230223: updated github actions

AnestisTouloumis · Feb 23, 2023 · 4074876 · 4074876
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diff --git a/.github/workflows/R-CMD-check.yaml b/.github/workflows/R-CMD-check.yaml
@@ -1,14 +1,10 @@
-# For help debugging build failures open an issue on the RStudio community with the 'github-actions' tag.
-# https://community.rstudio.com/new-topic?category=Package%20development&tags=github-actions
+# Workflow derived from https://github.com/r-lib/actions/tree/v2/examples
+# Need help debugging build failures? Start at https://github.com/r-lib/actions#where-to-find-help
 on:
   push:
-    branches:
-      - main
-      - master
+    branches: [main, master]
   pull_request:
-    branches:
-      - main
-      - master
+    branches: [main, master]
 
 name: R-CMD-check
 
@@ -22,63 +18,35 @@ jobs:
       fail-fast: false
       matrix:
         config:
+          - {os: macos-latest,   r: 'release'}
           - {os: windows-latest, r: 'release'}
-          - {os: macOS-latest, r: 'release'}
-          - {os: ubuntu-20.04, r: 'release', rspm: "https://packagemanager.rstudio.com/cran/__linux__/focal/latest"}
-          - {os: ubuntu-20.04, r: 'devel', rspm: "https://packagemanager.rstudio.com/cran/__linux__/focal/latest"}
+          - {os: ubuntu-latest,   r: 'devel', http-user-agent: 'release'}
+          - {os: ubuntu-latest,   r: 'release'}
+          - {os: ubuntu-latest,   r: 'oldrel-1'}
 
     env:
-      R_REMOTES_NO_ERRORS_FROM_WARNINGS: true
-      RSPM: ${{ matrix.config.rspm }}
       GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }}
+      R_KEEP_PKG_SOURCE: yes
 
     steps:
-      - uses: actions/checkout@v2
+      - uses: actions/checkout@v3
 
-      - uses: r-lib/actions/setup-r@v1
+      - uses: r-lib/actions/setup-pandoc@v2
+
+      - uses: r-lib/actions/setup-r@v2
         with:
           r-version: ${{ matrix.config.r }}
+          http-user-agent: ${{ matrix.config.http-user-agent }}
+          use-public-rspm: true
 
-      - uses: r-lib/actions/setup-pandoc@v1
-
-      - name: Query dependencies
-        run: |
-          install.packages('remotes')
-          saveRDS(remotes::dev_package_deps(dependencies = TRUE), ".github/depends.Rds", version = 2)
-          writeLines(sprintf("R-%i.%i", getRversion()$major, getRversion()$minor), ".github/R-version")
-        shell: Rscript {0}
-
-      - name: Cache R packages
-        if: runner.os != 'Windows'
-        uses: actions/cache@v2
+      - uses: r-lib/actions/setup-r-dependencies@v2
         with:
-          path: ${{ env.R_LIBS_USER }}
-          key: ${{ runner.os }}-${{ hashFiles('.github/R-version') }}-1-${{ hashFiles('.github/depends.Rds') }}
-          restore-keys: ${{ runner.os }}-${{ hashFiles('.github/R-version') }}-1-
-
-      - name: Install system dependencies
-        if: runner.os == 'Linux'
-        run: |
-          while read -r cmd
-          do
-            eval sudo $cmd
-          done < <(Rscript -e 'writeLines(remotes::system_requirements("ubuntu", "20.04"))')
-
-      - name: Install dependencies
-        run: |
-          remotes::install_deps(dependencies = TRUE)
-          remotes::install_cran("rcmdcheck")
-        shell: Rscript {0}
-
-      - name: Check
-        env:
-          _R_CHECK_CRAN_INCOMING_REMOTE_: false
-        run: rcmdcheck::rcmdcheck(args = c("--no-manual", "--as-cran"), error_on = "warning", check_dir = "check")
-        shell: Rscript {0}
+          extra-packages: any::rcmdcheck
+          needs: check
 
-      - name: Upload check results
-        if: failure()
-        uses: actions/upload-artifact@main
+      - uses: r-lib/actions/check-r-package@v2
         with:
-          name: ${{ runner.os }}-r${{ matrix.config.r }}-results
-          path: check
+          upload-snapshots: true
+          args: 'c("--as-cran")'
+          error-on: '"warning"'
+          check-dir: '"check"'
diff --git a/README.Rmd b/README.Rmd
@@ -32,7 +32,7 @@ knitr::opts_chunk$set(
 # SimCorMultRes: Simulates Correlated Multinomial Responses
 
