From 6ff3027e298504975fdf265a3d4b8378b4fee016 Mon Sep 17 00:00:00 2001
From: Douglas Bates <dmbates@gmail.com>
Date: Wed, 7 Jul 2021 18:07:43 -0500
Subject: [PATCH 01/23] Add a progress meter for LMM fits

---
 Project.toml            | 2 +-
 src/linearmixedmodel.jl | 7 ++++++-
 2 files changed, 7 insertions(+), 2 deletions(-)

diff --git a/Project.toml b/Project.toml
index 49ef70bb9..a5f65a451 100644
--- a/Project.toml
+++ b/Project.toml
@@ -34,7 +34,7 @@ JSON3 = "1"
 LazyArtifacts = "1"
 NLopt = "0.5, 0.6"
 PooledArrays = "0.5, 1"
-ProgressMeter = "1.5"
+ProgressMeter = "1.5, 1.6, 1.7"
 StaticArrays = "0.11, 0.12, 1"
 StatsBase = "0.31, 0.32, 0.33"
 StatsFuns = "0.8, 0.9"
diff --git a/src/linearmixedmodel.jl b/src/linearmixedmodel.jl
index 4df60c5b3..391e2815a 100644
--- a/src/linearmixedmodel.jl
+++ b/src/linearmixedmodel.jl
@@ -397,15 +397,20 @@ function fit!(m::LinearMixedModel{T}; verbose::Bool = false, REML::Bool = false)
     end
     opt = Opt(optsum)
     optsum.REML = REML
+    prog = ProgressUnknown(; showspeed=true)
+    iter = 1
     function obj(x, g)
         isempty(g) || throw(ArgumentError("g should be empty for this objective"))
         val = objective(updateL!(setθ!(m, x)))
+        ProgressMeter.next!(prog; showvalues = [(:objective, val),])
+        iter += 1
         verbose && println(round(val, digits = 5), " ", x)
         val
-    end
+    end    
     NLopt.min_objective!(opt, obj)
     optsum.finitial = obj(optsum.initial, T[])
     fmin, xmin, ret = NLopt.optimize!(opt, copyto!(optsum.final, optsum.initial))
+    ProgressMeter.finish!(prog)
     ## check if small non-negative parameter values can be set to zero
     xmin_ = copy(xmin)
     lb = optsum.lowerbd

From dbf1c09f8f670006a70083d9b538ae219c8ce2f1 Mon Sep 17 00:00:00 2001
From: Douglas Bates <dmbates@gmail.com>
Date: Wed, 7 Jul 2021 18:21:45 -0500
Subject: [PATCH 02/23] Remove unnecessary iteration counter

---
 src/linearmixedmodel.jl | 4 +---
 1 file changed, 1 insertion(+), 3 deletions(-)

diff --git a/src/linearmixedmodel.jl b/src/linearmixedmodel.jl
index 391e2815a..155986783 100644
--- a/src/linearmixedmodel.jl
+++ b/src/linearmixedmodel.jl
@@ -398,15 +398,13 @@ function fit!(m::LinearMixedModel{T}; verbose::Bool = false, REML::Bool = false)
     opt = Opt(optsum)
     optsum.REML = REML
     prog = ProgressUnknown(; showspeed=true)
-    iter = 1
     function obj(x, g)
         isempty(g) || throw(ArgumentError("g should be empty for this objective"))
         val = objective(updateL!(setθ!(m, x)))
         ProgressMeter.next!(prog; showvalues = [(:objective, val),])
-        iter += 1
         verbose && println(round(val, digits = 5), " ", x)
         val
-    end    
+    end
     NLopt.min_objective!(opt, obj)
     optsum.finitial = obj(optsum.initial, T[])
     fmin, xmin, ret = NLopt.optimize!(opt, copyto!(optsum.final, optsum.initial))

From 8e38b1772bcb64c468fbbe0d544d4ccd8350f3d1 Mon Sep 17 00:00:00 2001
From: Phillip Alday <me@phillipalday.com>
Date: Thu, 8 Jul 2021 10:00:15 -0500
Subject: [PATCH 03/23] require at least julia 1.6

---
 .github/workflows/Tier1.yml | 2 +-
 NEWS.md                     | 4 ++++
 Project.toml                | 2 +-
 README.md                   | 8 ++++----
 4 files changed, 10 insertions(+), 6 deletions(-)

diff --git a/.github/workflows/Tier1.yml b/.github/workflows/Tier1.yml
index 1b07f523c..0554fd5d6 100644
--- a/.github/workflows/Tier1.yml
+++ b/.github/workflows/Tier1.yml
@@ -19,7 +19,7 @@ jobs:
     runs-on: ${{ matrix.os }}
     strategy:
       matrix:
-        julia-version: [1.4, 1.6]
+        julia-version: [1.6]
         julia-arch: [x64]
         os: [ubuntu-18.04, macos-10.15, windows-2019]
     steps:
diff --git a/NEWS.md b/NEWS.md
index 680fe0696..31ed9161f 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -24,6 +24,9 @@ MixedModels v4.0.0 Release Notes
 * `saveoptsum` and `restoreoptsum!` provide for saving and restoring the `optsum`
   field of a `LinearMixedModel` as a JSON file, allowing for recreating a model fit
   that may take a long time for parameter optimization. [#506]
+* Verbose output now uses `ProgressMeter`, which gives useful information about the timing
+  of each iteration and does not flood stdio. [#539]
+* Support for Julia < 1.6 has been dropped. [#539]
 
 Run-time formula syntax
 -----------------------
@@ -257,3 +260,4 @@ Package dependencies
 [#524]: https://github.com/JuliaStats/MixedModels.jl/issues/524
 [#536]: https://github.com/JuliaStats/MixedModels.jl/issues/536
 [#537]: https://github.com/JuliaStats/MixedModels.jl/issues/537
+[#539]: https://github.com/JuliaStats/MixedModels.jl/issues/539
diff --git a/Project.toml b/Project.toml
index a5f65a451..fdd50cba8 100644
--- a/Project.toml
+++ b/Project.toml
@@ -41,7 +41,7 @@ StatsFuns = "0.8, 0.9"
 StatsModels = "0.6.23"
 StructTypes = "1"
 Tables = "1"
-julia = "1.4"
+julia = "1.6"
 
 [extras]
 DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
diff --git a/README.md b/README.md
index 13f9e3aa5..ec4de3ce1 100644
--- a/README.md
+++ b/README.md
@@ -49,11 +49,11 @@ Typical distribution forms are _Bernoulli_ for binary data or _Poisson_ for coun
 
 |OS|OS Version|Arch|Julia|Tier|
 |:-:|:-:|:-:|:-:|:-:|
-|Linux|Ubuntu 18.04|x64|v1.4, 1.5, 1.6|1|
-|macOS|Catalina 10.15|x64|v1.4, 1.5, 1.6|1|
-|Windows|Server 2019|x64|v1.4, 1.5, 1.6|1|
+|Linux|Ubuntu 18.04|x64|v1.6|1|
+|macOS|Catalina 10.15|x64v1.6|1|
+|Windows|Server 2019|x64|v1.6|1|
 
-Upon release of the next Julia LTS, Tier 1 will become Tier 2 and Julia LTS will become Tier 1.
+Note that previous releases still support older Julia versions.
 
 ## Version 3.0.0
 

From e8d0030189274267b7aaf677826c2f451beba319 Mon Sep 17 00:00:00 2001
From: Douglas Bates <dmbates@gmail.com>
Date: Thu, 8 Jul 2021 12:28:04 -0500
Subject: [PATCH 04/23] Add progress meter for GLMMs.  Change format.

---
 src/generalizedlinearmixedmodel.jl | 6 +++++-
 src/linearmixedmodel.jl            | 5 ++---
 2 files changed, 7 insertions(+), 4 deletions(-)

diff --git a/src/generalizedlinearmixedmodel.jl b/src/generalizedlinearmixedmodel.jl
index 6f781f65b..6af1659e9 100644
--- a/src/generalizedlinearmixedmodel.jl
+++ b/src/generalizedlinearmixedmodel.jl
@@ -241,7 +241,7 @@ fit(
 )
 
 """
-    fit!(m::GeneralizedLinearMixedModel[, verbose = false, fast = false, nAGQ = 1])
+    fit!(m::GeneralizedLinearMixedModel[, verbose=false, fast=false, nAGQ=1, progress=true])
 
 Optimize the objective function for `m`.
 
@@ -254,6 +254,7 @@ function fit!(
     verbose::Bool = false,
     fast::Bool = false,
     nAGQ::Integer = 1,
+    progress::Bool = true,
 ) where {T}
     β = m.β
     lm = m.LMM
@@ -269,16 +270,19 @@ function fit!(
         optsum.final = copy(optsum.initial)
     end
     setpar! = fast ? setθ! : setβθ!
+    prog = ProgressUnknown("Minimizing"; showspeed=true)
     function obj(x, g)
         isempty(g) || throw(ArgumentError("g should be empty for this objective"))
         val = deviance(pirls!(setpar!(m, x), fast, verbose), nAGQ)
         verbose && println(round(val, digits = 5), " ", x)
+        progress && ProgressMeter.next!(prog; showvalues = [(:objective, val),])
         val
     end
     opt = Opt(optsum)
     NLopt.min_objective!(opt, obj)
     optsum.finitial = obj(optsum.initial, T[])
     fmin, xmin, ret = NLopt.optimize(opt, copyto!(optsum.final, optsum.initial))
+    ProgressMeter.finish!(prog)
     ## check if very small parameter values bounded below by zero can be set to zero
     xmin_ = copy(xmin)
     for i in eachindex(xmin_)
diff --git a/src/linearmixedmodel.jl b/src/linearmixedmodel.jl
index 155986783..b4e5d4a4b 100644
--- a/src/linearmixedmodel.jl
+++ b/src/linearmixedmodel.jl
@@ -397,12 +397,11 @@ function fit!(m::LinearMixedModel{T}; verbose::Bool = false, REML::Bool = false)
     end
     opt = Opt(optsum)
     optsum.REML = REML
-    prog = ProgressUnknown(; showspeed=true)
+    prog = ProgressUnknown("Minimizing"; showspeed=true)
     function obj(x, g)
         isempty(g) || throw(ArgumentError("g should be empty for this objective"))
         val = objective(updateL!(setθ!(m, x)))
-        ProgressMeter.next!(prog; showvalues = [(:objective, val),])
-        verbose && println(round(val, digits = 5), " ", x)
+        verbose && ProgressMeter.next!(prog; showvalues = [(:objective, val),])
         val
     end
     NLopt.min_objective!(opt, obj)

