744 μs (5%)	44.34 KiB (1%)	941
`["EpiObsModels", "StackObservationModels", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	6.210 μs (5%)	96 bytes (1%)	3
`["EpiObsModels", "StackObservationModels", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	6.037 μs (5%)	96 bytes (1%)	3
`["EpiObsModels", "TransformObservationModel", "evaluation", "linked"]`	1.413 μs (5%)	672 bytes (1%)	16
`["EpiObsModels", "TransformObservationModel", "evaluation", "standard"]`	1.371 μs (5%)	672 bytes (1%)	16
`["EpiObsModels", "TransformObservationModel", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"]`	1.913 μs (5%)	896 bytes (1%)	23
`["EpiObsModels", "TransformObservationModel", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"]`	1.804 μs (5%)	896 bytes (1%)	23
`["EpiObsModels", "TransformObservationModel", "gradient", "ADTypes.AutoReverseDiff()", "linked"]`	73.247 μs (5%)	35.59 KiB (1%)	869
`["EpiObsModels", "TransformObservationModel", "gradient", "ADTypes.AutoReverseDiff()", "standard"]`	59.261 μs (5%)	30.84 KiB (1%)	766
`["EpiObsModels", "TransformObservationModel", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	5.734 μs (5%)	96 bytes (1%)	3
`["EpiObsModels", "TransformObservationModel", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	5.489 μs (5%)	96 bytes (1%)	3
`["EpiObsModels", "ascertainment_dayofweek", "evaluation", "linked"]`	4.146 μs (5%)	9.09 KiB (1%)	100
`["EpiObsModels", "ascertainment_dayofweek", "evaluation", "standard"]`	3.663 μs (5%)	7.84 KiB (1%)	92
`["EpiObsModels", "ascertainment_dayofweek", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"]`	5.973 μs (5%)	16.22 KiB (1%)	111
`["EpiObsModels", "ascertainment_dayofweek", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"]`	5.477 μs (5%)	14.97 KiB (1%)	103
`["EpiObsModels", "ascertainment_dayofweek", "gradient", "ADTypes.AutoReverseDiff()", "linked"]`	106.068 μs (5%)	59.83 KiB (1%)	1164
`["EpiObsModels", "ascertainment_dayofweek", "gradient", "ADTypes.AutoReverseDiff()", "standard"]`	87.864 μs (5%)	53.88 KiB (1%)	1054
`["EpiObsModels", "ascertainment_dayofweek", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	5.519 μs (5%)	496 bytes (1%)	9
`["EpiObsModels", "ascertainment_dayofweek", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	5.290 μs (5%)	496 bytes (1%)	9
`["EpiObsModels", "observation_error", "missing obs", "evaluation", "linked"]`	1.438 μs (5%)	3.05 KiB (1%)	38
`["EpiObsModels", "observation_error", "missing obs", "evaluation", "standard"]`	899.340 ns (5%)	1.48 KiB (1%)	28
`["EpiObsModels", "observation_error", "missing obs", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"]`	1.789 μs (5%)	4.11 KiB (1%)	48
`["EpiObsModels", "observation_error", "missing obs", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"]`	1.213 μs (5%)	2.55 KiB (1%)	38