 [![Github version](`r paste0("https://img.shields.io/badge/GitHub%20-", as.vector(read.dcf('DESCRIPTION')[, 'Version']),"-orange.svg")`)]("commits/master")
-[![R-CMD-check](https://github.com/AnestisTouloumis/SimCorMultRes/workflows/R-CMD-check/badge.svg)](https://github.com/AnestisTouloumis/SimCorMultRes/actions)
+[![R-CMD-check](https://github.com/AnestisTouloumis/SimCorMultRes/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/AnestisTouloumis/SimCorMultRes/actions/workflows/R-CMD-check.yaml)
 [![Project Status: Active The project has reached a stable, usable state and is being actively developed.](http://www.repostatus.org/badges/latest/active.svg)](http://www.repostatus.org/#active) 
 [![Codecov test coverage](https://codecov.io/gh/AnestisTouloumis/SimCorMultRes/branch/master/graph/badge.svg)](https://codecov.io/gh/AnestisTouloumis/SimCorMultRes?branch=master)
 

diff --git a/inst/NEWS.Rd b/inst/NEWS.Rd
@@ -1,13 +1,14 @@
 \name{NEWS}
 \title{NEWS file for the \pkg{SimCorMultRes} package}
 
-\section{Changes in Version 1.8.2 (2022-09-27)}{
+\section{Changes in Version 1.8.3 (2023-02-23)}{
   \subsection{MINOR CHANGES}{
   \itemize{
     \item{Added R journal paper as vignette.}
     \item{Improved README.}
     \item{Improved vignette.}
     \item{Reinstated code coverage using \pkg{covr}.}
+    \item{Updated GitHub Actions.}
     }
     }
     }
@@ -21,7 +22,7 @@
     \item{Removed \pkg{markdown} dependency.}
     \item{Created NEWS.Rd.}
     \item{Fixed ORCID in DESCRIPTION.}
-    \item{Migrating from travis-ci to github actions.}
+    \item{Migrating from Travis-CI to GitHub Actions.}
     }
     }
     }

diff --git a/vignettes/SimCorMultRes.Rmd b/vignettes/SimCorMultRes.Rmd
@@ -473,20 +473,19 @@ apply(simulated_nominal_dataset$Ysim, 2, table) / sample_size
 
 ## Notes on NORTA
 
-The NORTA method, as implemented in `SimCorMultRes`, does not require specification of the correlation matrix of the latent variable. Instead the user defines the correlation matrix of the multivariate normal distribution, say $\mathbf R$, that appears in the intermediate step of the NORTA method. 
+In `SimCorMultRes`, the user specifies the correlation matrix of the multivariate normal distribution, denoted by $\mathbf R$, that is used in the intermediate step of the NORTA method. This is justified by the observation that when all the marginal distributions of the correlated latent variables are logistic, $\mathbf R$ is expected to approximate well their true but unknown correlation matrix [@Touloumis2016].
 
-When the marginal distribution of the correlated responses is the logistic distribution, @Touloumis2016 argued that $\mathbf R$ is expected to approximate well the true but unknown correlation matrix of the correlated responses. 
 
