testing (#128)

* testing

* closes #126 closes #127

* fix formatting issue in README

* modernize test skipping, remove travis

.Rbuildignore

@@ -1,18 +1,19 @@
-^Meta$
-^doc$
-^.*\.Rproj$
-^\.Rproj\.user$
-^\.travis\.yml$
-^cran-comments\.md$
-.Rhistory
-^README\.Rmd$
-CONTRIBUTING.md
-^revdep$
-^README-.*\.png$
-readmeplot
-.gitignore
-^vignettes/merToolsIntro\.Rmd\.orig$
-^vignettes/marginal_effects\.Rmd\.orig$
-^vignettes/Using_predictInterval\.Rmd\.orig$
-^vignettes/imputation\.Rmd\.orig$
-^CRAN-RELEASE$
+^Meta$
+^doc$
+^.*\.Rproj$
+^\.Rproj\.user$
+^\.travis\.yml$
+^cran-comments\.md$
+.Rhistory
+^README\.Rmd$
+CONTRIBUTING.md
+^revdep$
+^README-.*\.png$
+readmeplot
+.gitignore
+^vignettes/merToolsIntro\.Rmd\.orig$
+^vignettes/marginal_effects\.Rmd\.orig$
+^vignettes/Using_predictInterval\.Rmd\.orig$
+^vignettes/imputation\.Rmd\.orig$
+^CRAN-RELEASE$
+^\.github$

.github/.gitignore

@@ -0,0 +1 @@
+*.html

.github/workflows/check-standard.yaml

@@ -0,0 +1,49 @@
+# 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]
+  pull_request:
+    branches: [main, master]
+
+name: R-CMD-check
+
+jobs:
+  R-CMD-check:
+    runs-on: ${{ matrix.config.os }}
+
+    name: ${{ matrix.config.os }} (${{ matrix.config.r }})
+
+    strategy:
+      fail-fast: false
+      matrix:
+        config:
+          - {os: macos-latest,   r: 'release'}
+          - {os: windows-latest, r: 'release'}
+          - {os: ubuntu-latest,   r: 'devel', http-user-agent: 'release'}
+          - {os: ubuntu-latest,   r: 'release'}
+          - {os: ubuntu-latest,   r: 'oldrel-1'}
+
+    env:
+      GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }}
+      R_KEEP_PKG_SOURCE: yes
+
+    steps:
+      - uses: actions/checkout@v3
+
+      - 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-r-dependencies@v2
+        with:
+          extra-packages: any::rcmdcheck
+          needs: check
+
+      - uses: r-lib/actions/check-r-package@v2
+        with:
+          upload-snapshots: true

DESCRIPTION

@@ -1,43 +1,43 @@
-Package: merTools
-Title: Tools for Analyzing Mixed Effect Regression Models
-Version: 0.6.0
-Authors@R: c(
-    person(c("Jared", "E."), "Knowles", email = "jknowles@gmail.com", role = c("aut", "cre")),
-    person("Carl", "Frederick", email="carlbfrederick@gmail.com", role = c("aut")),
-    person("Alex", "Whitworth", email="whitworth.alex@gmail.com", role = c("ctb")))
-Description: Provides methods for extracting results from mixed-effect model
-    objects fit with the 'lme4' package. Allows construction of prediction intervals
-    efficiently from large scale linear and generalized linear mixed-effects models.
-    This method draws from the simulation framework used in the Gelman and Hill (2007) textbook:
-    Data Analysis Using Regression and Multilevel/Hierarchical Models.
-Depends:
-    R (>= 3.0.2),
-    arm,
-    lme4 (>= 1.1-11),
-    methods
-Suggests:
-    testthat,
-    knitr,
-    rmarkdown,
-    parallel,
-    nlme,
-    future.apply,
-    rstanarm,
-    Amelia,
-    DT
-Imports:
-    dplyr,
-    mvtnorm,
-    foreach,
-    shiny,
-    abind,
-    ggplot2,
-    blme,
-    broom.mixed,
-    Matrix
-License: GPL (>=2)
-LazyData: true
-VignetteBuilder: knitr
-RoxygenNote: 7.2.0
-Encoding: UTF-8
-BugReports: https://www.github.com/jknowles/merTools
+Package: merTools
+Title: Tools for Analyzing Mixed Effect Regression Models
+Version: 0.6.1
+Authors@R: c(
+    person(c("Jared", "E."), "Knowles", email = "jared@civilytics.com", role = c("aut", "cre")),
+    person("Carl", "Frederick", email="carlbfrederick@gmail.com", role = c("aut")),
+    person("Alex", "Whitworth", email="whitworth.alex@gmail.com", role = c("ctb")))
+Description: Provides methods for extracting results from mixed-effect model
+    objects fit with the 'lme4' package. Allows construction of prediction intervals
+    efficiently from large scale linear and generalized linear mixed-effects models.
+    This method draws from the simulation framework used in the Gelman and Hill (2007) textbook:
+    Data Analysis Using Regression and Multilevel/Hierarchical Models.
+Depends:
+    R (>= 3.0.2),
+    arm,
+    lme4 (>= 1.1-11),
+    methods
+Suggests:
+    testthat,
+    knitr,
+    rmarkdown,
+    parallel,
+    nlme,
+    future.apply,
+    rstanarm,
+    Amelia,
+    DT
+Imports:
+    dplyr,
+    mvtnorm,
+    foreach,
+    shiny,
+    abind,
+    ggplot2,
+    blme,
+    broom.mixed,
+    Matrix
+License: GPL (>=2)
+LazyData: true
+VignetteBuilder: knitr
+RoxygenNote: 7.2.3
+Encoding: UTF-8
+BugReports: https://github.com/jknowles/merTools