From cbd69083e26f988011a84dbf7e715fc091bb88e3 Mon Sep 17 00:00:00 2001
From: Douglas Bates <dmbates@gmail.com>
Date: Fri, 9 Jul 2021 11:42:27 -0500
Subject: [PATCH 05/23] Rename verbose to progress, change default to true

---
 src/generalizedlinearmixedmodel.jl |  4 ++++
 src/linearmixedmodel.jl            | 27 ++++++++++++++-------------
 2 files changed, 18 insertions(+), 13 deletions(-)

diff --git a/src/generalizedlinearmixedmodel.jl b/src/generalizedlinearmixedmodel.jl
index 6af1659e9..86a06aa1a 100644
--- a/src/generalizedlinearmixedmodel.jl
+++ b/src/generalizedlinearmixedmodel.jl
@@ -248,6 +248,10 @@ Optimize the objective function for `m`.
 When `fast` is `true` a potentially much faster but slightly less accurate algorithm, in
 which `pirls!` optimizes both the random effects and the fixed-effects parameters,
 is used.
+
+if `progress` is `true`, the default, a `ProgressMeter.ProgressUnknown` counter is displayed
+during the iterations to minimize the deviance.  There is a delay before this display is initialized
+and it may not be shown at all for models that are optimized quickly.
 """
 function fit!(
     m::GeneralizedLinearMixedModel{T};
diff --git a/src/linearmixedmodel.jl b/src/linearmixedmodel.jl
index b4e5d4a4b..7182f8a60 100644
--- a/src/linearmixedmodel.jl
+++ b/src/linearmixedmodel.jl
@@ -166,7 +166,7 @@ fit(
     tbl;
     wts = [],
     contrasts = Dict{Symbol,Any}(),
-    verbose::Bool = false,
+    progress::Bool = true,
     REML::Bool = false,
 ) = fit(
     LinearMixedModel,
@@ -174,7 +174,7 @@ fit(
     Tables.columntable(tbl),
     wts = wts,
     contrasts = contrasts,
-    verbose = verbose,
+    progress = progress,
     REML = REML,
 )
 
@@ -184,11 +184,11 @@ fit(
     tbl::Tables.ColumnTable;
     wts = wts,
     contrasts = contrasts,
-    verbose = verbose,
+    progress = progress,
     REML = REML,
 ) = fit!(
     LinearMixedModel(f, tbl, contrasts = contrasts, wts = wts),
-    verbose = verbose,
+    progress = progress,
     REML = REML,
 )
 
@@ -201,7 +201,7 @@ fit(
     tbl;
     wts = [],
     contrasts = Dict{Symbol,Any}(),
-    verbose::Bool = false,
+    progress::Bool = true,
     REML::Bool = false,
     offset = [],
 ) = !isempty(offset) ? _offseterr() : fit(
@@ -210,7 +210,7 @@ fit(
     tbl,
     wts = wts,
     contrasts = contrasts,
-    verbose = verbose,
+    progress = progress,
     REML = REML,
 )
 
@@ -222,7 +222,7 @@ fit(
     l::IdentityLink;
     wts = [],
     contrasts = Dict{Symbol,Any}(),
-    verbose::Bool = false,
+    progress::Bool = true,
     REML::Bool = false,
     offset = [],
     fast::Bool = false,
@@ -233,7 +233,7 @@ fit(
     tbl,
     wts = wts,
     contrasts = contrasts,
-    verbose = verbose,
+    progress = progress,
     REML = REML,
 )
 
@@ -384,12 +384,13 @@ function feL(m::LinearMixedModel)
 end
 
 """
-    fit!(m::LinearMixedModel[; verbose::Bool=false, REML::Bool=false])
+    fit!(m::LinearMixedModel[; progress::Bool=true, REML::Bool=false])
 
-Optimize the objective of a `LinearMixedModel`.  When `verbose` is `true` the values of the
-objective and the parameters are printed on stdout at each function evaluation.
+Optimize the objective of a `LinearMixedModel`.  When `progress` is `true` a 
+`ProgressMeter.ProgressUnknown` display is shown during the optimization of the
+objective, if the optimization takes more than one second or so.
 """
-function fit!(m::LinearMixedModel{T}; verbose::Bool = false, REML::Bool = false) where {T}
+function fit!(m::LinearMixedModel{T}; progress::Bool=true, REML::Bool=false) where {T}
     optsum = m.optsum
     # this doesn't matter for LMM, but it does for GLMM, so let's be consistent
     if optsum.feval > 0
@@ -401,7 +402,7 @@ function fit!(m::LinearMixedModel{T}; verbose::Bool = false, REML::Bool = false)
     function obj(x, g)
         isempty(g) || throw(ArgumentError("g should be empty for this objective"))
         val = objective(updateL!(setθ!(m, x)))
-        verbose && ProgressMeter.next!(prog; showvalues = [(:objective, val),])
+        progress && ProgressMeter.next!(prog; showvalues = [(:objective, val),])
         val
     end
     NLopt.min_objective!(opt, obj)

From 5696c9169dc19321fe574df22212393db0d69ed3 Mon Sep 17 00:00:00 2001
From: Douglas Bates <dmbates@gmail.com>
Date: Sat, 10 Jul 2021 10:31:48 -0500
Subject: [PATCH 06/23] Expand on NEWS entry for #539

---
 NEWS.md | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/NEWS.md b/NEWS.md
index 31ed9161f..20bb42ccb 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -25,7 +25,8 @@ MixedModels v4.0.0 Release Notes
   field of a `LinearMixedModel` as a JSON file, allowing for recreating a model fit
   that may take a long time for parameter optimization. [#506]
 * Verbose output now uses `ProgressMeter`, which gives useful information about the timing
-  of each iteration and does not flood stdio. [#539]
+  of each iteration and does not flood stdio. The `verbose` argument has been renamed `progress`
+  and the default changed to `true`. [#539]
 * Support for Julia < 1.6 has been dropped. [#539]
 
 Run-time formula syntax

From 8c3ddffa06b5bd5ba87d27342375d5dfedc3aac9 Mon Sep 17 00:00:00 2001
From: Douglas Bates <dmbates@gmail.com>
Date: Sat, 10 Jul 2021 10:42:03 -0500
Subject: [PATCH 07/23] Back-out changes to compat for ProgressMeter

---
 Project.toml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/Project.toml b/Project.toml
index fdd50cba8..7287a723d 100644
--- a/Project.toml
+++ b/Project.toml
@@ -34,7 +34,7 @@ JSON3 = "1"
 LazyArtifacts = "1"
 NLopt = "0.5, 0.6"
 PooledArrays = "0.5, 1"
-ProgressMeter = "1.5, 1.6, 1.7"
+ProgressMeter = "1.5"
 StaticArrays = "0.11, 0.12, 1"
 StatsBase = "0.31, 0.32, 0.33"
 StatsFuns = "0.8, 0.9"

From b002ad205f3934aee8513df5669b7ee265b11249 Mon Sep 17 00:00:00 2001
From: Douglas Bates <dmbates@gmail.com>
Date: Mon, 12 Jul 2021 09:03:25 -0500
Subject: [PATCH 08/23] Update src/generalizedlinearmixedmodel.jl

Co-authored-by: Phillip Alday <palday@users.noreply.github.com>
---
 src/generalizedlinearmixedmodel.jl | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/generalizedlinearmixedmodel.jl b/src/generalizedlinearmixedmodel.jl
index 86a06aa1a..5121cdaea 100644
--- a/src/generalizedlinearmixedmodel.jl
+++ b/src/generalizedlinearmixedmodel.jl
@@ -249,7 +249,7 @@ When `fast` is `true` a potentially much faster but slightly less accurate algor
 which `pirls!` optimizes both the random effects and the fixed-effects parameters,
 is used.
 
-if `progress` is `true`, the default, a `ProgressMeter.ProgressUnknown` counter is displayed
+If `progress` is `true`, the default, a `ProgressMeter.ProgressUnknown` counter is displayed.
 during the iterations to minimize the deviance.  There is a delay before this display is initialized
 and it may not be shown at all for models that are optimized quickly.
 """

From 4c1adcf3a2203ee147aef6d58f581c7a1efb81ab Mon Sep 17 00:00:00 2001
From: Douglas Bates <dmbates@gmail.com>
Date: Mon, 12 Jul 2021 09:03:41 -0500
Subject: [PATCH 09/23] Update src/generalizedlinearmixedmodel.jl

Co-authored-by: Phillip Alday <palday@users.noreply.github.com>
---
 src/generalizedlinearmixedmodel.jl | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/src/generalizedlinearmixedmodel.jl b/src/generalizedlinearmixedmodel.jl
index 5121cdaea..edbc86744 100644
--- a/src/generalizedlinearmixedmodel.jl
+++ b/src/generalizedlinearmixedmodel.jl
@@ -252,6 +252,8 @@ is used.
 If `progress` is `true`, the default, a `ProgressMeter.ProgressUnknown` counter is displayed.
 during the iterations to minimize the deviance.  There is a delay before this display is initialized
 and it may not be shown at all for models that are optimized quickly.
+
+If `verbose` is `true`, then both the intermediate results of both the nonlinear optimization and PIRLS are also displayed on standard output.
 """
 function fit!(
     m::GeneralizedLinearMixedModel{T};

From 20807e7d81440b2edcfb67f0651d1074099a8c51 Mon Sep 17 00:00:00 2001
From: Douglas Bates <dmbates@gmail.com>
Date: Mon, 12 Jul 2021 12:12:29 -0500
Subject: [PATCH 10/23] Add progress arg to refit! with default of false.

