Merge pull request #59 from tlverse/devel

.Rbuildignore

@@ -1,5 +1,6 @@
 ^.*\.Rproj$
 ^\.Rproj\.user$
+^\.github$
 ^\.travis\.yml$
 ^appveyor\.yml$
 ^codecov\.yml$

.github/.gitignore

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

.github/workflows/R-CMD-check.yml

@@ -0,0 +1,66 @@
+on:
+  push:
+    branches:
+      - master
+      - devel
+  pull_request:
+    branches:
+      - master
+      - devel
+
+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: ubuntu-latest, r: 'release'}
+          - {os: macOS-latest, r: 'release'}
+          - {os: windows-latest, r: 'release'}
+
+    env:
+      R_REMOTES_NO_ERRORS_FROM_WARNINGS: true
+      GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }}
+
+    steps:
+      - name: Checkout repo
+        uses: actions/checkout@v2
+
+      - name: Setup R
+        uses: r-lib/actions/setup-r@master
+        with:
+          r-version: ${{ matrix.config.r }}
+
+      - name: Install pandoc
+        uses: r-lib/actions/setup-pandoc@v1
+
+      - name: Install tinyTeX
+        uses: r-lib/actions/setup-tinytex@v1
+
+      - name: Install system dependencies
+        if: runner.os == 'Linux'
+        run: |
+          sudo apt install -y curl libcurl4-doc libcurl4-openssl-dev
+
+      - name: Install package dependencies
+        run: |
+          install.packages(c("remotes", "rcmdcheck", "covr", "sessioninfo"))
+          remotes::install_deps(dependencies = TRUE)
+        shell: Rscript {0}
+
+      - name: Check package
+        run: |
+          options(crayon.enabled = TRUE)
+          rcmdcheck::rcmdcheck(args = "--no-manual", error_on = "error")
+        shell: Rscript {0}
+
+      - name: Upload code coverage
+        if: runner.os == 'Linux'
+        run: |
+          covr::codecov()
+        shell: Rscript {0}

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: origami
 Title: Generalized Framework for Cross-Validation
-Version: 1.0.4
+Version: 1.0.5
 Authors@R: c(
     person("Jeremy", "Coyle", email = "jeremyrcoyle@gmail.com",
            role = c("aut", "cre", "cph"),
@@ -39,8 +39,9 @@ Suggests:
     rmarkdown,
     knitr,
     stringr,
+    glmnet,
     forecast,
     randomForest
 LazyData: true
 VignetteBuilder: knitr
-RoxygenNote: 7.1.1.9000
+RoxygenNote: 7.1.2

NEWS.md

@@ -1,3 +1,8 @@
+# origami 1.0.5
+* Addition of `glmnet` to package `Suggests` since used in the vignette.
+* Resolve issue stemming from the partial matching of argument names
+  (https://github.com/tlverse/origami/issues/56) by specifying throughout.
+
 # origami 1.0.4
 * Fix the incorrect use of `future.apply` by including `future.seed = TRUE`,
    as per https://github.com/tlverse/origami/issues/48.
@@ -6,10 +11,10 @@
 * Sped up all pooled time-series fold functions with `lapply`.
 * All pooled time-series fold functions (`folds_rolling_origin_pooled`,
    `folds_rolling_window_pooled`, `folds_vfold_rolling_origin_pooled`,
-   `folds_vfold_rolling_window_pooled`) now allow for variability in the 
-   number of observations for each independent unit (i.e. subject).
+   `folds_vfold_rolling_window_pooled`) now allow for variability in the
+   number of observations for each independent unit (i.e., the subject).
 * Added test for pooled time-series cross-validation with multi-unit time-series
-   to test the update above. In this test, the total number of observations 
+   to test the update above. In this test, the total number of observations
    and the time-points for which there are observations varies across the units.
 
