Add RcppArmadillo dependency to get around R CMD Check NOTE

DESCRIPTION

@@ -60,6 +60,7 @@ Imports:
     parallel,
     randtoolbox,
     Rcpp (>= 0.12.7),
+    RcppArmadillo,
     stats,
     tibble,
     utils

NAMESPACE

@@ -43,6 +43,8 @@ importFrom(MASS,ginv)
 importFrom(MASS,mvrnorm)
 importFrom(Matrix,nearPD)
 importFrom(Rcpp,evalCpp)
+importFrom(RcppArmadillo,armadillo_reset_cores)
+importFrom(RcppArmadillo,armadillo_throttle_cores)
 importFrom(cobs,cobs)
 importFrom(doParallel,registerDoParallel)
 importFrom(generics,augment)

NEWS.md

@@ -24,6 +24,10 @@
 
 -   Add R-hub workflow to enable checks for platforms not covered already.
 
+-   Imported `RcppArmadillo` functions `armadillo_throttle_cores` and `armadillo_reset_cores` to try and prevent CRAN R CMD checks using multiple threads.
+
+-   Updated Makevars (now Makevars.in) and Makevars.win as part of previous update.
+
 # joineRML 0.4.6
 
 ## Housekeeping

R/joineRML.R

@@ -15,6 +15,7 @@
 #'   
 #' @useDynLib joineRML, .registration = TRUE
 #' @importFrom Rcpp evalCpp
+#' @importFrom RcppArmadillo armadillo_throttle_cores armadillo_reset_cores
 #'
 #' @references
 #' Wulfsohn MS, Tsiatis AA. A joint model for survival and longitudinal data

src/Makevars.in

@@ -0,0 +1,3 @@
+PKG_CPPFLAGS = -I../inst/include
+PKG_CXXFLAGS = @OPENMP_FLAG@
+PKG_LIBS= @OPENMP_FLAG@ $(LAPACK_LIBS) $(BLAS_LIBS) $(FLIBS)

src/Makevars.win

@@ -1,2 +1,2 @@
-PKG_CXXFLAGS = $(SHLIB_OPENMP_CXXFLAGS)
+PKG_CXXFLAGS = -I../inst/include -I. $(SHLIB_OPENMP_CXXFLAGS)
 PKG_LIBS = $(SHLIB_OPENMP_CXXFLAGS) $(LAPACK_LIBS) $(BLAS_LIBS) $(FLIBS)

src/RcppExports.cpp

@@ -125,3 +125,19 @@ BEGIN_RCPP
     return rcpp_result_gen;
 END_RCPP
 }
+
+static const R_CallMethodDef CallEntries[] = {
+    {"_joineRML_expWArma", (DL_FUNC) &_joineRML_expWArma, 4},
+    {"_joineRML_gammaUpdate", (DL_FUNC) &_joineRML_gammaUpdate, 11},
+    {"_joineRML_hazHat", (DL_FUNC) &_joineRML_hazHat, 3},
+    {"_joineRML_gammaUpdate_approx", (DL_FUNC) &_joineRML_gammaUpdate_approx, 10},
+    {"_joineRML_lambdaUpdate", (DL_FUNC) &_joineRML_lambdaUpdate, 10},
+    {"_joineRML_mvrnormArma", (DL_FUNC) &_joineRML_mvrnormArma, 3},
+    {"_joineRML_bSim", (DL_FUNC) &_joineRML_bSim, 3},
+    {NULL, NULL, 0}
+};
+
+RcppExport void R_init_joineRML(DllInfo *dll) {
+    R_registerRoutines(dll, NULL, CallEntries, NULL, NULL);
+    R_useDynamicSymbols(dll, FALSE);
+}

vignettes/joineRML-tidy.Rmd

@@ -10,14 +10,18 @@ vignette: >
 ---
 
 ```{r load-joineRML, include=FALSE}
+library(RcppArmadillo)
+RcppArmadillo::armadillo_throttle_cores(2)
+
+Sys.setenv(OMP_THREAD_LIMIT = 1)
+Sys.setenv(OMP_NUM_THREADS = 1)
+Sys.setenv(OPENBLAS_NUM_THREADS = 1)
+Sys.setenv(ARMA_OPENMP_THREADS = 1)
+Sys.setenv(R_INSTALL_NCPUS = 1)
+options(Ncpus = 1)
+
 library(joineRML)
 library(knitr)
-
-Sys.setenv("OMP_THREAD_LIMIT" = 1)
-Sys.setenv("OMP_NUM_THREADS" = 1)
-Sys.setenv("OPENBLAS_NUM_THREADS" = 1)
-Sys.setenv("ARMA_OPENMP_THREADS" = 1)
-options(Ncpus = 1)
 ```
 