`["EpiObsModels", "observation_error", "missing obs", "gradient", "ADTypes.AutoReverseDiff()", "linked"]`	41.447 μs (5%)	25.78 KiB (1%)	533
`["EpiObsModels", "observation_error", "missing obs", "gradient", "ADTypes.AutoReverseDiff()", "standard"]`	23.894 μs (5%)	18.80 KiB (1%)	401
`["EpiObsModels", "observation_error", "missing obs", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	2.189 μs (5%)	144 bytes (1%)	3
`["EpiObsModels", "observation_error", "missing obs", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	1.968 μs (5%)	144 bytes (1%)	3
`["EpiObsModels", "observation_error", "no missing obs", "evaluation", "linked"]`	408.060 ns (5%)	352 bytes (1%)	10
`["EpiObsModels", "observation_error", "no missing obs", "evaluation", "standard"]`	348.869 ns (5%)	352 bytes (1%)	10
`["EpiObsModels", "observation_error", "no missing obs", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"]`	529.138 ns (5%)	576 bytes (1%)	17
`["EpiObsModels", "observation_error", "no missing obs", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"]`	482.133 ns (5%)	576 bytes (1%)	17
`["EpiObsModels", "observation_error", "no missing obs", "gradient", "ADTypes.AutoReverseDiff()", "linked"]`	32.681 μs (5%)	19.64 KiB (1%)	446
`["EpiObsModels", "observation_error", "no missing obs", "gradient", "ADTypes.AutoReverseDiff()", "standard"]`	18.425 μs (5%)	14.22 KiB (1%)	324
`["EpiObsModels", "observation_error", "no missing obs", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	1.857 μs (5%)	96 bytes (1%)	3
`["EpiObsModels", "observation_error", "no missing obs", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	1.669 μs (5%)	96 bytes (1%)	3
`["EpiObsModels", "observation_error", "partially missing obs", "evaluation", "linked"]`	31.258 μs (5%)	3.02 KiB (1%)	64
`["EpiObsModels", "observation_error", "partially missing obs", "evaluation", "standard"]`	31.049 μs (5%)	2.70 KiB (1%)	62
`["EpiObsModels", "observation_error", "partially missing obs", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "linked"]`	21.290 μs (5%)	2.73 KiB (1%)	51
`["EpiObsModels", "observation_error", "partially missing obs", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)", "standard"]`	21.149 μs (5%)	2.42 KiB (1%)	49
`["EpiObsModels", "observation_error", "partially missing obs", "gradient", "ADTypes.AutoReverseDiff()", "linked"]`	65.543 μs (5%)	26.11 KiB (1%)	555
`["EpiObsModels", "observation_error", "partially missing obs", "gradient", "ADTypes.AutoReverseDiff()", "standard"]`	45.224 μs (5%)	20.38 KiB (1%)	431
`["EpiObsModels", "observation_error", "partially missing obs", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "linked"]`	2.218 μs (5%)	112 bytes (1%)	3
`["EpiObsModels", "observation_error", "partially missing obs", "gradient", "ADTypes.AutoReverseDiff(compile=true)", "standard"]`	1.997 μs (5%)	112 bytes (1%)	3