 # origami 1.0.3

R/fold_funs.R

@@ -45,7 +45,7 @@ folds_vfold <- function(n, V = 10L) {
     warning("n <= V so using leave-one-out CV")
     return(folds_loo(n))
   }
-  folds <- rep(seq_len(V), length = n)
+  folds <- rep(x = seq_len(V), length.out = n)
 
   # shuffle folds
   folds <- sample(folds)
@@ -157,7 +157,7 @@ folds_rolling_origin_pooled <- function(n, t, id = NULL, time = NULL,
 
   # make IDs the same length as time if only one ID provided
   if (length(id) == 1L) {
-    id <- rep(id, length(time))
+    id <- rep(x = id, length.out = length(time))
   }
 
   # make skeleton dataset with IDs and times
@@ -202,7 +202,7 @@ folds_rolling_window_pooled <- function(n, t, id = NULL, time = NULL,
 
   # make IDs the same length as time if only one ID provided
   if (length(id) == 1L) {
-    id <- rep(id, length(time))
+    id <- rep(x = id, length.out = length(time))
   }
 
   # make skeleton dataset with IDs and times
@@ -249,7 +249,7 @@ folds_vfold_rolling_origin_pooled <- function(n, t, id = NULL, time = NULL,
 
   # make IDs the same length as time if only one ID provided
   if (length(id) == 1L) {
-    id <- rep(id, length(time))
+    id <- rep(x = id, length.out = length(time))
   }
 
   # make skeleton dataset with IDs and times
@@ -268,7 +268,7 @@ folds_vfold_rolling_origin_pooled <- function(n, t, id = NULL, time = NULL,
   message(paste("Processing", length(ids), "samples with", t, "time points."))
 
   # establish V folds for cross-validating ids
-  Vfold_allocation <- sample(rep(seq_len(V), length = length(ids)))
+  Vfold_allocation <- sample(rep(x = seq_len(V), length.out = length(ids)))
   Vfolds_skeleton <- lapply(seq_len(V), fold_from_foldvec, Vfold_allocation)
 
   # establish rolling origin forecast for time-series cross-validation
@@ -316,7 +316,7 @@ folds_vfold_rolling_window_pooled <- function(n, t, id = NULL, time = NULL,
 
   # make IDs the same length as time if only one ID provided
   if (length(id) == 1) {
-    id <- rep(id, length(time))
+    id <- rep(x = id, length.out = length(time))
   }
 
   # make skeleton dataset with IDs and times
@@ -335,7 +335,7 @@ folds_vfold_rolling_window_pooled <- function(n, t, id = NULL, time = NULL,
   message(paste("Processing", length(ids), "samples with", t, "time points."))
 
   # establish V folds for cross-validating ids
-  Vfold_allocation <- sample(rep(seq_len(V), length = length(ids)))
+  Vfold_allocation <- sample(rep(x = seq_len(V), length.out = length(ids)))
   Vfolds_skeleton <- lapply(seq_len(V), fold_from_foldvec, Vfold_allocation)
 
   # establish rolling origin forecast for time-series cross-validation

README.Rmd

@@ -14,8 +14,7 @@ knitr::opts_chunk$set(
 
 # R/`origami` <img src="./hex/origami-sticker.png" align="right" width='125'/>
 
-[![Travis-CI Build Status](https://travis-ci.org/tlverse/origami.svg?branch=master)](https://travis-ci.org/tlverse/origami)
-[![Build status](https://ci.appveyor.com/api/projects/status/bfe2jd9a065jhql7?svg=true)](https://ci.appveyor.com/project/tlverse/origami)
+[![R-CMD-check](https://github.com/tlverse/origami/workflows/R-CMD-check/badge.svg)](https://github.com/tlverse/origami/actions)
 [![Coverage Status](https://codecov.io/gh/tlverse/origami/branch/master/graph/badge.svg)](https://codecov.io/gh/tlverse/origami)
 [![CRAN](http://www.r-pkg.org/badges/version/origami)](http://www.r-pkg.org/pkg/origami)
 [![CRAN downloads](https://cranlogs.r-pkg.org/badges/origami)](https://CRAN.R-project.org/package=origami)
@@ -34,11 +33,11 @@ Phillips](https://github.com/rachaelvphillips)
 
 ---
 
-## Description
+## What's `origami`?
 
-`origami` is an R package that provides a general framework for the application
-of cross-validation schemes to particular functions. By allowing arbitrary lists
-of results, `origami` accommodates a range of cross-validation applications.
+The `origami` R package provides a general framework for the application of
+cross-validation schemes to particular functions. By allowing arbitrary lists of
+results, `origami` accommodates a range of cross-validation applications.
 
