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dm13450 committed Mar 10, 2023
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1 change: 1 addition & 0 deletions .Rbuildignore
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^docs$
^pkgdown$
^\.github$
^CRAN-SUBMISSION$
3 changes: 3 additions & 0 deletions CRAN-SUBMISSION
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Version: 0.4.1
Date: 2023-03-10 11:19:55 UTC
SHA: 2475911ff75ad7f043eae9834d3ad7073925b3d2
2 changes: 1 addition & 1 deletion DESCRIPTION
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Package: dirichletprocess
Type: Package
Title: Build Dirichlet Process Objects for Bayesian Modelling
Version: 0.4.0.9000
Version: 0.4.1
Authors@R: c(
person("Gordon", "J. Ross", email="[email protected]", role=c("aut")),
person("Dean", "Markwick", email="[email protected]", role=c("aut", "cre")),
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20 changes: 10 additions & 10 deletions README.Rmd
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[![R build status](https://github.com/dm13450/dirichletprocess/workflows/R-CMD-check/badge.svg)](https://github.com/dm13450/dirichletprocess/actions)
[![AppVeyor Build Status](https://ci.appveyor.com/api/projects/status/github/dm13450/dirichletprocess?branch=master&svg=true)](https://ci.appveyor.com/project/dm13450/dirichletprocess)
[![Coverage Status](https://codecov.io/gh/dm13450/dirichletprocess/branch/master/graph/badge.svg)](https://codecov.io/gh/dm13450/dirichletprocess)
[![Coverage Status](https://codecov.io/gh/dm13450/dirichletprocess/branch/master/graph/badge.svg)](https://app.codecov.io/gh/dm13450/dirichletprocess)


The dirichletprocess package provides tools for you to build custom Dirichlet process mixture models. You can use the pre-built Normal/Weibull/Beta distributions or create your own following the instructions in the vignette. In as little as four lines of code you can be modelling your data nonparametrically.
Expand Down Expand Up @@ -81,16 +81,16 @@ For more detailed explanations and examples see the vignette.

I've written a number of tutorials:

* [Non parametric priors](http://dm13450.github.io/2019/02/22/Nonparametric-Prior.html)
* [Calculating cluster probabilities](http://dm13450.github.io/2018/11/21/Cluster-Probabilities.html)
* [Clustering](http://dm13450.github.io/2018/05/30/Clustering.html)
* [Point processes](http://dm13450.github.io/2018/03/08/dirichletprocess-pointprocess.html)
* [Custom mixtures](http://dm13450.github.io/2018/02/21/Custom-Distributions-Conjugate.html)
* [Density estimation](http://dm13450.github.io/2018/02/01/Dirichlet-Density.html)
* [Checking convergence](http://dm13450.github.io/2020/01/11/Dirichlet-Convergence.html)
* [Non parametric priors](https://dm13450.github.io/2019/02/22/Nonparametric-Prior.html)
* [Calculating cluster probabilities](https://dm13450.github.io/2018/11/21/Cluster-Probabilities.html)
* [Clustering](https://dm13450.github.io/2018/05/30/Clustering.html)
* [Point processes](https://dm13450.github.io/2018/03/08/dirichletprocess-pointprocess.html)
* [Custom mixtures](https://dm13450.github.io/2018/02/21/Custom-Distributions-Conjugate.html)
* [Density estimation](https://dm13450.github.io/2018/02/01/Dirichlet-Density.html)
* [Checking convergence](https://dm13450.github.io/2020/01/11/Dirichlet-Convergence.html)

and some case studies:

* [State of the Market - Infinite State Hidden Markov Models](http://dm13450.github.io/2020/06/03/State-of-the-Market.html)
* [Palmer Penguins and an Introduction to Dirichlet Processes](http://dm13450.github.io/2020/09/28/PriorToPosterior.html)
* [State of the Market - Infinite State Hidden Markov Models](https://dm13450.github.io/2020/06/03/State-of-the-Market.html)
* [Palmer Penguins and an Introduction to Dirichlet Processes](https://dm13450.github.io/2020/09/28/PriorToPosterior.html)

20 changes: 10 additions & 10 deletions README.md
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[![AppVeyor Build
Status](https://ci.appveyor.com/api/projects/status/github/dm13450/dirichletprocess?branch=master&svg=true)](https://ci.appveyor.com/project/dm13450/dirichletprocess)
[![Coverage
Status](https://codecov.io/gh/dm13450/dirichletprocess/branch/master/graph/badge.svg)](https://codecov.io/gh/dm13450/dirichletprocess)
Status](https://codecov.io/gh/dm13450/dirichletprocess/branch/master/graph/badge.svg)](https://app.codecov.io/gh/dm13450/dirichletprocess)

The dirichletprocess package provides tools for you to build custom
Dirichlet process mixture models. You can use the pre-built
Expand Down Expand Up @@ -76,22 +76,22 @@ For more detailed explanations and examples see the vignette.
I’ve written a number of tutorials:

- [Non parametric
priors](http://dm13450.github.io/2019/02/22/Nonparametric-Prior.html)
priors](https://dm13450.github.io/2019/02/22/Nonparametric-Prior.html)
- [Calculating cluster
probabilities](http://dm13450.github.io/2018/11/21/Cluster-Probabilities.html)
- [Clustering](http://dm13450.github.io/2018/05/30/Clustering.html)
probabilities](https://dm13450.github.io/2018/11/21/Cluster-Probabilities.html)
- [Clustering](https://dm13450.github.io/2018/05/30/Clustering.html)
- [Point
processes](http://dm13450.github.io/2018/03/08/dirichletprocess-pointprocess.html)
processes](https://dm13450.github.io/2018/03/08/dirichletprocess-pointprocess.html)
- [Custom
mixtures](http://dm13450.github.io/2018/02/21/Custom-Distributions-Conjugate.html)
mixtures](https://dm13450.github.io/2018/02/21/Custom-Distributions-Conjugate.html)
- [Density
estimation](http://dm13450.github.io/2018/02/01/Dirichlet-Density.html)
estimation](https://dm13450.github.io/2018/02/01/Dirichlet-Density.html)
- [Checking
convergence](http://dm13450.github.io/2020/01/11/Dirichlet-Convergence.html)
convergence](https://dm13450.github.io/2020/01/11/Dirichlet-Convergence.html)

and some case studies:

- [State of the Market - Infinite State Hidden Markov
Models](http://dm13450.github.io/2020/06/03/State-of-the-Market.html)
Models](https://dm13450.github.io/2020/06/03/State-of-the-Market.html)
- [Palmer Penguins and an Introduction to Dirichlet
Processes](http://dm13450.github.io/2020/09/28/PriorToPosterior.html)
Processes](https://dm13450.github.io/2020/09/28/PriorToPosterior.html)
9 changes: 3 additions & 6 deletions cran-comments.md
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-dirichletprocess 0.4.0
-dirichletprocess 0.4.1
-----------------------



## Changes
In this version I have:

* Hierarchical Normal Models added by Giovanni Sighinolfi
* Added Giovanni Sighinolfi as a contributor.
* Added params chain to Hidden Markov Models
* Updated the vignette for hierarchical normal models.
* Changed the MhParameterProposal function signature to be consistent.


## Test environments
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## R CMD check results

0 errors | 0 warnings | 1 note
0 errors | 0 warnings | 0 note

* This is a new release.

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