---
 src/generalizedlinearmixedmodel.jl | 15 ++++++++++-----
 src/linearmixedmodel.jl            | 13 +++++++------
 2 files changed, 17 insertions(+), 11 deletions(-)

diff --git a/src/generalizedlinearmixedmodel.jl b/src/generalizedlinearmixedmodel.jl
index edbc86744..1831666d7 100644
--- a/src/generalizedlinearmixedmodel.jl
+++ b/src/generalizedlinearmixedmodel.jl
@@ -171,6 +171,7 @@ fit(
     verbose::Bool = false,
     fast::Bool = false,
     nAGQ::Integer = 1,
+    progress::Bool = true,
 ) = fit(
     GeneralizedLinearMixedModel,
     f,
@@ -183,6 +184,7 @@ fit(
     verbose = verbose,
     fast = fast,
     nAGQ = nAGQ,
+    progress = progress,
 )
 
 fit(
@@ -197,6 +199,7 @@ fit(
     verbose::Bool = false,
     fast::Bool = false,
     nAGQ::Integer = 1,
+    progress::Bool = true,
 ) = fit!(
     GeneralizedLinearMixedModel(
         f,
@@ -210,6 +213,7 @@ fit(
     verbose = verbose,
     fast = fast,
     nAGQ = nAGQ,
+    progress = progress,
 )
 
 
@@ -226,6 +230,7 @@ fit(
     REML::Bool = false,
     fast::Bool = false,
     nAGQ::Integer = 1,
+    progress::Bool = true,
 ) = fit(
     GeneralizedLinearMixedModel,
     f,
@@ -238,6 +243,7 @@ fit(
     verbose = verbose,
     fast = fast,
     nAGQ = nAGQ,
+    progress = progress,
 )
 
 """
@@ -583,21 +589,20 @@ If not specified, the `fast` and `nAGQ` options from the previous fit are used.
 """
 function refit!(m::GeneralizedLinearMixedModel{T};
                 fast::Bool = (length(m.θ) == length(m.optsum.final)),
-                nAGQ::Integer = m.optsum.nAGQ)  where T
+                nAGQ::Integer = m.optsum.nAGQ, progress::Bool=false)  where T
 
-    fit!(unfit!(m); fast=fast, nAGQ=nAGQ)
+    fit!(unfit!(m); fast=fast, nAGQ=nAGQ, progress=progress)
 end
 
 function refit!(m::GeneralizedLinearMixedModel{T}, y;
                 fast::Bool = (length(m.θ) == length(m.optsum.final)),
-                nAGQ::Integer = m.optsum.nAGQ) where T
+                nAGQ::Integer = m.optsum.nAGQ, progress::Bool=false) where T
     m_resp_y = m.resp.y
     length(y) == size(m_resp_y, 1) || throw(DimensionMismatch(""))
     copyto!(m_resp_y, y)
-    refit!(m)
+    refit!(m; progress=progress)
 end
 
-
 """
     setβθ!(m::GeneralizedLinearMixedModel, v)
 
diff --git a/src/linearmixedmodel.jl b/src/linearmixedmodel.jl
index 7182f8a60..f718bc34c 100644
--- a/src/linearmixedmodel.jl
+++ b/src/linearmixedmodel.jl
@@ -775,21 +775,22 @@ function reevaluateAend!(m::LinearMixedModel)
 end
 
 """
-    refit!(m::LinearMixedModel[, y::Vector]; REML=m.optsum.REML)
+    refit!(m::LinearMixedModel[, y::Vector]; REML=m.optsum.REML, progress::Bool=false)
 
 Refit the model `m` after installing response `y`.
 
-If `y` is omitted the current response vector is used.
+If `y` is omitted the current response vector is used.  Note that the default for
+`progress` is `false` on refits (whereas it is `true` in the `fit!` methods.)
 """
-function refit!(m::LinearMixedModel; REML=m.optsum.REML)
-    fit!(unfit!(m); REML=REML)
+function refit!(m::LinearMixedModel; REML=m.optsum.REML, progress::Bool=false)
+    fit!(unfit!(m); REML=REML, progress=progress)
 end
 
-function refit!(m::LinearMixedModel, y; REML=m.optsum.REML)
+function refit!(m::LinearMixedModel, y; REML=m.optsum.REML, progress::Bool=false)
     resp = m.y
     length(y) == length(resp) || throw(DimensionMismatch(""))
     copyto!(resp, y)
-    refit!(m; REML=REML)
+    refit!(m; REML=REML, progress=progress)
 end
 
 """

From 5a311178deb795ae93149058174e0908ef576975 Mon Sep 17 00:00:00 2001
From: Douglas Bates <dmbates@gmail.com>
Date: Mon, 12 Jul 2021 12:20:05 -0500
Subject: [PATCH 11/23] progress=false in fits; model_response -> response

---
 test/FactorReTerm.jl | 16 ++++++++--------
 test/bootstrap.jl    |  6 +++---
 test/fit.jl          |  8 ++++----
 test/grouping.jl     |  2 +-
 test/linalg.jl       |  6 +++---
 test/matrixterm.jl   |  2 +-
 test/modelcache.jl   |  4 ++--
 test/pirls.jl        | 18 +++++++++---------
 test/pls.jl          | 12 ++++++------
 9 files changed, 37 insertions(+), 37 deletions(-)

diff --git a/test/FactorReTerm.jl b/test/FactorReTerm.jl
index 68e83e0bf..d42042de0 100644
--- a/test/FactorReTerm.jl
+++ b/test/FactorReTerm.jl
@@ -117,8 +117,8 @@ end
         # equality of RE terms not defined so check that they generate same modelcols
         @test modelcols(ff1.rhs[end], slp) == modelcols(ff2.rhs[end], slp)
         
-        m1 = fit(LMM, f1, slp)
-        m2 = fit(LMM, f2, slp)
+        m1 = fit(LMM, f1, slp; progress=false)
+        m2 = fit(LMM, f2, slp; progress=false)
         @test all(m1.λ .== m2.λ)
 
         @test StatsModels.terms(f2.rhs[end]) == [one, d, s]
@@ -140,8 +140,8 @@ end
         # test that zerocorr actually worked
         @test mc1.inds == mc2.inds == [1, 4]
         
-        m1 = fit(LMM, f1, slp)
-        m2 = fit(LMM, f2, slp)
+        m1 = fit(LMM, f1, slp; progress=false)
+        m2 = fit(LMM, f2, slp; progress=false)
         @test all(m1.λ .== m2.λ)
 
         @test StatsModels.terms(f2.rhs[end]) == [one, d, s]
@@ -263,10 +263,10 @@ end
 
         # fitted model to test amalgamate and fnames, and equivalence with other formulations
         psts = dataset("pastes")
-        m = fit(MixedModel, @formula(strength ~ 1 + (1|batch/cask)), psts)
-        m2 = fit(MixedModel, @formula(strength ~ 1 + (1|batch) + (1|batch&cask)), psts)
-        mr = fit(MixedModel, term(:strength) ~ term(1) + (term(1)|term(:batch)/term(:cask)), psts)
-        m2r = fit(MixedModel, term(:strength) ~ term(1) + (term(1)|term(:batch)) + (term(1)|term(:batch)&term(:cask)), psts)
+        m = fit(MixedModel, @formula(strength ~ 1 + (1|batch/cask)), psts; progress=false)
+        m2 = fit(MixedModel, @formula(strength ~ 1 + (1|batch) + (1|batch&cask)), psts; progress=false)
+        mr = fit(MixedModel, term(:strength) ~ term(1) + (term(1)|term(:batch)/term(:cask)), psts; progress=false)
+        m2r = fit(MixedModel, term(:strength) ~ term(1) + (term(1)|term(:batch)) + (term(1)|term(:batch)&term(:cask)), psts; progress=false)
 
         @test fnames(m) == fnames(m2) == fnames(mr) == fnames(m2r) == (Symbol("batch & cask"), :batch)
         @test m.λ == m2.λ == mr.λ == m2r.λ
diff --git a/test/bootstrap.jl b/test/bootstrap.jl
index 3b3d629ff..e9aa3302e 100644
--- a/test/bootstrap.jl
+++ b/test/bootstrap.jl
@@ -39,14 +39,14 @@ include("modelcache.jl")
         center(v::AbstractVector) = v .- (sum(v) / length(v))
         grouseticks = DataFrame(dataset(:grouseticks))
         grouseticks.ch = center(grouseticks.height)
-        gm4 = fit(MixedModel, only(gfms[:grouseticks]), grouseticks, Poisson(), fast=true)  # fails in pirls! with fast=false
+        gm4 = fit(MixedModel, only(gfms[:grouseticks]), grouseticks, Poisson(), fast=true, progress=false)  # fails in pirls! with fast=false
         gm4sim = refit!(simulate!(StableRNG(42), deepcopy(gm4)))
         @test isapprox(gm4.β, gm4sim.β; atol=norm(stderror(gm4)))
     end
 
     @testset "Binomial" begin
         cbpp = dataset(:cbpp)
-        gm2 = fit(MixedModel, first(gfms[:cbpp]), cbpp, Binomial(), wts=float(cbpp.hsz))
+        gm2 = fit(MixedModel, first(gfms[:cbpp]), cbpp, Binomial(), wts=float(cbpp.hsz), progress=false)
         gm2sim = refit!(simulate!(StableRNG(42), deepcopy(gm2)), fast=true)
         @test isapprox(gm2.β, gm2sim.β; atol=norm(stderror(gm2)))
     end
@@ -102,7 +102,7 @@ end
         contra = dataset(:contra)
         # need a model with fast=false to test that we only
         # copy the optimizer constraints for θ and not β
-        gm0 = fit(MixedModel, only(gfms[:contra]), contra, Bernoulli(), fast=false)
+        gm0 = fit(MixedModel, only(gfms[:contra]), contra, Bernoulli(), fast=false, progress=false)
         bs = parametricbootstrap(StableRNG(42), 100, gm0)
         # make sure we're not copying
         @test length(bs.lowerbd) == length(gm0.θ)
diff --git a/test/fit.jl b/test/fit.jl
index d9b7d5874..622fa506d 100644
--- a/test/fit.jl
+++ b/test/fit.jl
@@ -1,18 +1,18 @@
 using MixedModels, Test
 
 @testset "linear" begin
-    m1 = fit(MixedModel, @formula(yield ~ 1 + (1|batch)), MixedModels.dataset(:dyestuff))
+    m1 = fit(MixedModel, @formula(yield ~ 1 + (1|batch)), MixedModels.dataset(:dyestuff); progress=false)
     @test first(m1.θ) ≈ 0.7525806757718846 rtol=1.0e-5
 end
 
 @testset "generalized" begin
     gm1 = fit(MixedModel, @formula(use ~ 1 + urban + livch + age + abs2(age) + (1|dist)),
-              MixedModels.dataset(:contra), Bernoulli())
+              MixedModels.dataset(:contra), Bernoulli(); progress=false)
     @test deviance(gm1) ≈ 2372.7286 atol=1.0e-3
 end
 
 @testset "Normal-IdentityLink" begin
-    @test isa(fit(MixedModel, @formula(yield ~ 1 + (1|batch)), MixedModels.dataset(:dyestuff), Normal()),
+    @test isa(fit(MixedModel, @formula(yield ~ 1 + (1|batch)), MixedModels.dataset(:dyestuff), Normal(); progress=false),
               LinearMixedModel)
     @test_throws(ArgumentError("use LinearMixedModel for Normal distribution with IdentityLink"),
                  fit(GeneralizedLinearMixedModel,
@@ -22,6 +22,6 @@ end
 
