Increasing the package version to 0.3.2. Also:

(1) changing a github version badge URL to get rid of the auxiliary file .version.json. Now the version information is retrieved from DESCRIPTION itself
(2) changing URL in projecteuclid link to DOI in a few files to get rid of a persistent incorrect URL NOTE
(3) reformatting the vignette URL to CRAN canonical
(4) speeding up examples
(5) rephrasing some help entries into the 3rd person (e.g. plots vs plot)
(6) including missing package scope denotation (utils::) in a call to packageDescription() function in the .onAttach() handler

DESCRIPTION

@@ -2,11 +2,11 @@ Package: DMRnet
 Type: Package
 Title: Delete or Merge Regressors Algorithms for Linear and Logistic
         Model Selection and High-Dimensional Data
-Version: 0.3.1.9002
+Version: 0.3.2
 Authors@R: c(person("Agnieszka", "Prochenka-Sołtys", email = "[email protected]", role = c("aut"), comment = "previous maintainer for versions <= 0.2.0"),
    person("Piotr", "Pokarowski", role = c("aut")),
    person("Szymon", "Nowakowski", email = "[email protected]", role = c("aut", "cre"), comment = c(ORCID = "0000-0002-1939-9512")))
-Description: Model selection algorithms for regression and classification, where the predictors can be continuous or categorical and the number of regressors may exceed the number of observations. The selected model consists of a subset of numerical regressors and partitions of levels of factors. Aleksandra Maj-Kańska, Piotr Pokarowski and Agnieszka Prochenka, 2015. Delete or merge regressors for linear model selection. Electronic Journal of Statistics 9(2): 1749-1778. <https://projecteuclid.org/euclid.ejs/1440507392>. Piotr Pokarowski and Jan Mielniczuk, 2015. Combined l1 and greedy l0 penalized least squares for linear model selection. Journal of Machine Learning Research 16(29): 961-992. <https://www.jmlr.org/papers/volume16/pokarowski15a/pokarowski15a.pdf>. Piotr Pokarowski, Wojciech Rejchel, Agnieszka Sołtys, Michał Frej and Jan Mielniczuk, 2022. Improving Lasso for model selection and prediction. Scandinavian Journal of Statistics, 49(2): 831–863. <doi:10.1111/sjos.12546>.
+Description: Model selection algorithms for regression and classification, where the predictors can be continuous or categorical and the number of regressors may exceed the number of observations. The selected model consists of a subset of numerical regressors and partitions of levels of factors. Aleksandra Maj-Kańska, Piotr Pokarowski and Agnieszka Prochenka, 2015. Delete or merge regressors for linear model selection. Electronic Journal of Statistics 9(2): 1749-1778. <doi:10.1214/15-EJS1050>. Piotr Pokarowski and Jan Mielniczuk, 2015. Combined l1 and greedy l0 penalized least squares for linear model selection. Journal of Machine Learning Research 16(29): 961-992. <https://www.jmlr.org/papers/volume16/pokarowski15a/pokarowski15a.pdf>. Piotr Pokarowski, Wojciech Rejchel, Agnieszka Sołtys, Michał Frej and Jan Mielniczuk, 2022. Improving Lasso for model selection and prediction. Scandinavian Journal of Statistics, 49(2): 831–863. <doi:10.1111/sjos.12546>.
 License: GPL-2
 Encoding: UTF-8
 LazyData: true

NEWS.md

@@ -1,15 +1,11 @@
 
-# DMRnet 0.3.1.9002
+# DMRnet 0.3.2
 
 - Improved readability of a getting-started vignette
 - Fixed a bug in model-indexed cross validation related to folds with different model sizes
 - Added df.1se to GIC-indexed cross validation
 - Improved CV plots with df.1se model
-
-
-# DMRnet 0.3.1.9001
-
-- Improved readability of Readme on CRAN ([issue #32](https://github.com/SzymonNowakowski/DMRnet/issues/32))
+- Improved readability of README on CRAN ([issue #32](https://github.com/SzymonNowakowski/DMRnet/issues/32))
 - Welcome message on package load added
 