Benchmark Group List

Here's a list of all the benchmark groups executed by this job:

["EpiAwareUtils"]
["EpiInfModels", "DirectInfections", "evaluation"]
["EpiInfModels", "DirectInfections", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)"]
["EpiInfModels", "DirectInfections", "gradient", "ADTypes.AutoReverseDiff()"]
["EpiInfModels", "DirectInfections", "gradient", "ADTypes.AutoReverseDiff(compile=true)"]
["EpiInfModels", "ExpGrowthRate", "evaluation"]
["EpiInfModels", "ExpGrowthRate", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)"]
["EpiInfModels", "ExpGrowthRate", "gradient", "ADTypes.AutoReverseDiff()"]
["EpiInfModels", "ExpGrowthRate", "gradient", "ADTypes.AutoReverseDiff(compile=true)"]
["EpiLatentModels", "AR", "evaluation"]
["EpiLatentModels", "AR", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)"]
["EpiLatentModels", "AR", "gradient", "ADTypes.AutoReverseDiff()"]
["EpiLatentModels", "AR", "gradient", "ADTypes.AutoReverseDiff(compile=true)"]
["EpiLatentModels", "BroadcastLatentModel", "evaluation"]
["EpiLatentModels", "BroadcastLatentModel", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)"]
["EpiLatentModels", "BroadcastLatentModel", "gradient", "ADTypes.AutoReverseDiff()"]
["EpiLatentModels", "BroadcastLatentModel", "gradient", "ADTypes.AutoReverseDiff(compile=true)"]
["EpiLatentModels", "CombineLatentModels", "evaluation"]
["EpiLatentModels", "CombineLatentModels", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)"]
["EpiLatentModels", "CombineLatentModels", "gradient", "ADTypes.AutoReverseDiff()"]
["EpiLatentModels", "CombineLatentModels", "gradient", "ADTypes.AutoReverseDiff(compile=true)"]
["EpiLatentModels", "ConcatLatentModels", "evaluation"]
["EpiLatentModels", "ConcatLatentModels", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)"]
["EpiLatentModels", "ConcatLatentModels", "gradient", "ADTypes.AutoReverseDiff()"]
["EpiLatentModels", "ConcatLatentModels", "gradient", "ADTypes.AutoReverseDiff(compile=true)"]
["EpiLatentModels", "DiffLatentModel", "evaluation"]
["EpiLatentModels", "DiffLatentModel", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)"]
["EpiLatentModels", "DiffLatentModel", "gradient", "ADTypes.AutoReverseDiff()"]
["EpiLatentModels", "DiffLatentModel", "gradient", "ADTypes.AutoReverseDiff(compile=true)"]
["EpiLatentModels", "HierarchicalNormal", "evaluation"]
["EpiLatentModels", "HierarchicalNormal", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)"]
["EpiLatentModels", "HierarchicalNormal", "gradient", "ADTypes.AutoReverseDiff()"]
["EpiLatentModels", "HierarchicalNormal", "gradient", "ADTypes.AutoReverseDiff(compile=true)"]
["EpiLatentModels", "Intercept", "evaluation"]
["EpiLatentModels", "Intercept", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)"]
["EpiLatentModels", "Intercept", "gradient", "ADTypes.AutoReverseDiff()"]
["EpiLatentModels", "Intercept", "gradient", "ADTypes.AutoReverseDiff(compile=true)"]
["EpiLatentModels", "PrefixLatentModel", "evaluation"]
["EpiLatentModels", "PrefixLatentModel", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)"]
["EpiLatentModels", "PrefixLatentModel", "gradient", "ADTypes.AutoReverseDiff()"]
["EpiLatentModels", "PrefixLatentModel", "gradient", "ADTypes.AutoReverseDiff(compile=true)"]
["EpiLatentModels", "RandomWalk", "evaluation"]
["EpiLatentModels", "RandomWalk", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)"]
["EpiLatentModels", "RandomWalk", "gradient", "ADTypes.AutoReverseDiff()"]
["EpiLatentModels", "RandomWalk", "gradient", "ADTypes.AutoReverseDiff(compile=true)"]
["EpiLatentModels", "RecordExpectedLatent", "evaluation"]
["EpiLatentModels", "RecordExpectedLatent", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)"]
["EpiLatentModels", "RecordExpectedLatent", "gradient", "ADTypes.AutoReverseDiff()"]
["EpiLatentModels", "RecordExpectedLatent", "gradient", "ADTypes.AutoReverseDiff(compile=true)"]
["EpiLatentModels", "TransformLatentModel", "evaluation"]
["EpiLatentModels", "TransformLatentModel", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)"]
["EpiLatentModels", "TransformLatentModel", "gradient", "ADTypes.AutoReverseDiff()"]
["EpiLatentModels", "TransformLatentModel", "gradient", "ADTypes.AutoReverseDiff(compile=true)"]
["EpiLatentModels", "broadcast_dayofweek", "evaluation"]
["EpiLatentModels", "broadcast_dayofweek", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)"]
["EpiLatentModels", "broadcast_dayofweek", "gradient", "ADTypes.AutoReverseDiff()"]