-A simulation study was carried out assess the validity of this claim. For a fixed sample size $N$ and a correlation parameter $\rho$, $N$ independent bivariate random vectors $\mathbf y_{1}, \ldots, \mathbf y_{N}$ from a bivariate normal distribution with mean vector the zero vector and covariance matrix the correlation matrix 
+To evaluate the validity of approximation study, a simulation study was conducted. For a fixed sample size $N$ and a correlation parameter $\rho$, $N$ independent bivariate random vectors $\{\mathbf y_{i}: i = 1, \ldots, N \}$ from a bivariate normal distribution with mean vector the zero vector and covariance matrix the correlation matrix 
 \[
 \mathbf R = \begin{bmatrix}
 1 & \rho\\
 \rho & 1
 \end{bmatrix}
 \]
-were drawn. The sample correlation was used to estimate $\rho$. Next, the NORTA method was applied to obtain bivariate random vectors $\mathbf z_{1}, \ldots, \mathbf z_{N}$ so that their marginal distribution is a logistic distribution. Their correlation parameter, say $\rho_{z}$, was estimated using the sample correlation based on $\mathbf z_{1}, \ldots, \mathbf z_{N}$. Then, the NORTA method was applied to obtain bivariate random vectors $\mathbf w_{1}, \ldots, \mathbf w_{N}$ so that their marginal distribution is the gumbel distribution. Their correlation parameter, say $\rho_{w}$, was estimated using the sample correlation based on $\mathbf w_{1}, \ldots, \mathbf w_{N}$. This procedure was replicated $10,000,000$ times. The three correlation parameters $\rho$, $\rho_z$ and $\rho_w$ were estimated using the corresponding Monte Carlo counterparts $\widehat{\rho}$, $\widehat{\rho}_z$ and $\widehat{\rho}_w$, respectively. To reduce the sample variability, we set $N=10,000$. Finally, we considered $\rho= 0, 0.01,0.02,\ldots, 0.99$.
+were drawn. The sample correlation was used to estimate $\rho$. Next, the NORTA method was applied to obtain bivariate random vectors $\{\mathbf z_{i}: i = 1, \ldots, N \}$ so that their marginal distribution is a logistic distribution. Their correlation parameter, say $\rho_{z}$, was estimated using the corresponding sample correlation. Then, the NORTA method was applied to obtain bivariate random vectors $\{\mathbf w_{i}: i = 1, \ldots, N \}$ so that their marginal distribution is the Gumbel distribution. Their correlation parameter, say $\rho_{w}$, was estimated using their sample correlation. This procedure was replicated $10,000,000$ times. The three correlation parameters $\rho$, $\rho_z$ and $\rho_w$ were estimated using the corresponding Monte Carlo estimates $\widehat{\rho}$, $\widehat{\rho}_z$ and $\widehat{\rho}_w$, respectively. To reduce the sample variability, we set $N=10,000$. Finally, we considered $\rho= 0, 0.01,0.02,\ldots, 0.99$.
 
-The dataframe `simulation` contains the simulation results. As expected, $\widehat{\rho}$ is a good estimator of $\rho$ regardless of the strength of the correlation parameter. For the logistic case, the average difference between $\rho$ and $\widehat{\rho}_z$ is `r rho = simulation$rho; logistic = simulation$logistic; round(mean(rho - logistic), 4)`, taking the maximum value of `r round(max(rho - logistic), 4)` at $\rho = `r rho[which.max(rho - logistic)]`$. These imply that $\rho$ is a good approximation of $\rho_{z}$ (at least in 2 decimal points).  For the gumbel case, the average difference between $\rho$ and $\widehat{\rho}_w$ is `r gumbel = simulation$gumbel; round(mean(rho - gumbel), 4)`, taking the maximum value of `r round(max(rho - gumbel), 4)` at $\rho = `r rho[which.max(rho - gumbel)]`$. Now $\rho$ appears again to approximate $\rho_{w}$ but it less accurate than that of the logistic case. 
+The dataframe `simulation` contains the true correlation parameter $\rho$ and the Monte Carlo estimates $\widehat{\rho}$, $\widehat{\rho}_z$ and $\widehat{\rho}_w$ of the simulation study. As expected, $\widehat{\rho} \approx \rho$ regardless of the strength of the correlation parameter. For the logistic case, the average difference between $\rho$ and $\widehat{\rho}_z$ is `r rho = simulation$rho; logistic = simulation$logistic; round(mean(rho - logistic), 4)`, taking the maximum value of `r round(max(rho - logistic), 4)` at $\rho = `r rho[which.max(rho - logistic)]`$. Thus, $\rho$ appears to approximate $\rho_{z}$ 2 decimal points.  For the Gumbel case, the average difference between $\rho$ and $\widehat{\rho}_w$ is `r gumbel = simulation$gumbel; round(mean(rho - gumbel), 4)`, taking the maximum value of `r round(max(rho - gumbel), 4)` at $\rho = `r rho[which.max(rho - gumbel)]`$. Again, $\rho$ appears to approximate $\rho_{w}$ but there is some accuracy loss compared to $\rho_{z}$. 
 
 
 ```{r echo = FALSE, fig.cap= "Difference between the correlation parameters of the bivariate normal distribution and of the latent variables for three different marginal distributions."}