 @testset "Normal Distribution GLMM" begin
     @test isa(fit(MixedModel, @formula(yield ~ 1 + (1|batch)), MixedModels.dataset(:dyestuff),
-                         Normal(), SqrtLink()),
+                         Normal(), SqrtLink(); progress=false),
                      GeneralizedLinearMixedModel)
 end
diff --git a/test/grouping.jl b/test/grouping.jl
index 6bc31d5a0..a6b53f9aa 100644
--- a/test/grouping.jl
+++ b/test/grouping.jl
@@ -23,5 +23,5 @@ end
     @test all(t.contrasts.levels[i] == lev for (i,lev) in enumerate(levs))
 
     # @test_throws OutOfMemoryError fit(MixedModel, @formula(y ~ 1 + (1 | grp)), d)
-    @test fit(MixedModel, @formula(y ~ 1 + (1 | grp)), d, contrasts=Dict(:grp => Grouping())) isa LinearMixedModel
+    @test fit(MixedModel, @formula(y ~ 1 + (1 | grp)), d, contrasts=Dict(:grp => Grouping()), progress=false) isa LinearMixedModel
 end
diff --git a/test/linalg.jl b/test/linalg.jl
index d62608857..26b2c79f0 100644
--- a/test/linalg.jl
+++ b/test/linalg.jl
@@ -45,8 +45,8 @@ end
         G = repeat(PooledArray(string.('A':'T')), inner = 2, outer=10),
         H = repeat(PooledArray(string.('a':'j')), inner=40),
         )
-    m1 = fit(MixedModel, @formula(Y ~ 1 + A + (1+A|G) + (1+A|H)), df)
-    wm1 = fit(MixedModel, @formula(Y ~ 1+A+(1+A|G)+(1+A|H)), df, wts  = ones(400))
+    m1 = fit(MixedModel, @formula(Y ~ 1 + A + (1+A|G) + (1+A|H)), df; progress=false)
+    wm1 = fit(MixedModel, @formula(Y ~ 1+A+(1+A|G)+(1+A|H)), df, wts=ones(400), progress=false)
     @test loglikelihood(wm1) ≈ loglikelihood(m1)
     MixedModels.reweight!(wm1, ones(400))
     @test loglikelihood(refit!(wm1)) ≈ loglikelihood(m1)
@@ -93,7 +93,7 @@ end
         # an example in MixedModels.jl#123
         df = gendata(10000, 500)
         f = @formula(Y ~ (1 + X|H) + (1|G))
-        m500 = fit!(LinearMixedModel(f, df))
+        m500 = fit!(LinearMixedModel(f, df), progress=false)
         # the real test here isn't in the theta comparison but in that the fit
         # completes successfully
         @test length(m500.u) == 2
diff --git a/test/matrixterm.jl b/test/matrixterm.jl
index 2fe31dd02..6ac9ea24c 100644
--- a/test/matrixterm.jl
+++ b/test/matrixterm.jl
@@ -42,7 +42,7 @@ end
         @test typeof(X.x) <: SparseMatrixCSC
         @test X.rank == 28
         @test X.cnames == fe.cnames
-        m1 = fit!(LinearMixedModel(collect(m.y), X, deepcopy(m.reterms), m.formula))
+        m1 = fit!(LinearMixedModel(collect(m.y), X, deepcopy(m.reterms), m.formula); progress=false)
         @test isapprox(m1.θ, m.θ, rtol = 1.0e-5)
     end
 
diff --git a/test/modelcache.jl b/test/modelcache.jl
index 79fd0954e..5512bf860 100644
--- a/test/modelcache.jl
+++ b/test/modelcache.jl
@@ -38,13 +38,13 @@ using MixedModels: dataset
 
 # for some reason it seems necessary to prime the pump in julia-1.6.0-DEV
 @isdefined(fittedmodels) || const global fittedmodels = Dict{Symbol,Vector{MixedModel}}(
-    :dyestuff => [fit(MixedModel, only(fms[:dyestuff]), dataset(:dyestuff))]
+    :dyestuff => [fit(MixedModel, only(fms[:dyestuff]), dataset(:dyestuff); progress=false)]
 );
 
 @isdefined(allfms) || const global allfms = merge(fms, gfms)
 
 function models(nm::Symbol)
     get!(fittedmodels, nm) do
-        [fit(MixedModel, f, dataset(nm)) for f in allfms[nm]]
+        [fit(MixedModel, f, dataset(nm), progress=false) for f in allfms[nm]]
     end
 end
diff --git a/test/pirls.jl b/test/pirls.jl
index 7d929f795..a6e038b24 100644
--- a/test/pirls.jl
+++ b/test/pirls.jl
@@ -13,7 +13,7 @@ include("modelcache.jl")
 
 @testset "contra" begin
     contra = dataset(:contra)
-    gm0 = fit(MixedModel, only(gfms[:contra]), contra, Bernoulli(), fast=true)
+    gm0 = fit(MixedModel, only(gfms[:contra]), contra, Bernoulli(), fast=true, progress=false)
     @test gm0.lowerbd == zeros(1)
     @test isapprox(gm0.θ, [0.5720734451352923], atol=0.001)
     @test !issingular(gm0)
@@ -37,7 +37,7 @@ include("modelcache.jl")
     @test Distribution(gm0) == Distribution(gm0.resp)
     @test Link(gm0) == Link(gm0.resp)
 
-    gm1 = fit(MixedModel, only(gfms[:contra]), contra, Bernoulli());
+    gm1 = fit(MixedModel, only(gfms[:contra]), contra, Bernoulli(); progress=false);
     @test isapprox(gm1.θ, [0.573054], atol=0.005)
     @test lowerbd(gm1) == vcat(fill(-Inf, 7), 0.)
     @test isapprox(deviance(gm1), 2361.54575, rtol=0.00001)
@@ -47,7 +47,7 @@ include("modelcache.jl")
     @test nobs(gm0) == 1934
     refit!(gm0, fast=true, nAGQ=7)
     @test isapprox(deviance(gm0), 2360.9838, atol=0.001)
-    gm1 = fit(MixedModel, only(gfms[:contra]), contra, Bernoulli(), nAGQ=7)
+    gm1 = fit(MixedModel, only(gfms[:contra]), contra, Bernoulli(), nAGQ=7, progress=false)
     @test isapprox(deviance(gm1), 2360.8760, atol=0.001)
     @test gm1.β == gm1.beta
     @test gm1.θ == gm1.theta
@@ -55,7 +55,7 @@ include("modelcache.jl")
     @test length(gm1y) == size(gm1.X, 1)
     @test eltype(gm1y) == eltype(gm1.X)
     @test gm1y == (MixedModels.dataset(:contra).use .== "Y")
-    @test model_response(gm1) == gm1y
+    @test response(gm1) == gm1y
     @test !islinear(gm1)
     @test :θ in propertynames(gm0)
 
@@ -74,7 +74,7 @@ end
 
 @testset "cbpp" begin
     cbpp = dataset(:cbpp)
-    gm2 = fit(MixedModel, first(gfms[:cbpp]), cbpp, Binomial(), wts=float(cbpp.hsz))
+    gm2 = fit(MixedModel, first(gfms[:cbpp]), cbpp, Binomial(), wts=float(cbpp.hsz), progress=false)
     @test weights(gm2) == cbpp.hsz
     @test deviance(gm2,true) ≈ 100.09585619892968 atol=0.0001
     @test sum(abs2, gm2.u[1]) ≈ 9.723054788538546 atol=0.0001
@@ -102,7 +102,7 @@ end
 end
 
 @testset "verbagg" begin
-    gm3 = fit(MixedModel, only(gfms[:verbagg]), dataset(:verbagg), Bernoulli())
+    gm3 = fit(MixedModel, only(gfms[:verbagg]), dataset(:verbagg), Bernoulli(), progress=false)
     @test deviance(gm3) ≈ 8151.40 rtol=1e-5
     @test lowerbd(gm3) == vcat(fill(-Inf, 6), zeros(2))
     @test fitted(gm3) == predict(gm3)
@@ -115,7 +115,7 @@ end
     center(v::AbstractVector) = v .- (sum(v) / length(v))
     grouseticks = DataFrame(dataset(:grouseticks))
     grouseticks.ch = center(grouseticks.height)
-    gm4 = fit(MixedModel, only(gfms[:grouseticks]), grouseticks, Poisson(), fast=true)  # fails in pirls! with fast=false
+    gm4 = fit(MixedModel, only(gfms[:grouseticks]), grouseticks, Poisson(), fast=true, progress=false)  # fails in pirls! with fast=false
     @test isapprox(deviance(gm4), 851.4046, atol=0.001)
     # these two values are not well defined at the optimum
     #@test isapprox(sum(x -> sum(abs2, x), gm4.u), 196.8695297987013, atol=0.1)
@@ -152,7 +152,7 @@ end
     @test deviance(m1) ≈ 193.5587302384811 rtol=1.e-5
     @test only(m1.β) ≈ 4.192196439077657 atol=1.e-5
     @test only(m1.θ) ≈ 1.838245201739852 atol=1.e-5
-    m11 = fit(MixedModel, gform, goldstein, Poisson(), nAGQ=11)
+    m11 = fit(MixedModel, gform, goldstein, Poisson(), nAGQ=11, progress=false)
     @test deviance(m11) ≈ 193.51028088736842 rtol=1.e-5
     @test only(m11.β) ≈ 4.192196439077657 atol=1.e-5
     @test only(m11.θ) ≈ 1.838245201739852 atol=1.e-5
@@ -181,7 +181,7 @@ end
     # The offset of log.(expected) is to examine the ratio of observed to expected, based on population
     mmec = dataset(:mmec)
     mmform = @formula(deaths ~ 1 + uvb + (1|region))
-    gm5 = fit(MixedModel, mmform, mmec, Poisson(); offset=log.(mmec.expected), nAGQ=11)
+    gm5 = fit(MixedModel, mmform, mmec, Poisson(); offset=log.(mmec.expected), nAGQ=11, progress=false)
     @test isapprox(deviance(gm5), 655.2533533016059, atol=5.e-3)
     @test isapprox(first(gm5.θ), 0.4121684550775567, atol=1.e-3)
     @test isapprox(first(gm5.β), -0.13860166843315044, atol=1.e-3)
diff --git a/test/pls.jl b/test/pls.jl
index f59259654..8c6a7d6a4 100644
--- a/test/pls.jl
+++ b/test/pls.jl
@@ -90,7 +90,7 @@ end
     @test fm1.X == ones(30,1)
     ds = MixedModels.dataset(:dyestuff)
     @test fm1.y == ds[:yield]
-    @test model_response(fm1) == ds.yield
+    @test response(fm1) == ds.yield
     @test cond(fm1) == ones(1)
     @test first(leverage(fm1)) ≈ 0.15650534392640486 rtol=1.e-5
     @test sum(leverage(fm1)) ≈ 4.695160317792145 rtol=1.e-5
@@ -379,7 +379,7 @@ end
     @test ρ === -0.0   # test that systematic zero correlations are returned as -0.0
 