["EpiLatentModels", "broadcast_dayofweek", "gradient", "ADTypes.AutoReverseDiff(compile=true)"]
["EpiLatentModels", "broadcast_weekly", "evaluation"]
["EpiLatentModels", "broadcast_weekly", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)"]
["EpiLatentModels", "broadcast_weekly", "gradient", "ADTypes.AutoReverseDiff()"]
["EpiLatentModels", "broadcast_weekly", "gradient", "ADTypes.AutoReverseDiff(compile=true)"]
["EpiObsModels", "Ascertainment", "evaluation"]
["EpiObsModels", "Ascertainment", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)"]
["EpiObsModels", "Ascertainment", "gradient", "ADTypes.AutoReverseDiff()"]
["EpiObsModels", "Ascertainment", "gradient", "ADTypes.AutoReverseDiff(compile=true)"]
["EpiObsModels", "LatentDelay", "evaluation"]
["EpiObsModels", "LatentDelay", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)"]
["EpiObsModels", "LatentDelay", "gradient", "ADTypes.AutoReverseDiff()"]
["EpiObsModels", "LatentDelay", "gradient", "ADTypes.AutoReverseDiff(compile=true)"]
["EpiObsModels", "NegativeBinomialError", "evaluation"]
["EpiObsModels", "NegativeBinomialError", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)"]
["EpiObsModels", "NegativeBinomialError", "gradient", "ADTypes.AutoReverseDiff()"]
["EpiObsModels", "NegativeBinomialError", "gradient", "ADTypes.AutoReverseDiff(compile=true)"]
["EpiObsModels", "PoissonError", "evaluation"]
["EpiObsModels", "PoissonError", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)"]
["EpiObsModels", "PoissonError", "gradient", "ADTypes.AutoReverseDiff()"]
["EpiObsModels", "PoissonError", "gradient", "ADTypes.AutoReverseDiff(compile=true)"]
["EpiObsModels", "PrefixObservationModel", "evaluation"]
["EpiObsModels", "PrefixObservationModel", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)"]
["EpiObsModels", "PrefixObservationModel", "gradient", "ADTypes.AutoReverseDiff()"]
["EpiObsModels", "PrefixObservationModel", "gradient", "ADTypes.AutoReverseDiff(compile=true)"]
["EpiObsModels", "RecordExpectedObs", "evaluation"]
["EpiObsModels", "RecordExpectedObs", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)"]
["EpiObsModels", "RecordExpectedObs", "gradient", "ADTypes.AutoReverseDiff()"]
["EpiObsModels", "RecordExpectedObs", "gradient", "ADTypes.AutoReverseDiff(compile=true)"]
["EpiObsModels", "StackObservationModels", "evaluation"]
["EpiObsModels", "StackObservationModels", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)"]
["EpiObsModels", "StackObservationModels", "gradient", "ADTypes.AutoReverseDiff()"]
["EpiObsModels", "StackObservationModels", "gradient", "ADTypes.AutoReverseDiff(compile=true)"]
["EpiObsModels", "TransformObservationModel", "evaluation"]
["EpiObsModels", "TransformObservationModel", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)"]
["EpiObsModels", "TransformObservationModel", "gradient", "ADTypes.AutoReverseDiff()"]
["EpiObsModels", "TransformObservationModel", "gradient", "ADTypes.AutoReverseDiff(compile=true)"]
["EpiObsModels", "ascertainment_dayofweek", "evaluation"]
["EpiObsModels", "ascertainment_dayofweek", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)"]
["EpiObsModels", "ascertainment_dayofweek", "gradient", "ADTypes.AutoReverseDiff()"]
["EpiObsModels", "ascertainment_dayofweek", "gradient", "ADTypes.AutoReverseDiff(compile=true)"]
["EpiObsModels", "observation_error", "missing obs", "evaluation"]
["EpiObsModels", "observation_error", "missing obs", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)"]
["EpiObsModels", "observation_error", "missing obs", "gradient", "ADTypes.AutoReverseDiff()"]
["EpiObsModels", "observation_error", "missing obs", "gradient", "ADTypes.AutoReverseDiff(compile=true)"]
["EpiObsModels", "observation_error", "no missing obs", "evaluation"]
["EpiObsModels", "observation_error", "no missing obs", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)"]
["EpiObsModels", "observation_error", "no missing obs", "gradient", "ADTypes.AutoReverseDiff()"]
["EpiObsModels", "observation_error", "no missing obs", "gradient", "ADTypes.AutoReverseDiff(compile=true)"]
["EpiObsModels", "observation_error", "partially missing obs", "evaluation"]
["EpiObsModels", "observation_error", "partially missing obs", "gradient", "ADTypes.AutoForwardDiff(chunksize=0)"]
["EpiObsModels", "observation_error", "partially missing obs", "gradient", "ADTypes.AutoReverseDiff()"]
["EpiObsModels", "observation_error", "partially missing obs", "gradient", "ADTypes.AutoReverseDiff(compile=true)"]