NEWS.md

@@ -1,5 +1,14 @@
 # NEWS
 
+## merTools 0.6.1
+
+- Maintenance release to keep package listed on CRAN
+- Fix a small bug where parallel code path is run twice (#126)
+- Update plotting functions to avoid deprecated `aes_string()` calls (#127)
+- Fix (#115) in description
+- Speed up PI using @bbolker pull request (#120)
+- Updated package maintainer contact information
+
 ## merTools 0.5.2
 
 - Streamline vignette building to be precompiled and move tests to limit burden on CRAN check

R/REmargins.R

@@ -71,7 +71,7 @@
 #' Gatz, DF and Smith, L. The Standard Error of a Weighted Mean Concentration.
 #' I. Bootstrapping vs other methods. \emph{Atmospheric Environment}.
 #' 1995;11(2)1185-1193. Available at
-#' \url{http://www.sciencedirect.com/science/article/pii/135223109400210C}
+#' \url{https://www.sciencedirect.com/science/article/pii/135223109400210C}
 #'
 #' Cochran, WG. 1977. Sampling Techniques (3rd Edition). Wiley, New York.
 #'

R/merData.R

@@ -435,7 +435,7 @@ REquantile <- function(merMod, quantile, groupFctr, term = "(Intercept)"){
     stop("Random effect group name not found. Please respecify grouping factor.")
   }
   myRE.tmp <- try(myRE[order(myRE[, term]), ,drop = FALSE], silent = TRUE)
-  if(class(myRE.tmp) != "data.frame"){
+  if(!is(myRE.tmp, "data.frame")){
     term1 <- names(myRE)[1]
     myRE.tmp <- try(myRE[order(myRE[, term1]), ,drop = FALSE], silent = TRUE)
     warning(paste0(term, " not found in random effect terms. Returning first term, ",

R/merList.R

@@ -197,7 +197,7 @@ ranef.merModList <- function(object, ...){
 #' fml <- "Reaction ~ Days + (Days | Subject)"
 #' mod <- lmerModList(fml, data = sim_list)
 #' VarCorr(mod)
-VarCorr.merModList <- function(x, sigma = 1, rdig = 3L){
+VarCorr.merModList <- function(x, sigma = 1, rdig = 3L, ...){
   modList <- x
   ngrps <- length(VarCorr(modList[[1]]))
   errorList <- vector(mode = 'list', length = ngrps)
@@ -386,12 +386,13 @@ print.summary.merModList <- function(x, ...){
 #' summary(mod)
 #' }
 lmerModList <- function(formula, data, parallel = FALSE, ...){
-  if(parallel) {
+  if (parallel) {
     if (requireNamespace("future.apply", quietly=TRUE)) {
       ml <- future.apply::future_lapply(data, function(d) lmer(formula, data = d, ...))
+    } else {
+      warning("Parallel set but future.apply not available. Running sequentially.")
+      ml <- lapply(data, function(d) lmer(formula, data = d, ...))
     }
-    warning("Parallel set but future.apply not available. Running sequentially.")
-    ml <- lapply(data, function(d) lmer(formula, data = d, ...))
   } else {
     ml <- lapply(data, function(d) lmer(formula, data = d, ...))
   }

R/merPlots.R

@@ -69,8 +69,8 @@ plotREsim <- function(data, level = 0.95, stat = "median", sd = TRUE,
     xlabs.tmp <- element_blank()
   }
 