 ---
 
@@ -124,10 +123,8 @@ issue](https://github.com/tlverse/origami/issues).
 
 ## Contributions
 
-It is our hope that `origami` will grow to be adopted as a backend for most any
-procedure requiring cross-validation, including its integration into larger
-machine learning frameworks. To that end, contributions are very welcome, though
-we ask that interested contributors consult our [contribution
+Contributions are very welcome. Interested contributors should consult our
+[contribution
 guidelines](https://github.com/tlverse/origami/blob/master/CONTRIBUTING.md)
 prior to submitting a pull request.
 
@@ -154,7 +151,7 @@ After using the `origami` R package, please cite it:
 
 ## License
 
-&copy; 2017-2020 [Jeremy R. Coyle](https://github.com/jeremyrcoyle)
+&copy; 2017-2021 [Jeremy R. Coyle](https://github.com/jeremyrcoyle)
 
 The contents of this repository are distributed under the GPL-3 license. See
 file `LICENSE` for details.

README.md

@@ -3,10 +3,7 @@
 
 # R/`origami` <img src="./hex/origami-sticker.png" align="right" width='125'/>
 
-[![Travis-CI Build
-Status](https://travis-ci.org/tlverse/origami.svg?branch=master)](https://travis-ci.org/tlverse/origami)
-[![Build
-status](https://ci.appveyor.com/api/projects/status/bfe2jd9a065jhql7?svg=true)](https://ci.appveyor.com/project/tlverse/origami)
+[![R-CMD-check](https://github.com/tlverse/origami/workflows/R-CMD-check/badge.svg)](https://github.com/tlverse/origami/actions)
 [![Coverage
 Status](https://codecov.io/gh/tlverse/origami/branch/master/graph/badge.svg)](https://codecov.io/gh/tlverse/origami)
 [![CRAN](http://www.r-pkg.org/badges/version/origami)](http://www.r-pkg.org/pkg/origami)
@@ -32,11 +29,11 @@ Phillips](https://github.com/rachaelvphillips)
 
 -----
 
-## Description
+## What’s `origami`?
 
-`origami` is an R package that provides a general framework for the
-application of cross-validation schemes to particular functions. By
-allowing arbitrary lists of results, `origami` accommodates a range of
+The `origami` R package provides a general framework for the application
+of cross-validation schemes to particular functions. By allowing
+arbitrary lists of results, `origami` accommodates a range of
 cross-validation applications.
 
 -----
@@ -78,7 +75,7 @@ estimate of the mean:
 ``` r
 library(stringr)
 library(origami)
-#> origami v1.0.3: Generalized Framework for Cross-Validation
+#> origami v1.0.4: Generalized Framework for Cross-Validation
 set.seed(4795)
 
 data(mtcars)
@@ -90,7 +87,6 @@ head(mtcars)
 #> Hornet 4 Drive    21.4   6  258 110 3.08 3.215 19.44  1  0    3    1
 #> Hornet Sportabout 18.7   8  360 175 3.15 3.440 17.02  0  0    3    2
 #> Valiant           18.1   6  225 105 2.76 3.460 20.22  1  0    3    1
-
 # build a cv_fun that wraps around lm
 cv_lm <- function(fold, data, reg_form) {
   # get name and index of outcome variable from regression formula
@@ -133,11 +129,8 @@ If you encounter any bugs or have any specific feature requests, please
 
 ## Contributions
 
-It is our hope that `origami` will grow to be adopted as a backend for
-most any procedure requiring cross-validation, including its integration
-into larger machine learning frameworks. To that end, contributions are
-very welcome, though we ask that interested contributors consult our
-[contribution
+Contributions are very welcome. Interested contributors should consult
+our [contribution
 guidelines](https://github.com/tlverse/origami/blob/master/CONTRIBUTING.md)
 prior to submitting a pull request.
 
@@ -166,7 +159,7 @@ After using the `origami` R package, please cite it:
 
 ## License
 
-© 2017-2020 [Jeremy R. Coyle](https://github.com/jeremyrcoyle)
+© 2017-2021 [Jeremy R. Coyle](https://github.com/jeremyrcoyle)
 
 The contents of this repository are distributed under the GPL-3 license.
 See file `LICENSE` for details.