Julia versioninfo

Julia Version 1.11.2
Commit 5e9a32e7af2 (2024-12-01 20:02 UTC)
Build Info:
  Official https://julialang.org/ release
Platform Info:
  OS: Linux (x86_64-linux-gnu)
      Ubuntu 22.04.5 LTS
  uname: Linux 6.5.0-1025-azure #26~22.04.1-Ubuntu SMP Thu Jul 11 22:33:04 UTC 2024 x86_64 x86_64
  CPU: AMD EPYC 7763 64-Core Processor: 
              speed         user         nice          sys         idle          irq
       #1     0 MHz      15804 s          0 s       1220 s      32233 s          0 s
       #2     0 MHz      15608 s          0 s       1254 s      32405 s          0 s
       #3     0 MHz      15156 s          0 s       1254 s      32835 s          0 s
       #4     0 MHz      13905 s          0 s       1212 s      34137 s          0 s
  Memory: 15.606491088867188 GB (12734.84765625 MB free)
  Uptime: 4935.9 sec
  Load Avg:  1.16  1.04  1.01
  WORD_SIZE: 64
  LLVM: libLLVM-16.0.6 (ORCJIT, znver3)
Threads: 1 default, 0 interactive, 1 GC (on 4 virtual cores)

Runtime information

Runtime Info
BLAS #threads	2
`BLAS.vendor()`	`lbt`
`Sys.CPU_THREADS`	4

lscpu output:

Architecture:                       x86_64
CPU op-mode(s):                     32-bit, 64-bit
Address sizes:                      48 bits physical, 48 bits virtual
Byte Order:                         Little Endian
CPU(s):                             4
On-line CPU(s) list:                0-3
Vendor ID:                          AuthenticAMD
Model name:                         AMD EPYC 7763 64-Core Processor
CPU family:                         25
Model:                              1
Thread(s) per core:                 2
Core(s) per socket:                 2
Socket(s):                          1
Stepping:                           1
BogoMIPS:                           4890.85
Flags:                              fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm constant_tsc rep_good nopl tsc_reliable nonstop_tsc cpuid extd_apicid aperfmperf pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm cmp_legacy svm cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw topoext invpcid_single vmmcall fsgsbase bmi1 avx2 smep bmi2 erms invpcid rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 xsaves clzero xsaveerptr rdpru arat npt nrip_save tsc_scale vmcb_clean flushbyasid decodeassists pausefilter pfthreshold v_vmsave_vmload umip vaes vpclmulqdq rdpid fsrm
Virtualization:                     AMD-V
Hypervisor vendor:                  Microsoft
Virtualization type:                full
L1d cache:                          64 KiB (2 instances)
L1i cache:                          64 KiB (2 instances)
L2 cache:                           1 MiB (2 instances)
L3 cache:                           32 MiB (1 instance)
NUMA node(s):                       1
NUMA node0 CPU(s):                  0-3
Vulnerability Gather data sampling: Not affected
Vulnerability Itlb multihit:        Not affected
Vulnerability L1tf:                 Not affected
Vulnerability Mds:                  Not affected
Vulnerability Meltdown:             Not affected
Vulnerability Mmio stale data:      Not affected
Vulnerability Retbleed:             Not affected
Vulnerability Spec rstack overflow: Vulnerable: Safe RET, no microcode
Vulnerability Spec store bypass:    Vulnerable
Vulnerability Spectre v1:           Mitigation; usercopy/swapgs barriers and __user pointer sanitization
Vulnerability Spectre v2:           Mitigation; Retpolines; STIBP disabled; RSB filling; PBRSB-eIBRS Not affected; BHI Not affected
Vulnerability Srbds:                Not affected
Vulnerability Tsx async abort:      Not affected