-  p <- ggplot(data, aes_string(x = "xvar", y = stat, ymax = "ymax", ymin = "ymin")) +
-         geom_hline(yintercept = hlineInt, color = I("red"), size = I(1.1)) +
+  p <- ggplot(data, aes(x = .data[["xvar"]], y = .data[[stat]], ymax = .data[["ymax"]], ymin = .data[["ymin"]])) +
+         geom_hline(yintercept = hlineInt, color = I("red"), linewidth = I(1.1)) +
          geom_point(color="gray75", alpha=1/(nrow(data)^.33), size=I(0.5)) +
          geom_point(data=subset(data, sig==TRUE), size=I(3)) +
          labs(x = "Group", y = "Effect Range", title = "Effect Ranges") +
@@ -140,7 +140,7 @@ plotFEsim <- function(data, level=0.95, stat = "median", sd = TRUE,
   xvar <- "term"
   data$term <- as.character(data$term)
   data$term <- factor(data$term , levels = data[order(data[, stat]), 1])
-  p <- ggplot(aes_string(x = xvar, y = stat, ymax = "ymax", ymin = "ymin"), data = data) +
+  p <- ggplot(aes(x = .data[[xvar]], y = .data[[stat]], ymax = .data[["ymax"]], ymin = .data[["ymin"]]), data = data) +
     geom_hline(yintercept = hlineInt, color = I("red")) +
     geom_point(size=I(3)) +
     coord_flip() +

README.Rmd

@@ -5,20 +5,19 @@ output:
 ---
 
 
-[![Travis-CI Build Status](https://travis-ci.org/jknowles/merTools.png?branch=master)](https://travis-ci.org/jknowles/merTools)
-[![Coverage Status](https://coveralls.io/repos/jknowles/merTools/badge.svg?branch=master)](https://coveralls.io/r/jknowles/merTools?branch=master)
 [![CRAN_Status_Badge](http://www.r-pkg.org/badges/version/merTools)](https://cran.r-project.org/package=merTools)
 [![Downloads](http://cranlogs.r-pkg.org/badges/merTools)](https://cran.r-project.org/package=merTools)
 [![Downloads](http://cranlogs.r-pkg.org/badges/grand-total/merTools)](https://cran.r-project.org/package=merTools)
 
 <!-- README.md is generated from README.Rmd. Please edit that file -->
 
-```{r, echo = FALSE}
+```{r, include=FALSE}
 knitr::opts_chunk$set(
   collapse = TRUE, echo = TRUE,
   comment = "#>",
   fig.path = "man/figures/README_"
 )
+library(merTools)
 ```
 
 # merTools
@@ -48,6 +47,15 @@ install.packages("merTools")
 
 ## Recent Updates
 
+## merTools 0.6.1 (Spring 2023)
+
+- Maintenance release to keep package listed on CRAN
+- Fix a small bug where parallel code path is run twice (#126)
+- Update plotting functions to avoid deprecated `aes_string()` calls (#127)
+- Fix (#115) in description
+- Speed up PI using @bbolker pull request (#120)
+- Updated package maintainer contact information
+
 ### merTools 0.5.0
 
 #### New Features
@@ -75,42 +83,20 @@ correction for multiple imputation
 - Contribution by Alex Whitworth (@alexWhitworth) adding error checking to plotting
 functions
 
-### merTools 0.4.0
-
-#### New Features
-- Added vignette on using multilevel models with multiply imputed data
-- Added `fixef` and `ranef` generics for `merModList` objects
-- Added `fastdisp` generic for `merModList`
-- Added `summary` generic for `merModList`
-- Added `print` generic for `merModList`
-- Documented all generics for `merModList` including examples and a new 
-imputation vignette
-- Added `modelInfo` generic for `merMod` objects that provides simple summary 
-stats about a whole model
-
-#### Bug Fixes
-- Fix bug that returned NaN for `std.error` of a multiply imputed `merModList` 
-when calling `modelRandEffStats`
-- Fixed bug in `REimpact` where some column names in `newdata` would prevent the 
-prediction intervals from being computed correctly. Users will now be warned.
-- Fixed bug in `wiggle` where documentation incorrectly stated the arguments to 
-the function and the documentation did not describe function correctly
-
-See [NEWS.md](https://github.com/jknowles/merTools/blob/master/NEWS.md) for 
-more details. 
 
 ## Shiny App and Demo
 
 The easiest way to demo the features of this application is to use the bundled 
 Shiny application which launches a number of the metrics here to aide in exploring 
 the model. To do this:
 
-```{r, echo = TRUE, eval=FALSE}
+```
 library(merTools)
 m1 <- lmer(y ~ service + lectage + studage + (1|d) + (1|s), data=InstEval)
 shinyMer(m1, simData = InstEval[1:100, ]) # just try the first 100 rows of data
 ```
 
+
 ```{r, include=FALSE}
 library(merTools)
 m1 <- lmer(y ~ service + lectage + studage + (1|d) + (1|s), data=InstEval)
@@ -159,7 +145,6 @@ With `predictInterval` we obtain predictions that are more like the standard
 objects produced by `lm` and `glm`:
 
 ```{r}
-#predictInterval(m1, newdata = InstEval[1:10, ]) # all other parameters are optional
 predictInterval(m1, newdata = InstEval[1:10, ], n.sims = 500, level = 0.9, 
                 stat = 'median')
 ```