 # DMRnet 0.3.1

R/DMR.R

@@ -1,6 +1,6 @@
 #' @title Delete or Merge Regressors
 #'
-#' @description Fit a path of linear (\code{family="gaussian"}) or logistic (\code{family="binomial"}) regression models, where the number of parameters changes from 1 to p (p is the number of columns in the model matrix). Models are subsets of continuous predictors and partitions of levels of factors in \code{X}.
+#' @description Fits a path of linear (\code{family="gaussian"}) or logistic (\code{family="binomial"}) regression models, where the number of parameters changes from 1 to p (p is the number of columns in the model matrix). Models are subsets of continuous predictors and partitions of levels of factors in \code{X}.
 #'
 #' @param X Input data frame; each row is an observation vector; each column can be numerical or integer for a continuous predictor or a factor for a categorical predictor; DMR works only if p<n (n is the number of observations, p the number of columns in the model matrix), for p>=n see \code{\link{DMRnet}}.
 #'

R/DMRnet-package.R

@@ -31,7 +31,7 @@
 #'
 #' @references
 #'
-#' Aleksandra Maj-Kańska, Piotr Pokarowski and Agnieszka Prochenka, 2015. Delete or merge regressors for linear model selection. Electronic Journal of Statistics 9(2): 1749-1778. \url{https://projecteuclid.org/euclid.ejs/1440507392}
+#' Aleksandra Maj-Kańska, Piotr Pokarowski and Agnieszka Prochenka, 2015. Delete or merge regressors for linear model selection. Electronic Journal of Statistics 9(2): 1749-1778. \doi{10.1214/15-EJS1050}
 #'
 #' Piotr Pokarowski and Jan Mielniczuk, 2015. Combined l1 and greedy l0 penalized least squares for linear model selection. Journal of Machine Learning Research 16(29): 961-992. \url{https://www.jmlr.org/papers/volume16/pokarowski15a/pokarowski15a.pdf}
 #'

R/DMRnet.R

@@ -1,6 +1,6 @@
 #' @title Delete or Merge Regressors net
 #'
-#' @description Fit a path of linear (\code{family="gaussian"}) or logistic (\code{family="binomial"}) regression models, where models are subsets of continuous predictors and partitions of levels of factors in \code{X}. Works even if p>=n (the number of observations is greater than the number of columns in the model matrix).
+#' @description Fits a path of linear (\code{family="gaussian"}) or logistic (\code{family="binomial"}) regression models, where models are subsets of continuous predictors and partitions of levels of factors in \code{X}. Works even if p>=n (the number of observations is greater than the number of columns in the model matrix).
 #'
 #' @param X Input data frame; each row is an observation vector; each column can be numerical or integer for a continuous predictor or a factor for a categorical predictor.
 #'
@@ -51,9 +51,9 @@
 #' @examples
 #' ## DMRnet for linear regression
 #' data(miete)
-#' ytr <- miete[1:500,1]
-#' Xtr <- miete[1:500,-1]
-#' Xte <- miete[501:1000,-1]
+#' ytr <- miete[1:200,1]
+#' Xtr <- miete[1:200,-1]
+#' Xte <- miete[201:250,-1]
 #' m1 <- DMRnet(Xtr, ytr)
 #' print(m1)
 #' plot(m1)
@@ -64,9 +64,9 @@
 #'
 #' ## DMRnet for logistic regression
 #' data(promoter)
-#' ytr <- factor(promoter[1:80,1])
-#' Xtr <- promoter[1:80,-1]
-#' Xte <- promoter[81:106,-1]
+#' ytr <- factor(promoter[1:70,1])
+#' Xtr <- promoter[1:70,-1]
+#' Xte <- promoter[71:106,-1]
 #' m2 <- DMRnet(Xtr, ytr, family = "binomial")
 #' print(m2)
 #' plot(m2)
@@ -77,9 +77,9 @@
 #'
 #' ## GLAMER for linear regression
 #' data(miete)
-#' ytr <- miete[1:500,1]
-#' Xtr <- miete[1:500,-1]
-#' Xte <- miete[501:1000,-1]
+#' ytr <- miete[1:200,1]
+#' Xtr <- miete[1:200,-1]
+#' Xte <- miete[201:250,-1]
 #' m1 <- DMRnet(Xtr, ytr, algorithm="glamer")
 #' print(m1)
 #' plot(m1)
@@ -90,9 +90,9 @@
 #'
 #' ## GLAMER for logistic regression
 #' data(promoter)
-#' ytr <- factor(promoter[1:80,1])
-#' Xtr <- promoter[1:80,-1]
-#' Xte <- promoter[81:106,-1]
+#' ytr <- factor(promoter[1:70,1])
+#' Xtr <- promoter[1:70,-1]
+#' Xte <- promoter[71:106,-1]
 #' m2 <- DMRnet(Xtr, ytr, family = "binomial", algorithm="glamer")
 #' print(m2)
 #' plot(m2)