Cpu Property	Value
Brand	AMD EPYC 7763 64-Core Processor
Vendor	:AMD
Architecture	:Unknown
Model	Family: 0xaf, Model: 0x01, Stepping: 0x01, Type: 0x00
Cores	16 physical cores, 16 logical cores (on executing CPU)
	No Hyperthreading hardware capability detected
Clock Frequencies	Not supported by CPU
Data Cache	Level 1:3 : (32, 512, 32768) kbytes
	64 byte cache line size
Address Size	48 bits virtual, 48 bits physical
SIMD	256 bit = 32 byte max. SIMD vector size
Time Stamp Counter	TSC is accessible via `rdtsc`
	TSC runs at constant rate (invariant from clock frequency)
Perf. Monitoring	Performance Monitoring Counters (PMC) are not supported
Hypervisor	Yes, Microsoft

seabbs · 2024-12-10T15:37:48Z

I put in some control code and reduced the broadcasting in HierarchicalNormal which seems to have marginally helped the performance regressions

seabbs · 2024-12-11T10:04:18Z

@SamuelBrand1 any chance you can get to this today?

SamuelBrand1

The core contribution of this PR is adding the MA moving-average model, and helper functions that stack MA AR and DiffLatent to construct any ARIMA model through model composition. I think this is a great pattern, and opens the way to using ARIMA models inside/compositionally with model units that are more specialised to epi modelling.

Also lot of changes to promote common approaches in the code base. This is really good.

My only blocker to merging is that I'm not sure that #440 is addressed, which was part of the plan (cf #441). I don't think thats a major lift (it might already be addressed but I'm not sure). Otherwise, just some comments that look like future issues.

EpiAware/src/EpiLatentModels/models/MA.jl

EpiAware/src/EpiLatentModels/combinations/arima.jl

EpiAware/src/EpiLatentModels/combinations/arma.jl

EpiAware/src/EpiLatentModels/models/AR.jl

EpiAware/src/EpiLatentModels/models/IID.jl

EpiAware/src/EpiLatentModels/models/RandomWalk.jl

EpiAware/src/EpiLatentModels/models/HierarchicalNormal.jl

SamuelBrand1 · 2024-12-11T12:09:16Z

Based on the benchmarks this looks like its quite a lot slower for all low-level models (and dependent benchmarks) in all cases except where compiled reverse diff is used. I think that is probably just the cost of dispatching through the multiple new submodel calls vs specifying the iid process directly and so somewhat unavoidable if we want this functionality (which I think we do)

This seems possible. I've spotted some places we could have tried a parameteric dispatch e.g. have a method where the $\epsilon_t$ field of IID being a concrete Normal type dispatches to using MvNormal

seabbs · 2024-12-11T17:41:32Z

**My only blocker to merging is that I'm not sure that #440 is addressed, which was part of the plan (cf #441). I don't think thats a major lift (it might already be addressed but I'm not sure). Otherwise, just some comments that look like future issues.
**

So my understanding was that this PR blocked #441 but wasn't trying to address #440 (hence not doing so). If you agree I think we should widen the scope of #440 to all parameters of this kind ot make sure they are in the order people might expect.

SamuelBrand1 · 2024-12-12T11:03:05Z

**My only blocker to merging is that I'm not sure that #440 is addressed, which was part of the plan (cf #441). I don't think thats a major lift (it might already be addressed but I'm not sure). Otherwise, just some comments that look like future issues.
**

So my understanding was that this PR blocked #441 but wasn't trying to address #440 (hence not doing so). If you agree I think we should widen the scope of #440 to all parameters of this kind ot make sure they are in the order people might expect.

