Version 0.4.1

Author: dm13450

.Rbuildignore

@@ -12,3 +12,4 @@
 ^docs$
 ^pkgdown$
 ^\.github$
+^CRAN-SUBMISSION$

CRAN-SUBMISSION

@@ -0,0 +1,3 @@
+Version: 0.4.1
+Date: 2023-03-10 11:19:55 UTC
+SHA: 2475911ff75ad7f043eae9834d3ad7073925b3d2

DESCRIPTION

@@ -1,7 +1,7 @@
 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="gordon@gordonjross.co.uk", role=c("aut")),
             person("Dean", "Markwick", email="dean.markwick@talk21.com", role=c("aut", "cre")),

README.Rmd

@@ -19,7 +19,7 @@ knitr::opts_chunk$set(
 
 [![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.
@@ -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)
 

README.md

@@ -8,7 +8,7 @@ status](https://github.com/dm13450/dirichletprocess/workflows/R-CMD-check/badge.
 [![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
@@ -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)

cran-comments.md

@@ -1,15 +1,12 @@
--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
@@ -19,7 +16,7 @@ In this version I have:
 
 ## R CMD check results
 
-0 errors | 0 warnings | 1 note
+0 errors | 0 warnings | 0 note
 
 * This is a new release.