R/cv.DMR.R

@@ -1,6 +1,6 @@
 #' @title cross-validation for DMR
 #'
-#' @description Does k-fold cross-validation for \code{DMR} and returns a value for df.
+#' @description Executes k-fold cross-validation for \code{DMR} and returns a value for df.
 #'
 #' @param X Input data frame, of dimension n x p; \code{DMR} works only if p<n, for p>=n see \code{\link{DMRnet}}; each row is an observation vector. Columns can be numerical or integer for continuous predictors or factors for categorical predictors.
 #'

R/cv.DMRnet.R

@@ -1,6 +1,6 @@
 #' @title cross-validation for DMRnet
 #'
-#' @description Does k-fold cross-validation for DMR and returns a value for df.
+#' @description Executes k-fold cross-validation for DMR and returns a value for df.
 #'
 #' @param X Input data frame, of dimension n x p; each row is an observation vector. Columns can be numerical or integer for continuous predictors or factors for categorical predictors.
 #'

R/onAttach.R

@@ -1,3 +1,3 @@
 .onAttach=function(libname,pkgname){
-  packageStartupMessage("Loaded DMRnet version ", as.character(packageDescription("DMRnet")[["Version"]]))
+  packageStartupMessage("Loaded DMRnet version ", as.character(utils::packageDescription("DMRnet")[["Version"]]))
 }

R/plot.DMR.R

@@ -1,6 +1,6 @@
 #' @title plot.DMR
 #'
-#' @description Plot coefficients from a \code{DMR} object.
+#' @description Plots coefficients from a \code{DMR} object.
 #'
 #' @param x Fitted \code{DMR} object.
 #'

R/plot.cv.DMR.R

@@ -1,6 +1,6 @@
 #' @title plot.cv.DMR
 #'
-#' @description Plot cross-validated error values from a \code{cv.DMR} object.
+#' @description Plots cross-validated error values from a \code{cv.DMR} object.
 #'
 #' @param x Fitted \code{cv.DMR} object.
 #'

R/plot.gic.DMR.R

@@ -1,6 +1,6 @@
 #' @title plot.gic.DMR
 #'
-#' @description Plot gic values from a \code{gic.DMR} object.
+#' @description Plots gic values from a \code{gic.DMR} object.
 #'
 #' @param x Fitted \code{gic.DMR} object.
 #'

R/predict.DMR.R

@@ -1,6 +1,6 @@
 #' @title predict.DMR
 #'
-#' @description Make predictions from a \code{DMR} object.
+#' @description Makes predictions from a \code{DMR} object.
 #'
 #' @param object Fitted \code{DMR} object.
 #'

R/predict.cv.DMR.R

@@ -1,6 +1,6 @@
 #' @title predict.cv.DMR
 #'
-#' @description Make predictions from a cv.DMR object (for the model with minimal cross-validated error /the default/ or the smallest model falling under the upper curve of a prediction error plus one standard deviation).
+#' @description Makes predictions from a cv.DMR object (for the model with minimal cross-validated error /the default/ or the smallest model falling under the upper curve of a prediction error plus one standard deviation).
 #'
 #' @param object Fitted cv.DMR object.
 #'