     MixedModels.likelihoodratiotest(fm, fmnc)
-    fmrs = fit(MixedModel, @formula(reaction ~ 1+days + (0+days|subj)), slp);
+    fmrs = fit(MixedModel, @formula(reaction ~ 1+days + (0+days|subj)), slp; progress=false);
     @test objective(fmrs) ≈ 1774.080315280528 rtol=0.00001
     @test fmrs.θ ≈ [0.24353985679033105] rtol=0.00001
 
@@ -394,7 +394,7 @@ end
 
     # combining [ReMat{T,S1}, ReMat{T,S2}] for S1 ≠ S2
     slpcat = (subj = slp.subj, days = PooledArray(string.(slp.days)), reaction = slp.reaction)
-    fm_cat = fit(MixedModel, @formula(reaction ~ 1+days+(1|subj)+(0+days|subj)),slpcat)
+    fm_cat = fit(MixedModel, @formula(reaction ~ 1+days+(1|subj)+(0+days|subj)),slpcat; progress=false)
     @test fm_cat isa LinearMixedModel
     σρ = fm_cat.σρs
     @test σρ isa NamedTuple
@@ -411,7 +411,7 @@ end
     @test all(ρs_intercept .=== -0.0)
 
     # also works without explicitly dropped intercept
-    fm_cat2 = fit(MixedModel, @formula(reaction ~ 1+days+(1|subj)+(days|subj)),slpcat)
+    fm_cat2 = fit(MixedModel, @formula(reaction ~ 1+days+(1|subj)+(days|subj)),slpcat; progress=false)
     @test fm_cat2 isa LinearMixedModel
     σρ = fm_cat2.σρs
     @test σρ isa NamedTuple
@@ -511,7 +511,7 @@ end
     rng = MersenneTwister(0);
     x = rand(rng, 100);
     data = (x = x, x2 = 1.5 .* x, y = rand(rng, 100), z = repeat('A':'T', 5))
-    model = fit(MixedModel, @formula(y ~ x + x2 + (1|z)), data)
+    model = fit(MixedModel, @formula(y ~ x + x2 + (1|z)), data; progress=false)
     @test length(fixef(model)) == 2
     @test rank(model) == 2
     @test length(coef(model)) == 3
@@ -546,7 +546,7 @@ end
     @test vcov(m1) ≈ [1.177035 -4.802598; -4.802598 24.664497] atol = 1.e-4
     =#
 
-    m2 = fit(MixedModel, @formula(a ~ 1 + b + (1|c)), data, wts = data.w1)
+    m2 = fit(MixedModel, @formula(a ~ 1 + b + (1|c)), data, wts = data.w1, progress=false)
     @test m2.θ ≈ [0.295181729258352]  atol = 1.e-4
     @test stderror(m2) ≈  [0.9640167, 3.6309696] atol = 1.e-4
     @test vcov(m2) ≈ [0.9293282 -2.557527; -2.5575267 13.183940] atol = 1.e-4

From c602e6787566902797bdfdcc497eee78e76c86f9 Mon Sep 17 00:00:00 2001
From: Douglas Bates <dmbates@gmail.com>
Date: Mon, 12 Jul 2021 12:31:08 -0500
Subject: [PATCH 12/23] Missed one `progress=false` change

---
 test/pirls.jl | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/test/pirls.jl b/test/pirls.jl
index a6e038b24..adea18d32 100644
--- a/test/pirls.jl
+++ b/test/pirls.jl
@@ -147,7 +147,7 @@ end
     gform = @formula(y ~ 1 + (1|group))
     m1 = GeneralizedLinearMixedModel(gform, goldstein, Poisson())
     @test !isfitted(m1)
-    fit!(m1)
+    fit!(m1; progress=false)
     @test isfitted(m1)
     @test deviance(m1) ≈ 193.5587302384811 rtol=1.e-5
     @test only(m1.β) ≈ 4.192196439077657 atol=1.e-5

From e15dd1c78cca9aa04354f0b54a9702b54dc1f658 Mon Sep 17 00:00:00 2001
From: Phillip Alday <me@phillipalday.com>
Date: Mon, 12 Jul 2021 12:53:28 -0500
Subject: [PATCH 13/23] ProgressMeter compat bump (needed for showspeed)

---
 Project.toml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/Project.toml b/Project.toml
index 7287a723d..b5176c889 100644
--- a/Project.toml
+++ b/Project.toml
@@ -34,7 +34,7 @@ JSON3 = "1"
 LazyArtifacts = "1"
 NLopt = "0.5, 0.6"
 PooledArrays = "0.5, 1"
-ProgressMeter = "1.5"
+ProgressMeter = "1.7"
 StaticArrays = "0.11, 0.12, 1"
 StatsBase = "0.31, 0.32, 0.33"
 StatsFuns = "0.8, 0.9"

From bf56ca28cfd1c1c4c8655c1068c32e6c8ec917a7 Mon Sep 17 00:00:00 2001
From: Phillip Alday <me@phillipalday.com>
Date: Mon, 12 Jul 2021 13:08:04 -0500
Subject: [PATCH 14/23] kwargs... for refit!

---
 src/generalizedlinearmixedmodel.jl | 19 ++++++++++---------
 src/linearmixedmodel.jl            | 16 ++++++++--------
 2 files changed, 18 insertions(+), 17 deletions(-)

diff --git a/src/generalizedlinearmixedmodel.jl b/src/generalizedlinearmixedmodel.jl
index 1831666d7..45893dd95 100644
--- a/src/generalizedlinearmixedmodel.jl
+++ b/src/generalizedlinearmixedmodel.jl
@@ -577,30 +577,31 @@ LinearAlgebra.rank(m::GeneralizedLinearMixedModel) = m.LMM.feterm.rank
 
 """
     refit!(m::GeneralizedLinearMixedModel[, y::Vector];
-          fast::Bool = (length(m.θ) == length(m.optsum.final)),
-          nAGQ::Integer = m.optsum.nAGQ))
+           fast::Bool = (length(m.θ) == length(m.optsum.final)),
+           nAGQ::Integer = m.optsum.nAGQ,
+           kwargs...)
 
 Refit the model `m` after installing response `y`.
 
 If `y` is omitted the current response vector is used.
 
 If not specified, the `fast` and `nAGQ` options from the previous fit are used.
-
+`kwargs` are the same as [`fit!`](@ref)
 """
-function refit!(m::GeneralizedLinearMixedModel{T};
+function refit!(m::GeneralizedLinearMixedModel;
                 fast::Bool = (length(m.θ) == length(m.optsum.final)),
-                nAGQ::Integer = m.optsum.nAGQ, progress::Bool=false)  where T
+                nAGQ::Integer = m.optsum.nAGQ, kwargs...)
 
-    fit!(unfit!(m); fast=fast, nAGQ=nAGQ, progress=progress)
+    fit!(unfit!(m); fast, nAGQ, kwargs...)
 end
 
-function refit!(m::GeneralizedLinearMixedModel{T}, y;
+function refit!(m::GeneralizedLinearMixedModel, y;
                 fast::Bool = (length(m.θ) == length(m.optsum.final)),
-                nAGQ::Integer = m.optsum.nAGQ, progress::Bool=false) where T
+                nAGQ::Integer = m.optsum.nAGQ, kwargs...)
     m_resp_y = m.resp.y
     length(y) == size(m_resp_y, 1) || throw(DimensionMismatch(""))
     copyto!(m_resp_y, y)
-    refit!(m; progress=progress)
+    refit!(m; fast, nAGQ, kwargs...)
 end
 
 """
diff --git a/src/linearmixedmodel.jl b/src/linearmixedmodel.jl
index f718bc34c..ccb61f6bd 100644
--- a/src/linearmixedmodel.jl
+++ b/src/linearmixedmodel.jl
@@ -386,7 +386,7 @@ end
 """
     fit!(m::LinearMixedModel[; progress::Bool=true, REML::Bool=false])
 
-Optimize the objective of a `LinearMixedModel`.  When `progress` is `true` a 
+Optimize the objective of a `LinearMixedModel`.  When `progress` is `true` a
 `ProgressMeter.ProgressUnknown` display is shown during the optimization of the
 objective, if the optimization takes more than one second or so.
 """
@@ -775,22 +775,22 @@ function reevaluateAend!(m::LinearMixedModel)
 end
 
 """
-    refit!(m::LinearMixedModel[, y::Vector]; REML=m.optsum.REML, progress::Bool=false)
+    refit!(m::LinearMixedModel[, y::Vector]; REML=m.optsum.REML, kwargs...)
 
 Refit the model `m` after installing response `y`.
 
-If `y` is omitted the current response vector is used.  Note that the default for
-`progress` is `false` on refits (whereas it is `true` in the `fit!` methods.)
+If `y` is omitted the current response vector is used.
+`kwargs` are the same as [`fit!`](@ref).
 """
-function refit!(m::LinearMixedModel; REML=m.optsum.REML, progress::Bool=false)
-    fit!(unfit!(m); REML=REML, progress=progress)
+function refit!(m::LinearMixedModel; REML=m.optsum.REML, kwargs...)
+    fit!(unfit!(m); REML=REML, kwargs...)
 end
 
-function refit!(m::LinearMixedModel, y; REML=m.optsum.REML, progress::Bool=false)
+function refit!(m::LinearMixedModel, y; REML=m.optsum.REML, kwargs...)
     resp = m.y
     length(y) == length(resp) || throw(DimensionMismatch(""))
     copyto!(resp, y)
-    refit!(m; REML=REML, progress=progress)
+    refit!(m; REML=REML, kwargs...)
 end
 
 """

From 7cabb81299dabbd0e7e6571c76200f3b32782ef3 Mon Sep 17 00:00:00 2001
From: Phillip Alday <me@phillipalday.com>
Date: Mon, 12 Jul 2021 13:23:13 -0500
Subject: [PATCH 15/23] simplify second method

---
 src/generalizedlinearmixedmodel.jl | 6 ++----
 src/linearmixedmodel.jl            | 4 ++--
 2 files changed, 4 insertions(+), 6 deletions(-)

diff --git a/src/generalizedlinearmixedmodel.jl b/src/generalizedlinearmixedmodel.jl
index 45893dd95..b5765f5f4 100644
--- a/src/generalizedlinearmixedmodel.jl
+++ b/src/generalizedlinearmixedmodel.jl
@@ -595,13 +595,11 @@ function refit!(m::GeneralizedLinearMixedModel;
     fit!(unfit!(m); fast, nAGQ, kwargs...)
 end
 