 # Introduction
@@ -60,7 +64,7 @@ Further to that, we only select the first 50 individuals to speed up these examp
 hvd <- hvd[hvd$num <= 50, ]
 ```
 
-```{r hvd_model_fit, cache=TRUE}
+```{r hvd_model_fit}
 set.seed(12345)
 fit <- mjoint(
   formLongFixed = list(
@@ -185,3 +189,7 @@ Several examples of how to use `broom` including more details are available on i
 ```{r vignette-broom, eval=FALSE, purl=FALSE}
 vignette(topic = "broom", package = "broom")
 ```
+
+```{r, echo=FALSE, message=FALSE}
+RcppArmadillo::armadillo_reset_cores()
+```

vignettes/joineRML.Rmd

@@ -10,6 +10,16 @@ vignette: >
 ---
 
 ```{r, echo=FALSE, message=FALSE}
+library(RcppArmadillo)
+RcppArmadillo::armadillo_throttle_cores(n = 2)
+
+Sys.setenv(OMP_THREAD_LIMIT = 1)
+Sys.setenv(OMP_NUM_THREADS = 1)
+Sys.setenv(OPENBLAS_NUM_THREADS = 1)
+Sys.setenv(ARMA_OPENMP_THREADS = 1)
+Sys.setenv(R_INSTALL_NCPUS = 1)
+options(Ncpus = 1)
+
 knitr::opts_chunk$set(collapse = TRUE, comment = "#>")
 library(Matrix)
 library(nlme)
@@ -21,12 +31,6 @@ if (requireNamespace('joineR', quietly = TRUE)) {
 } else {
   message("'joineR' not available")
 }
-
-Sys.setenv("OMP_THREAD_LIMIT" = 1)
-Sys.setenv("OMP_NUM_THREADS" = 1)
-Sys.setenv("OPENBLAS_NUM_THREADS" = 1)
-Sys.setenv("ARMA_OPENMP_THREADS" = 1)
-options(Ncpus = 1)
 ```
 
 # Introduction
@@ -92,7 +96,7 @@ The main function in the `joineRML` package is the `mjoint()` function. Its main
 
 We can fit a bivariate joint model to the log-transformed valve gradient and LVMI indices in the `hvd` subset using
 
-```{r hvd_model_fit, cache=TRUE}
+```{r hvd_model_fit}
 set.seed(12345)
 fit <- mjoint(
   formLongFixed = list("grad" = log.grad ~ time + sex + hs, 
@@ -175,7 +179,7 @@ To fit a univariate model in `joineR` we run the following code for the `hvd` da
 joineR_available <- require(joineR)
 ```
 
-```{r joineR, cache=TRUE, eval=joineR_available, purl=joineR_available}
+```{r joineR, eval=joineR_available, purl=joineR_available}
 library(joineR, quietly = TRUE)
 
 hvd.surv <- UniqueVariables(hvd, var.col = c("fuyrs", "status"), id.col = "num")
@@ -198,7 +202,7 @@ summary(fit.joiner)
 
 To fit a univariate model in `joineRML` we run the following code for the `hvd` data
 
-```{r joineRML, cache=TRUE}
+```{r joineRML}
 set.seed(123)
 fit.joinerml <- mjoint(formLongFixed = log.lvmi ~ time + age,
                        formLongRandom = ~ time | num,
@@ -226,3 +230,7 @@ plot(fit.joiner$coefficients$random[id.ord, 2], ranef(fit.joinerml)[, 2],
 grid()
 abline(a = 0, b = 1, col = 2, lty = "dashed")
 ```
+
+```{r, echo=FALSE, message=FALSE}
+RcppArmadillo::armadillo_reset_cores()
+```