2: add basic check workflow (#33)

closes #2

Author: danielinteractive

.Rbuildignore

@@ -10,3 +10,5 @@
 ^cran-comments\.md$
 ^CRAN-SUBMISSION$
 ^\.github$
+^air.toml$
+^\.lintr$

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

@@ -0,0 +1,33 @@
+# 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:
+
+name: R-CMD-check.yaml
+
+permissions: read-all
+
+jobs:
+  R-CMD-check:
+    runs-on: ubuntu-latest
+    env:
+      GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }}
+      R_KEEP_PKG_SOURCE: yes
+    steps:
+      - uses: actions/checkout@v4
+
+      - uses: r-lib/actions/setup-r@v2
+        with:
+          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
+          build_args: 'c("--no-manual","--compact-vignettes=gs+qpdf")'

.gitignore

@@ -12,3 +12,4 @@ src/*.dll
 inst/doc
 docs
 revdep
+tests/testthat/Rplots.pdf

R/Mods.R

@@ -201,7 +201,6 @@ Mods <- function(..., doses, placEff = 0, maxEff, direction = c("increasing", "d
 
 #' Extract mean response from set of dose-response models
 #'
-#' @inheritParams Mods
 #' @param fmodels An object of class Mods
 #'
 #' @rdname Mods
@@ -294,7 +293,6 @@ plotMods <- function(ModsObj, nPoints = 200, superpose = FALSE,
 
 #' Plot dose-response models
 #'
-#' @inheritParams plotMods
 #' @param Delta Delta: The target effect size use for the target dose (TD)
 #' (Delta should be > 0).
 #' @param x Object of class Mods with type Mods
@@ -486,8 +484,6 @@ TD <- function(object, Delta, TDtype = c("continuous", "discrete"),
 
 #' #' Calculate effective dose for a dose-response model
 #'
-#' @inheritParams targdose
-#'
 #' @rdname targdose
 #' @export
 ED <- function(object, p, EDtype = c("continuous", "discrete"),

R/bFitMod.R

@@ -233,7 +233,6 @@ print.bFitMod <- function(x, digits = 3, ...){
 
 #' Make predictions from fitted dose-response model
 #'
-#' @inheritParams coef.bFitMod
 #' @param predType,summaryFct,doseSeq,lenSeq Arguments for the predict method.
 #'
 #'   \samp{predType}: predType determines whether predictions are returned for the dose-response curve or the effect
@@ -286,7 +285,6 @@ predict.bFitMod <- function(object, predType = c("full-model", "effect-curve"),
 
 #' Plot fitted dose-response model
 #'
-#' @inheritParams coef.bFitMod
 #' @param plotType,quant,plotData,level,lenDose Arguments for plot method.
 #'
 #'   \samp{plotType}: Determining whether the dose-response curve or the effect curve should be plotted.

R/drmodels.R

@@ -14,7 +14,6 @@ emax <-  function(dose, e0, eMax, ed50){
 }
 
 #' @rdname drmodels
-#' @inheritParams emax
 #' @param ...  Just included for convenience in the gradient functions, so that
 #' for example \code{quadratic(dose, e0=0, b1=1, b2=3)} will not throw an error
 #' (although the gradient of the quadratic model is independent of e0, b1 and
@@ -26,7 +25,6 @@ emaxGrad <- function(dose, eMax, ed50, ...){
 }
 
 #' @rdname drmodels
-#' @inheritParams emax
 #' @param h Hill parameter, determining the steepness of the model at the ED50
 #' @usage NULL
 #' @export 
@@ -35,7 +33,6 @@ sigEmax <- function(dose, e0, eMax, ed50, h){
 }
 
 #' @rdname drmodels
-#' @inheritParams sigEmax
 #' @usage NULL
 #' @export 
 sigEmaxGrad <- function(dose, eMax, ed50, h, ...){
@@ -48,7 +45,6 @@ sigEmaxGrad <- function(dose, eMax, ed50, h, ...){
 }
 