-function refit!(m::GeneralizedLinearMixedModel, y;
-                fast::Bool = (length(m.θ) == length(m.optsum.final)),
-                nAGQ::Integer = m.optsum.nAGQ, kwargs...)
+function refit!(m::GeneralizedLinearMixedModel, y; kwargs...)
     m_resp_y = m.resp.y
     length(y) == size(m_resp_y, 1) || throw(DimensionMismatch(""))
     copyto!(m_resp_y, y)
-    refit!(m; fast, nAGQ, kwargs...)
+    refit!(m; kwargs...)
 end
 
 """
diff --git a/src/linearmixedmodel.jl b/src/linearmixedmodel.jl
index ccb61f6bd..e0b712f88 100644
--- a/src/linearmixedmodel.jl
+++ b/src/linearmixedmodel.jl
@@ -786,11 +786,11 @@ function refit!(m::LinearMixedModel; REML=m.optsum.REML, kwargs...)
     fit!(unfit!(m); REML=REML, kwargs...)
 end
 
-function refit!(m::LinearMixedModel, y; REML=m.optsum.REML, kwargs...)
+function refit!(m::LinearMixedModel, y; kwargs...)
     resp = m.y
     length(y) == length(resp) || throw(DimensionMismatch(""))
     copyto!(resp, y)
-    refit!(m; REML=REML, kwargs...)
+    refit!(m; kwargs...)
 end
 
 """

From a9d984a0db3b67b479b73aed4fe42745e309bb79 Mon Sep 17 00:00:00 2001
From: Phillip Alday <me@phillipalday.com>
Date: Mon, 12 Jul 2021 13:43:06 -0500
Subject: [PATCH 16/23] fix tests for latent floating point issue

---
 src/generalizedlinearmixedmodel.jl |  5 +++--
 test/pirls.jl                      | 15 +++++++++++----
 2 files changed, 14 insertions(+), 6 deletions(-)

diff --git a/src/generalizedlinearmixedmodel.jl b/src/generalizedlinearmixedmodel.jl
index b5765f5f4..21334cb0b 100644
--- a/src/generalizedlinearmixedmodel.jl
+++ b/src/generalizedlinearmixedmodel.jl
@@ -591,8 +591,9 @@ If not specified, the `fast` and `nAGQ` options from the previous fit are used.
 function refit!(m::GeneralizedLinearMixedModel;
                 fast::Bool = (length(m.θ) == length(m.optsum.final)),
                 nAGQ::Integer = m.optsum.nAGQ, kwargs...)
-
-    fit!(unfit!(m); fast, nAGQ, kwargs...)
+    @show fast
+    @show nAGQ
+    fit!(unfit!(m); fast=fast, nAGQ=nAGQ, kwargs...)
 end
 
 function refit!(m::GeneralizedLinearMixedModel, y; kwargs...)
diff --git a/test/pirls.jl b/test/pirls.jl
index adea18d32..e8ae3dd2f 100644
--- a/test/pirls.jl
+++ b/test/pirls.jl
@@ -74,7 +74,7 @@ end
 
 @testset "cbpp" begin
     cbpp = dataset(:cbpp)
-    gm2 = fit(MixedModel, first(gfms[:cbpp]), cbpp, Binomial(), wts=float(cbpp.hsz), progress=false)
+    gm2 = fit(MixedModel, first(gfms[:cbpp]), cbpp, Binomial(); wts=float(cbpp.hsz), progress=false)
     @test weights(gm2) == cbpp.hsz
     @test deviance(gm2,true) ≈ 100.09585619892968 atol=0.0001
     @test sum(abs2, gm2.u[1]) ≈ 9.723054788538546 atol=0.0001
@@ -91,11 +91,18 @@ end
     @testset "GLMM refit" begin
         gm2r = deepcopy(gm2)
         @test_throws ArgumentError fit!(gm2r)
-        refit!(gm2r, 1 .- gm2.y; fast=true)
-        @test gm2r.β ≈ -gm2.β atol=1e-3
+
+        refit!(gm2r; fast=true, progress=false)
+        @test length(gm2r.optsum.final) == 1
         @test gm2r.θ ≈ gm2.θ atol=1e-3
 
-        refit!(gm2r, 1 .- gm2.y; fast=false)
+        # swapping successes and failures to give us the same model
+        # but with opposite signs. healthy ≈ 1 - response(gm2r)
+        # but defining it in terms of the original values avoids some
+        # nasty floating point issues
+        healthy = @. (cbpp.hsz - cbpp.incid) / cbpp.hsz
+        refit!(gm2r, healthy; fast=false, progress=false)
+        @test length(gm2r.optsum.final) == 5
         @test gm2r.β ≈ -gm2.β atol=1e-3
         @test gm2r.θ ≈ gm2.θ atol=1e-3
     end

From 55a6d38324edd79ad32dffdbb1147ca49c23c649 Mon Sep 17 00:00:00 2001
From: Phillip Alday <me@phillipalday.com>
Date: Mon, 12 Jul 2021 13:46:40 -0500
Subject: [PATCH 17/23] remove debug code

---
 src/generalizedlinearmixedmodel.jl | 2 --
 1 file changed, 2 deletions(-)

diff --git a/src/generalizedlinearmixedmodel.jl b/src/generalizedlinearmixedmodel.jl
index 21334cb0b..18827b718 100644
--- a/src/generalizedlinearmixedmodel.jl
+++ b/src/generalizedlinearmixedmodel.jl
@@ -591,8 +591,6 @@ If not specified, the `fast` and `nAGQ` options from the previous fit are used.
 function refit!(m::GeneralizedLinearMixedModel;
                 fast::Bool = (length(m.θ) == length(m.optsum.final)),
                 nAGQ::Integer = m.optsum.nAGQ, kwargs...)
-    @show fast
-    @show nAGQ
     fit!(unfit!(m); fast=fast, nAGQ=nAGQ, kwargs...)
 end
 

From 39c3d0c43fcd17addc127c76ba937d252eb6c07a Mon Sep 17 00:00:00 2001
From: Douglas Bates <dmbates@gmail.com>
Date: Mon, 12 Jul 2021 18:48:32 -0500
Subject: [PATCH 18/23] pass progress=false in refit! in tests

---
 test/bootstrap.jl | 16 ++++++++--------
 test/linalg.jl    |  2 +-
 test/pirls.jl     |  2 +-
 3 files changed, 10 insertions(+), 10 deletions(-)

diff --git a/test/bootstrap.jl b/test/bootstrap.jl
index e9aa3302e..26b430ac9 100644
--- a/test/bootstrap.jl
+++ b/test/bootstrap.jl
@@ -20,34 +20,34 @@ include("modelcache.jl")
         resp₀ = copy(response(fm))
         # type conversion of ints to floats
         simulate!(StableRNG(1234321), fm, β=[1], σ=1)
-        refit!(fm,resp₀)
-        refit!(simulate!(StableRNG(1234321), fm))
+        refit!(fm, resp₀; progress=false)
+        refit!(simulate!(StableRNG(1234321), fm); progress=false)
         @test deviance(fm) ≈ 322.6582 atol=0.001
-        refit!(fm, float(ds.yield))
+        refit!(fm, float(ds.yield), progress=false)
         # Global/implicit RNG method
         Random.seed!(1234321)
-        refit!(simulate!(fm))
+        refit!(simulate!(fm); progress=false)
         # just make sure this worked, don't check fit
         # (because the RNG can change between Julia versions)
         @test response(fm) ≠ resp₀
         simulate!(fm, θ = fm.θ)
-        @test_throws DimensionMismatch refit!(fm, zeros(29))
+        @test_throws DimensionMismatch refit!(fm, zeros(29); progress=false)
         # restore the original state
-        refit!(fm, vec(float.(ds.yield)))
+        refit!(fm, vec(float.(ds.yield)); progress=false)
     end
     @testset "Poisson" begin
         center(v::AbstractVector) = v .- (sum(v) / length(v))
         grouseticks = DataFrame(dataset(:grouseticks))
         grouseticks.ch = center(grouseticks.height)
         gm4 = fit(MixedModel, only(gfms[:grouseticks]), grouseticks, Poisson(), fast=true, progress=false)  # fails in pirls! with fast=false
-        gm4sim = refit!(simulate!(StableRNG(42), deepcopy(gm4)))
+        gm4sim = refit!(simulate!(StableRNG(42), deepcopy(gm4)); progress=false)
         @test isapprox(gm4.β, gm4sim.β; atol=norm(stderror(gm4)))
     end
 
     @testset "Binomial" begin
         cbpp = dataset(:cbpp)
         gm2 = fit(MixedModel, first(gfms[:cbpp]), cbpp, Binomial(), wts=float(cbpp.hsz), progress=false)
-        gm2sim = refit!(simulate!(StableRNG(42), deepcopy(gm2)), fast=true)
+        gm2sim = refit!(simulate!(StableRNG(42), deepcopy(gm2)); fast=true, progress=false)
         @test isapprox(gm2.β, gm2sim.β; atol=norm(stderror(gm2)))
     end
     @testset "_rand with dispersion" begin
diff --git a/test/linalg.jl b/test/linalg.jl
index 26b2c79f0..4caa4eb10 100644
--- a/test/linalg.jl
+++ b/test/linalg.jl
@@ -49,7 +49,7 @@ end
     wm1 = fit(MixedModel, @formula(Y ~ 1+A+(1+A|G)+(1+A|H)), df, wts=ones(400), progress=false)
     @test loglikelihood(wm1) ≈ loglikelihood(m1)
     MixedModels.reweight!(wm1, ones(400))
-    @test loglikelihood(refit!(wm1)) ≈ loglikelihood(m1)
+    @test loglikelihood(refit!(wm1, progress=false)) ≈ loglikelihood(m1)
 end
 
 @testset "rankupdate!" begin
diff --git a/test/pirls.jl b/test/pirls.jl
index e8ae3dd2f..3dd242457 100644
--- a/test/pirls.jl
+++ b/test/pirls.jl
@@ -45,7 +45,7 @@ include("modelcache.jl")
 
     @test dof(gm0) == length(gm0.β) + length(gm0.θ)
     @test nobs(gm0) == 1934
-    refit!(gm0, fast=true, nAGQ=7)
+    refit!(gm0, fast=true, nAGQ=7, progress=false)
     @test isapprox(deviance(gm0), 2360.9838, atol=0.001)
     gm1 = fit(MixedModel, only(gfms[:contra]), contra, Bernoulli(), nAGQ=7, progress=false)
     @test isapprox(deviance(gm1), 2360.8760, atol=0.001)