I'm happy with this sequence.

SamuelBrand1

Looking really good. I'm happy to sequence addressing #440 in #441 rather than here.

seabbs mentioned this pull request Aug 28, 2024

Change ARStep to reverse AR parameters #441

Closed

seabbs force-pushed the issue408 branch from 42946f4 to 939ca74 Compare October 11, 2024 10:33

seabbs force-pushed the issue408 branch from 0fee831 to 3e2a471 Compare October 11, 2024 15:16

seabbs and others added 15 commits October 24, 2024 12:05

draft MA method

d4172aa

draft MA methd

d0fd36d

use IDD for epsilon in all models

34b743f

add MA benchmark

e8c4cbb

Add docs and tests for IDD

7c1828a

make episilon_t a arg of the latent model constructors

24ebd74

improve MA correctness

6a9905f

fully import EpiAwareUtils

150fddb

add a test for IDD

6a5c2aa

add tests for MA.jl

5cba2fa

add doc tests and unit tests + start on helper fn

c7812c1

more updatres to AR appraoc

db2a5cf

chase down partial arg changes

0c49fd0

clean up AR

9f09ace

clean up and add arma and arima helpers

07dd19f

seabbs force-pushed the issue408 branch from 7f3e398 to 07dd19f Compare October 24, 2024 11:05

seabbs added 2 commits October 24, 2024 17:39

Merge branch 'main' into issue408

d442dd5

Merge branch 'main' into issue408

54693fc

SamuelBrand1 mentioned this pull request Dec 4, 2024

Contributions towards Arma/Arima models #531

Merged

SamuelBrand1 and others added 2 commits December 5, 2024 11:01

Merge branch 'main' into issue408

4a83ba7

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seabbs added 4 commits December 10, 2024 11:50

add a compile time branch for HN

3d4d8f9

add a compile time branch for HN

2de9584

update test

2be792b

add a new constructor to get old default behaviour

d4023db

seabbs enabled auto-merge December 10, 2024 11:58

update docs

fdecd83

seabbs and others added 3 commits December 10, 2024 12:38

update docs - using the structs for priors is very brittle

c6b3e98

reorder prior plots

651bd02

Merge branch 'issue408' of https://github.com/CDCgov/Rt-without-renewal…

bb2b774

… into issue408

SamuelBrand1 requested changes Dec 11, 2024

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EpiAware/src/EpiLatentModels/models/HierarchicalNormal.jl Show resolved Hide resolved

seabbs requested a review from SamuelBrand1 December 11, 2024 17:41

SamuelBrand1 approved these changes Dec 12, 2024

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This was referenced Dec 12, 2024

Latent parameters in reverse order #440

Open

Add AR, MA, ARMA, and ARIMA methods specified by indexes #542

Closed

Add constants for common defaults such as IID(Normal()) #543

Open

Add specialised IID Normal methods for MvNormal #544

Closed

seabbs disabled auto-merge December 12, 2024 11:15

seabbs merged commit d7b7b3b into main Dec 12, 2024
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seabbs deleted the issue408 branch December 12, 2024 11:15

This was referenced Dec 12, 2024

User friendly constructor for AR(I)MA models #545

Open

Try parametric dispatch for performance optimisations #546

Open

Issue 408: ARMA and ARIMA models #438

Issue 408: ARMA and ARIMA models #438

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seabbs commented Aug 27, 2024 • edited Loading

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Try this Pull Request!

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Judge result

Benchmark Report for /home/runner/work/Rt-without-renewal/Rt-without-renewal

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Benchmark Report for /home/runner/work/Rt-without-renewal/Rt-without-renewal

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Benchmark Report for /home/runner/work/Rt-without-renewal/Rt-without-renewal

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seabbs commented Aug 27, 2024 •

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