 #' @rdname drmodels
-#' @inheritParams emax
 #' @param e1 Slope parameter for exponential model
 #' @param delta Exponential model: Parameter, controlling the convexity of the
 #' model.\cr Linear and linlog model: Slope parameter\cr Logistic model:
@@ -60,15 +56,13 @@ exponential <- function(dose, e0, e1, delta){
 }
 
 #' @rdname drmodels
-#' @inheritParams exponential
 #' @usage NULL
 #' @export 
 exponentialGrad <- function(dose, e1, delta, ...){
   cbind(e0=1, e1=exp(dose/delta)-1, delta=-exp(dose/delta) * dose * e1/delta^2)
 }
 
 #' @rdname drmodels
-#' @inheritParams emax
 #' @param b1 first parameter of quadratic model
 #' @param b2 second parameter of quadratic model (controls, whether model is
 #' convex or concave)
@@ -79,15 +73,13 @@ quadratic <- function(dose, e0, b1, b2){
 }
 
 #' @rdname drmodels
-#' @inheritParams quadratic
 #' @usage NULL
 #' @export 
 quadraticGrad <- function(dose, ...){
   cbind(e0=1, b1 = dose, b2 = dose^2)
 }
 
 #' @rdname drmodels
-#' @inheritParams emax
 #' @param delta1 delta1 parameter for beta model
 #' @param delta2 delta2 parameter for beta model
 #' @param scal Scale parameter (treated as a fixed value, not estimated)
@@ -101,7 +93,6 @@ betaMod <- function(dose, e0, eMax, delta1, delta2, scal){
 }
 
 #' @rdname drmodels
-#' @inheritParams betaMod
 #' @usage NULL
 #' @export 
 betaModGrad <- function(dose, eMax, delta1, delta2, scal, ...){
@@ -119,23 +110,20 @@ betaModGrad <- function(dose, eMax, delta1, delta2, scal, ...){
 }
 
 #' @rdname drmodels
-#' @inheritParams exponential
 #' @usage NULL
 #' @export 
 linear <- function(dose, e0, delta){
   e0 + delta * dose
 }
 
 #' @rdname drmodels
-#' @inheritParams linear
 #' @usage NULL
 #' @export 
 linearGrad <- function(dose, ...){
   cbind(e0=1, delta=dose)
 }
 
 #' @rdname drmodels
-#' @inheritParams exponential
 #' @param off Offset value to avoid problems with dose=0 (treated as a fixed
 #' value, not estimated)
 #' @usage NULL
@@ -145,15 +133,13 @@ linlog <- function(dose, e0, delta, off = 1){
 }
 
 #' @rdname drmodels
-#' @inheritParams linlog
 #' @usage NULL
 #' @export 
 linlogGrad <- function(dose, off, ...){
   cbind(e0=1, delta=log(dose+off))
 }
 
 #' @rdname drmodels
-#' @inheritParams emax
 #' @param delta Exponential model: Parameter, controlling the convexity of the
 #' model.\cr Linear and linlog model: Slope parameter\cr Logistic model:
 #' Parameter controlling determining the steepness of the curve
@@ -164,7 +150,6 @@ logistic <- function(dose, e0, eMax, ed50, delta){
 }
 
 #' @rdname drmodels
-#' @inheritParams logistic
 #' @usage NULL
 #' @export 
 logisticGrad <- function(dose, eMax, ed50, delta, ...){
@@ -175,7 +160,6 @@ logisticGrad <- function(dose, eMax, ed50, delta, ...){
 }
 
 #' @rdname drmodels
-#' @inheritParams emax
 #' @param resp Response values at the nodes for the linInt model
 #' @param nodes Interpolation nodes for the linear interpolation for the linInt
 #' model (treated as a fixed value, not estimated)
@@ -188,7 +172,6 @@ linInt <- function(dose, resp, nodes){
 }
 
 #' @rdname drmodels
-#' @inheritParams linInt
 #' @usage NULL
 #' @export 
 linIntGrad <- function(dose, resp, nodes, ...){