From 0ee090ff40a56c365cc647c161714f0aa10fafe5 Mon Sep 17 00:00:00 2001
From: Douglas Bates <dmbates@gmail.com>
Date: Mon, 12 Jul 2021 19:08:10 -0500
Subject: [PATCH 19/23] More cases of adding progress=false in refit! call

---
 test/mime.jl       |  4 ++--
 test/optsummary.jl |  2 +-
 test/pls.jl        | 10 +++++-----
 3 files changed, 8 insertions(+), 8 deletions(-)

diff --git a/test/mime.jl b/test/mime.jl
index 3492b38be..a5f8c8961 100644
--- a/test/mime.jl
+++ b/test/mime.jl
@@ -199,5 +199,5 @@ rows & subj & item & fixed \\\\
 end
 
 # return these models to their fitted state for the cache
-refit!(fm1)
-refit!(fm0)
+refit!(fm1; progress=false)
+refit!(fm0; progress=false)
diff --git a/test/optsummary.jl b/test/optsummary.jl
index da1de11c6..02bbb799e 100644
--- a/test/optsummary.jl
+++ b/test/optsummary.jl
@@ -8,7 +8,7 @@ include("modelcache.jl")
     @testset "maxfeval" begin
         fm1 = LinearMixedModel(first(fms[:sleepstudy]), dataset(:sleepstudy))
         fm1.optsum.maxfeval = 1
-        @test_logs (:warn, "NLopt optimization failure: MAXEVAL_REACHED") refit!(fm1)
+        @test_logs (:warn, "NLopt optimization failure: MAXEVAL_REACHED") refit!(fm1; progress=false)
         @test fm1.optsum.returnvalue == :MAXEVAL_REACHED
         @test fm1.optsum.feval == 1
     end
diff --git a/test/pls.jl b/test/pls.jl
index 8c6a7d6a4..4305e0b8e 100644
--- a/test/pls.jl
+++ b/test/pls.jl
@@ -53,7 +53,7 @@ end
     output = String(take!(io))
     @test startswith(output, "rows:")
 
-    refit!(fm1)
+    refit!(fm1; progress=false)
 
     @test isfitted(fm1)
     @test :θ in propertynames(fm1)
@@ -118,7 +118,7 @@ end
     @test startswith(str, "Variance components:")
     @test vc.s == sdest(fm1)
 
-    refit!(fm1, REML=true)
+    refit!(fm1; REML=true, progress=false)
     @test objective(fm1) ≈ 319.65427684225216 atol=0.0001
     @test_throws ArgumentError loglikelihood(fm1)
     @test dof_residual(fm1) ≥ 0
@@ -130,7 +130,7 @@ end
     @test isa(vc, Matrix{Float64})
     @test only(vc) ≈ 375.7167775 rtol=1.e-6
     # since we're caching the fits, we should get it back to being correctly fitted
-    refit!(fm1; REML=false)
+    refit!(fm1; REML=false, progress=false)
 end
 
 @testset "Dyestuff2" begin
@@ -147,9 +147,9 @@ end
     @test logdet(fm) ≈ 0.0
     @test issingular(fm)
     #### modifies the model
-    refit!(fm, float(MixedModels.dataset(:dyestuff)[:yield]))
+    refit!(fm, float(MixedModels.dataset(:dyestuff)[:yield]); progress=false)
     @test objective(fm) ≈ 327.3270598811428 atol=0.001
-    refit!(fm, float(MixedModels.dataset(:dyestuff2)[:yield])) # restore the model in the cache
+    refit!(fm, float(MixedModels.dataset(:dyestuff2)[:yield]); progress=false) # restore the model in the cache
 end
 
 @testset "penicillin" begin

From 384af96394ea87f1adbbab28c8c6c022ee98e281 Mon Sep 17 00:00:00 2001
From: Douglas Bates <dmbates@gmail.com>
Date: Mon, 12 Jul 2021 19:37:35 -0500
Subject: [PATCH 20/23] add progress=false in more fit and refit! calls

---
 test/fit.jl                 |  2 +-
 test/likelihoodratiotest.jl | 22 +++++++++++-----------
 test/missing.jl             |  6 +++---
 3 files changed, 15 insertions(+), 15 deletions(-)

diff --git a/test/fit.jl b/test/fit.jl
index 622fa506d..48f9f1643 100644
--- a/test/fit.jl
+++ b/test/fit.jl
@@ -17,7 +17,7 @@ end
     @test_throws(ArgumentError("use LinearMixedModel for Normal distribution with IdentityLink"),
                  fit(GeneralizedLinearMixedModel,
                      @formula(yield ~ 1 + (1|batch)),
-                     MixedModels.dataset(:dyestuff)))
+                     MixedModels.dataset(:dyestuff); progress=false))
 end
 
 @testset "Normal Distribution GLMM" begin
diff --git a/test/likelihoodratiotest.jl b/test/likelihoodratiotest.jl
index e8e47b0dc..c9e9dcddf 100644
--- a/test/likelihoodratiotest.jl
+++ b/test/likelihoodratiotest.jl
@@ -34,10 +34,10 @@ include("modelcache.jl")
     # fixed-effects specification in REML and
     # conversion of internal ArgumentError into @error for StatsModels.isnested
     kb07  = dataset(:kb07)
-    m1 = fit(MixedModel, @formula(rt_trunc ~ 1 + prec + (1|subj)), kb07, REML=true)
-    m2 = fit(MixedModel, @formula(rt_trunc ~ 1 + prec + (1+prec|subj)), kb07, REML=true)
+    m1 = fit(MixedModel, @formula(rt_trunc ~ 1 + prec + (1|subj)), kb07, REML=true, progress=false)
+    m2 = fit(MixedModel, @formula(rt_trunc ~ 1 + prec + (1+prec|subj)), kb07, REML=true, progress=false)
     @test isnested(m1, m2)
-    m2 = fit(MixedModel, @formula(rt_trunc ~ 1 + (1+prec|subj)), kb07, REML=true)
+    m2 = fit(MixedModel, @formula(rt_trunc ~ 1 + (1+prec|subj)), kb07, REML=true, progress=false)
     @test !isnested(m1, m2)
 
 end
@@ -45,8 +45,8 @@ end
 @testset "likelihoodratio test" begin
     slp = dataset(:sleepstudy);
 
-    fm0 = fit(MixedModel,@formula(reaction ~ 1 + (1+days|subj)),slp);
-    fm1 = fit(MixedModel,@formula(reaction ~ 1 + days + (1+days|subj)),slp);
+    fm0 = fit(MixedModel,@formula(reaction ~ 1 + (1+days|subj)),slp, progress=false);
+    fm1 = fit(MixedModel,@formula(reaction ~ 1 + days + (1+days|subj)),slp, progress=false);
     lm0 = lm(@formula(reaction ~ 1), slp)
     lm1 = lm(@formula(reaction ~ 1 + days), slp)
 
@@ -77,12 +77,12 @@ end
     @test_throws ArgumentError likelihoodratiotest(lm1, fm0)
 
     # mix of REML and ML
-    fm0 = fit(MixedModel,@formula(reaction ~ 1 + (1+days|subj)),slp, REML=true);
+    fm0 = fit(MixedModel,@formula(reaction ~ 1 + (1+days|subj)),slp, REML=true, progress=false);
     @test_throws ArgumentError likelihoodratiotest(fm0,fm1)
     @test_throws ArgumentError likelihoodratiotest(lm0,fm0)
 
     # differing FE with REML
-    fm1 = fit(MixedModel,@formula(reaction ~ 1 + days + (1+days|subj)),slp, REML=true);
+    fm1 = fit(MixedModel,@formula(reaction ~ 1 + days + (1+days|subj)),slp, REML=true, progress=false);
 
     @test_throws ArgumentError likelihoodratiotest(fm0,fm1)
     contra = MixedModels.dataset(:contra);
@@ -92,8 +92,8 @@ end
     cc.usenum = ifelse.(cc.use .== "Y", 1 , 0)
     gmf = glm(@formula(usenum ~ 1+age+urban+livch), cc, Bernoulli());
     gmf2 = glm(@formula(usenum ~ 1+age+abs2(age)+urban+livch), cc, Bernoulli());
-    gm0 = fit(MixedModel, @formula(use ~ 1+age+urban+livch+(1|urban&dist)), contra, Bernoulli(), fast=true);
-    gm1 = fit(MixedModel, @formula(use ~ 1+age+abs2(age)+urban+livch+(1|urban&dist)), contra, Bernoulli(), fast=true);
+    gm0 = fit(MixedModel, @formula(use ~ 1+age+urban+livch+(1|urban&dist)), contra, Bernoulli(), fast=true, progress=false);
+    gm1 = fit(MixedModel, @formula(use ~ 1+age+abs2(age)+urban+livch+(1|urban&dist)), contra, Bernoulli(), fast=true, progress=false);
 
     lrt = likelihoodratiotest(gmf, gm1)
     @test [-2 * loglikelihood(gmf), deviance(gm1)] ≈ lrt.deviance
@@ -112,12 +112,12 @@ end
     @test length(lrt.formulae) == 2
 
     # mismatched links
-    gm_probit = fit(MixedModel, @formula(use ~ 1+age+urban+livch+(1|urban&dist)), contra, Bernoulli(), ProbitLink(), fast=true);
+    gm_probit = fit(MixedModel, @formula(use ~ 1+age+urban+livch+(1|urban&dist)), contra, Bernoulli(), ProbitLink(), fast=true, progress=false);
     @test_throws ArgumentError likelihoodratiotest(gmf, gm_probit)
     @test_throws ArgumentError likelihoodratiotest(gm0, gm_probit)
 
     # mismatched families
-    gm_poisson = fit(MixedModel, @formula(use ~ 1+age+urban+livch+(1|urban&dist)), contra, Poisson(), fast=true);
+    gm_poisson = fit(MixedModel, @formula(use ~ 1+age+urban+livch+(1|urban&dist)), contra, Poisson(), fast=true, progress=false);
     @test_throws ArgumentError likelihoodratiotest(gmf, gm_poisson)
     @test_throws ArgumentError likelihoodratiotest(gm0, gm_poisson)
 
diff --git a/test/missing.jl b/test/missing.jl
index 8da3be5bf..72620a33f 100644
--- a/test/missing.jl
+++ b/test/missing.jl
@@ -21,14 +21,14 @@ slp[1,:days] = missing
 @testset "Missing Omit" begin
     @testset "Missing from unused variables" begin
         # missing from unused variables should have no impact
-        m1 = fit(MixedModel, @formula(reaction ~ 1 + (1|subj)), MixedModels.dataset(:sleepstudy))
+        m1 = fit(MixedModel, @formula(reaction ~ 1 + (1|subj)), MixedModels.dataset(:sleepstudy), progress=false)
         m1_missing = fit(MixedModel, @formula(reaction ~ 1 + (1|subj)), slp)
         @test isapprox(m1.θ, m1_missing.θ, rtol=1.0e-12)
     end
 