R/predict.gic.DMR.R

@@ -1,6 +1,6 @@
 #' @title predict.gic.DMR
 #'
-#' @description Make predictions from a \code{gic.DMR} object (for the model with minimal GIC).
+#' @description Makes predictions from a \code{gic.DMR} object (for the model with minimal GIC).
 #'
 #' @param object Fitted \code{gic.DMR} object.
 #'

R/print.DMR.R

@@ -1,6 +1,6 @@
 #' @title print.DMR
 #'
-#' @description Print a \code{DMR} object.
+#' @description Prints a \code{DMR} object.
 #'
 #' @param x Fitted \code{DMR} object.
 #'

R/release_questions.R

@@ -2,8 +2,6 @@ release_questions <- function() {
   c(
     "Have you run the testing_branch tests?",
     "Have you updated DESCRIPTION with new version number?",
-    "Have you updated README.md with new version number in master branch only (not testing_branch)?",
-    "Have you updated NEWS.md with new version number?",
-    "Have you updated .version.json with new version number?"
+    "Have you updated NEWS.md with new version number?"
   )
 }

README.md

@@ -1,5 +1,5 @@
 <!-- badges: start -->
-[![GitHub version](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/SzymonNowakowski/DMRnet/master/.version.json&style=flat&logo=github)](https://github.com/SzymonNowakowski/DMRnet)
+[![GitHub version](https://img.shields.io/github/r-package/v/SzymonNowakowski/DMRnet?color=yellowgreen&label=GitHub&logo=github](https://github.com/SzymonNowakowski/DMRnet)
 [![CRAN version](https://img.shields.io/cran/v/DMRnet?logo=R)](https://cran.r-project.org/package=DMRnet)
 [![downloads](https://cranlogs.r-pkg.org/badges/DMRnet)](https://cran.r-project.org/package=DMRnet)
 <!-- badges: end -->
@@ -9,17 +9,17 @@
 
 DMRnet (Delete or Merge Regressors) is a suit of algorithms for linear and logistic model selection with high-dimensional data (i.e. the number of regressors may exceed the number of observations). The predictors can be continuous or categorical. The selected model consists of a subset of numerical regressors and partitions of levels of factors. 
 
-For information on how to get started using DMRnet, see our [getting started vignette](https://cran.r-project.org/web/packages/DMRnet/vignettes/getting-started.html).
+For information on how to get started using DMRnet, see our [getting started vignette](https://cran.r-project.org/package=DMRnet/vignettes/getting-started.html).
 
 ## Installing `DMRnet` package
 
-To install the development package version (currently: 0.3.1.9002) please execute
+To install the development package version please execute
 ```
 library(devtools)
 devtools::install_github("SzymonNowakowski/DMRnet")
 ```
 
-Alternatively, to install the current stable CRAN version (currently: 0.3.1) please execute
+Alternatively, to install the current stable CRAN version please execute
 
 ```
 install.packages("DMRnet")

cran-comments.md

@@ -1,9 +1,6 @@
 ## local R CMD check results
 There were no ERRORs, WARNINGs, or NOTEs. 
 
-## R-hub check results
-There was a NOTE related to the maintainer change. As I read over the Internet, the previous maintainer will get an email asking for her consent
-
 ## Downstream dependencies
 There seem to be no downstream dependencies:
 

inst/CITATION

@@ -10,7 +10,7 @@ citEntry(entry="Article",
          volume = "9",
          number = "2",
          pages = "1749-1778",
-         url="https://projecteuclid.org/euclid.ejs/1440507392",
-         textVersion = "Aleksandra Maj-Kańska, Piotr Pokarowski and Agnieszka Prochenka, 2015. Delete or merge regressors for linear model selection. Electronic Journal of Statistics 9(2): 1749-1778. https://projecteuclid.org/euclid.ejs/1440507392")
+         doi = "10.1214/15-EJS1050",
+         textVersion = "Aleksandra Maj-Kańska, Piotr Pokarowski and Agnieszka Prochenka, 2015. Delete or merge regressors for linear model selection. Electronic Journal of Statistics 9(2): 1749-1778. doi:10.1214/15-EJS1050")