R/fitMod.R

@@ -381,8 +381,6 @@ coef.DRMod <- function(object, sep = FALSE, ...){
 
 #' Extract dose-response vcov matrix
 #'
-#' @inheritParams coef.DRMod
-#'
 #' @rdname fitMod
 #' @method vcov DRMod
 #' @export
@@ -451,7 +449,6 @@ vcov.DRMod <- function(object, ...){
 
 #'Make predictions from dose-response model
 #'
-#'@inheritParams coef.DRMod
 #'@param predType,newdata,doseSeq,se.fit predType determines whether predictions are returned for the full model
 #'  (including potential covariates), the ls-means (SAS type) or the effect curve (difference to placebo).
 #'
@@ -779,7 +776,6 @@ predict.DRMod <- function(object, predType = c("full-model", "ls-means", "effect
 
 #'Plot fitted dose-response model
 #'
-#'@inheritParams coef.DRMod
 #'@param CI,level,plotData,plotGrid,colMn,colFit Arguments for plot method: \samp{CI} determines whether confidence
 #'  intervals should be plotted. \samp{level} determines the level of the confidence intervals. \samp{plotData}
 #'  determines how the data are plotted: Either as means or as means with CI, raw data or none. In case of \samp{type =
@@ -798,8 +794,6 @@ plot.DRMod <- function(x, CI = FALSE, level = 0.95,
 
 #' Extract log-likelihood of dose-response model
 #'
-#' @inheritParams coef.DRMod
-#'
 #' @rdname fitMod
 #' @method logLik DRMod
 #' @export
@@ -822,7 +816,6 @@ logLik.DRMod <- function(object, ...){
 
 #' Extract AIC of dose-response model
 #'
-#' @inheritParams coef.DRMod
 #' @param k Penalty to use for model-selection criterion (AIC uses 2, BIC uses log(n)).
 #'
 #' @rdname fitMod
@@ -839,8 +832,6 @@ AIC.DRMod <- function(object, ..., k = 2){
 
 #' Extract gAIC of dose-response model
 #'
-#' @inheritParams AIC.DRMod
-#'
 #' @rdname fitMod
 #' @method gAIC DRMod
 #' @export

R/optContr.R

@@ -222,7 +222,6 @@ plot.optContr <- function (x, superpose = TRUE, xlab = "Dose",
 
 #' Plot optimal contrasts
 #'
-#' @inheritParams plot.optContr
 #' @param optContrObj For function \samp{plotContr} the \samp{optContrObj}
 #' should contain an object of class \samp{optContr}.
 #' 

R/optDesign.R

@@ -373,7 +373,6 @@ optDesign <- function(models, probs, doses,
 
 #' Calculate design criteria for set of designs
 #'
-#' @inheritParams optDesign
 #' @param design Argument for \samp{rndDesign} and \samp{calcCrit} functions: Numeric vector (or matrix) of allocation
 #'   weights for the different doses. The rows of the matrices need to sum to 1. Alternatively also an object of class
 #'   "DRdesign" can be used for \samp{rndDesign}. Note that there should be at least as many design points available as
@@ -497,7 +496,6 @@ print.DRdesign <- function(x, digits = 5, eps = 0.001, ...){
 
 #' Efficiently round calculated design to a finite sample size
 #'
-#' @inheritParams calcCrit
 #' @param eps Argument for \samp{rndDesign} function: Value under which elements of w will be regarded as 0.
 #' @rdname optDesign
 #' @export
@@ -539,7 +537,6 @@ rndDesign <- function(design, n, eps = 0.0001){
 
 #' Plot optimal designs
 #'
-#' @inheritParams optDesign
 #' @param x Object of class \samp{DRdesign} (for \samp{plot.design})
 #' @param lwdDes,colDes Line width and color of the lines plotted for the design (in \samp{plot.design})
 #'

R/planMod.R

@@ -421,7 +421,6 @@ print.summary.planMod <- function(x, digits = 3, len = 101,
 
 #' Plot to summarize dose-response and dose estimations
 #'
-#' @inheritParams plot.planMod
 #' @param x An object of class planMod
 #' @param type Type of plot to produce
 #' @param placAdj When \samp{type = "dose-response"}, this determines whether dose-response estimates are shown on