     @testset "Missing from used variables" begin
-        m1 = fit(MixedModel, @formula(reaction ~ 1 + days + (1|subj)), MixedModels.dataset(:sleepstudy))
-        m1_missing = fit(MixedModel, @formula(reaction ~ 1 + days + (1|subj)), slp)
+        m1 = fit(MixedModel, @formula(reaction ~ 1 + days + (1|subj)), MixedModels.dataset(:sleepstudy), progress=false)
+        m1_missing = fit(MixedModel, @formula(reaction ~ 1 + days + (1|subj)), slp, progress=false)
         @test nobs(m1) - nobs(m1_missing) == 1
     end
 end

From 8902b553bc1974d0979a11936d8bcec5a7b9859c Mon Sep 17 00:00:00 2001
From: Douglas Bates <dmbates@gmail.com>
Date: Mon, 12 Jul 2021 19:54:11 -0500
Subject: [PATCH 21/23] yet another progress=false

---
 test/missing.jl | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/test/missing.jl b/test/missing.jl
index 72620a33f..53d29770f 100644
--- a/test/missing.jl
+++ b/test/missing.jl
@@ -22,7 +22,7 @@ slp[1,:days] = missing
     @testset "Missing from unused variables" begin
         # missing from unused variables should have no impact
         m1 = fit(MixedModel, @formula(reaction ~ 1 + (1|subj)), MixedModels.dataset(:sleepstudy), progress=false)
-        m1_missing = fit(MixedModel, @formula(reaction ~ 1 + (1|subj)), slp)
+        m1_missing = fit(MixedModel, @formula(reaction ~ 1 + (1|subj)), slp, progress=false)
         @test isapprox(m1.θ, m1_missing.θ, rtol=1.0e-12)
     end
 

From 1222a512fd46adb7ddfe3751c4a3349a3fd5a0d1 Mon Sep 17 00:00:00 2001
From: Phillip Alday <me@phillipalday.com>
Date: Mon, 12 Jul 2021 22:15:07 -0500
Subject: [PATCH 22/23] disable single-model progress in bootstrap

---
 src/bootstrap.jl | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/bootstrap.jl b/src/bootstrap.jl
index 7051599c9..dbf7a0bec 100644
--- a/src/bootstrap.jl
+++ b/src/bootstrap.jl
@@ -106,7 +106,7 @@ function parametricbootstrap(
         lock(rnglock)
         mod = simulate!(rng, mod, β = β, σ = σ, θ = θ)
         unlock(rnglock)
-        refit!(mod)
+        refit!(mod; progress=false)
         (
          objective = mod.objective,
          σ = mod.σ,

From 491a4d144e597b56948ac568c3670587ef70aca5 Mon Sep 17 00:00:00 2001
From: Phillip Alday <me@phillipalday.com>
Date: Mon, 12 Jul 2021 22:17:56 -0500
Subject: [PATCH 23/23] whack-a-mole + ; before kwargs

---
 test/linalg.jl     |  2 +-
 test/modelcache.jl |  2 +-
 test/optsummary.jl |  2 +-
 test/pirls.jl      | 14 +++++++-------
 test/pls.jl        |  4 ++--
 5 files changed, 12 insertions(+), 12 deletions(-)

diff --git a/test/linalg.jl b/test/linalg.jl
index 4caa4eb10..b011c2d3d 100644
--- a/test/linalg.jl
+++ b/test/linalg.jl
@@ -93,7 +93,7 @@ end
         # an example in MixedModels.jl#123
         df = gendata(10000, 500)
         f = @formula(Y ~ (1 + X|H) + (1|G))
-        m500 = fit!(LinearMixedModel(f, df), progress=false)
+        m500 = fit!(LinearMixedModel(f, df); progress=false)
         # the real test here isn't in the theta comparison but in that the fit
         # completes successfully
         @test length(m500.u) == 2
diff --git a/test/modelcache.jl b/test/modelcache.jl
index 5512bf860..eaaeadee0 100644
--- a/test/modelcache.jl
+++ b/test/modelcache.jl
@@ -45,6 +45,6 @@ using MixedModels: dataset
 
 function models(nm::Symbol)
     get!(fittedmodels, nm) do
-        [fit(MixedModel, f, dataset(nm), progress=false) for f in allfms[nm]]
+        [fit(MixedModel, f, dataset(nm); progress=false) for f in allfms[nm]]
     end
 end
diff --git a/test/optsummary.jl b/test/optsummary.jl
index 02bbb799e..69c4d9897 100644
--- a/test/optsummary.jl
+++ b/test/optsummary.jl
@@ -19,7 +19,7 @@ include("modelcache.jl")
         fm1 = LinearMixedModel(last(fms[:kb07]), dataset(:kb07))
         maxtime = 1e-6
         fm1.optsum.maxtime = maxtime
-        @test_logs (:warn, "NLopt optimization failure: MAXTIME_REACHED") fit!(fm1)
+        @test_logs (:warn, "NLopt optimization failure: MAXTIME_REACHED") fit!(fm1; progress=false)
         @test fm1.optsum.returnvalue == :MAXTIME_REACHED
         @test fm1.optsum.maxtime == maxtime
     end
diff --git a/test/pirls.jl b/test/pirls.jl
index 3dd242457..f306945c5 100644
--- a/test/pirls.jl
+++ b/test/pirls.jl
@@ -13,7 +13,7 @@ include("modelcache.jl")
 
 @testset "contra" begin
     contra = dataset(:contra)
-    gm0 = fit(MixedModel, only(gfms[:contra]), contra, Bernoulli(), fast=true, progress=false)
+    gm0 = fit(MixedModel, only(gfms[:contra]), contra, Bernoulli(); fast=true, progress=false)
     @test gm0.lowerbd == zeros(1)
     @test isapprox(gm0.θ, [0.5720734451352923], atol=0.001)
     @test !issingular(gm0)
@@ -45,9 +45,9 @@ include("modelcache.jl")
 
     @test dof(gm0) == length(gm0.β) + length(gm0.θ)
     @test nobs(gm0) == 1934
-    refit!(gm0, fast=true, nAGQ=7, progress=false)
+    refit!(gm0; fast=true, nAGQ=7, progress=false)
     @test isapprox(deviance(gm0), 2360.9838, atol=0.001)
-    gm1 = fit(MixedModel, only(gfms[:contra]), contra, Bernoulli(), nAGQ=7, progress=false)
+    gm1 = fit(MixedModel, only(gfms[:contra]), contra, Bernoulli(); nAGQ=7, progress=false)
     @test isapprox(deviance(gm1), 2360.8760, atol=0.001)
     @test gm1.β == gm1.beta
     @test gm1.θ == gm1.theta
@@ -90,7 +90,7 @@ end
 
     @testset "GLMM refit" begin
         gm2r = deepcopy(gm2)
-        @test_throws ArgumentError fit!(gm2r)
+        @test_throws ArgumentError fit!(gm2r; progress=false)
 
         refit!(gm2r; fast=true, progress=false)
         @test length(gm2r.optsum.final) == 1
@@ -109,7 +109,7 @@ end
 end
 
 @testset "verbagg" begin
-    gm3 = fit(MixedModel, only(gfms[:verbagg]), dataset(:verbagg), Bernoulli(), progress=false)
+    gm3 = fit(MixedModel, only(gfms[:verbagg]), dataset(:verbagg), Bernoulli(); progress=false)
     @test deviance(gm3) ≈ 8151.40 rtol=1e-5
     @test lowerbd(gm3) == vcat(fill(-Inf, 6), zeros(2))
     @test fitted(gm3) == predict(gm3)
@@ -122,7 +122,7 @@ end
     center(v::AbstractVector) = v .- (sum(v) / length(v))
     grouseticks = DataFrame(dataset(:grouseticks))
     grouseticks.ch = center(grouseticks.height)
-    gm4 = fit(MixedModel, only(gfms[:grouseticks]), grouseticks, Poisson(), fast=true, progress=false)  # fails in pirls! with fast=false
+    gm4 = fit(MixedModel, only(gfms[:grouseticks]), grouseticks, Poisson(); fast=true, progress=false)  # fails in pirls! with fast=false
     @test isapprox(deviance(gm4), 851.4046, atol=0.001)
     # these two values are not well defined at the optimum
     #@test isapprox(sum(x -> sum(abs2, x), gm4.u), 196.8695297987013, atol=0.1)
@@ -159,7 +159,7 @@ end
     @test deviance(m1) ≈ 193.5587302384811 rtol=1.e-5
     @test only(m1.β) ≈ 4.192196439077657 atol=1.e-5
     @test only(m1.θ) ≈ 1.838245201739852 atol=1.e-5
-    m11 = fit(MixedModel, gform, goldstein, Poisson(), nAGQ=11, progress=false)
+    m11 = fit(MixedModel, gform, goldstein, Poisson(); nAGQ=11, progress=false)
     @test deviance(m11) ≈ 193.51028088736842 rtol=1.e-5
     @test only(m11.β) ≈ 4.192196439077657 atol=1.e-5
     @test only(m11.θ) ≈ 1.838245201739852 atol=1.e-5
diff --git a/test/pls.jl b/test/pls.jl
index 4305e0b8e..3f7ed7ff4 100644
--- a/test/pls.jl
+++ b/test/pls.jl
@@ -540,13 +540,13 @@ end
     )
 
     #= no need to fit yet another model without weights, but here are the reference values from lme4
-    m1 = fit(MixedModel, @formula(a ~ 1 + b + (1|c)), data)
+    m1 = fit(MixedModel, @formula(a ~ 1 + b + (1|c)), data; progress=false)
     @test m1.θ ≈ [0.0]
     @test stderror(m1) ≈  [1.084912, 4.966336] atol = 1.e-4
     @test vcov(m1) ≈ [1.177035 -4.802598; -4.802598 24.664497] atol = 1.e-4
     =#
 
-    m2 = fit(MixedModel, @formula(a ~ 1 + b + (1|c)), data, wts = data.w1, progress=false)
+    m2 = fit(MixedModel, @formula(a ~ 1 + b + (1|c)), data; wts = data.w1, progress=false)
     @test m2.θ ≈ [0.295181729258352]  atol = 1.e-4
     @test stderror(m2) ≈  [0.9640167, 3.6309696] atol = 1.e-4
     @test vcov(m2) ≈ [0.9293282 -2.557527; -2.5575267 13.183940] atol = 1.e-4