wip: convert to package

.gitignore

@@ -1,4 +1,4 @@
-# ---> C
+## C compiler and compiled library files
 # Prerequisites
 *.d
 
@@ -43,24 +43,7 @@
 *.idb
 *.pdb
 
-# Kernel Module Compile Results
-*.mod*
-*.cmd
-.tmp_versions/
-modules.order
-Module.symvers
-Mkfile.old
-dkms.conf
-
-# ---> R
-# History files
-.Rhistory
-.Rapp.history
-
-# Session Data files
-*.RData
-*.Rdata
-
+## R environment, data and pacakge build intermediate files/folders
 # R Data Object files
 *.Rds
 *.rds
@@ -68,22 +51,13 @@ dkms.conf
 # Example code in package build process
 *-Ex.R
 
-# Output files from R CMD build
-/*.tar.gz
-
 # Output files from R CMD check
 /*.Rcheck/
 
-# RStudio files
-.Rproj.user/
-
 # produced vignettes
 vignettes/*.html
 vignettes/*.pdf
 
-# OAuth2 token, see https://github.com/hadley/httr/releases/tag/v0.3
-.httr-oauth
-
 # knitr and R markdown default cache directories
 /*_cache/
 /cache/
@@ -92,8 +66,32 @@ vignettes/*.pdf
 *.utf8.md
 *.knit.md
 
-# Shiny token, see https://shiny.rstudio.com/articles/shinyapps.html
-rsconnect/
+# R documentation
+*.Rd
 
+## R session, RStudio and VSCode files
+# RStudio files
+.Rproj.user/
+
+# History files
+.Rhistory
+.Rapp.history
+
+# Session Data files
+*.RData
+*.Rdata
+
+# VSCode configuration
+.vscode/
+
+## Archives, compressed files/folders
+# Output files from R CMD build
+*.tar.gz
+*.zip
+
+## Further project development folders/files
 # General Work In Progress files
 wip/
+
+# PDFs
+*.pdf

tensorPredictors/DESCRIPTION

@@ -1,6 +1,6 @@
 Package: tensorPredictors
 Type: Package
-Title: Tensor/Matrix valued predictors for regression and dimension reduction
+Title: Sufficient Dimension Reduction for Matrix Valued Predictors
 Version: 0.1
 Date: 2021-10-28
 Author: Daniel Kapla [aut]
@@ -8,6 +8,8 @@ Maintainer: Daniel Kapla <daniel@kapla.at>
 Description: Multiple methods using tensor/matrix valued predictors for
     regression or sufficient dimension reduction.
 License: GPL-3
-Depends: 
+Depends: R(>= 3.0)
+Imports: stats
+Suggests: RSpectra
 Encoding: UTF-8
 RoxygenNote: 7.1.1

tensorPredictors/NAMESPACE

@@ -4,4 +4,6 @@ export(LSIR)
 export(PCA2d)
 export(POI)
 export(approx.kronecker)
+export(reduce)
+import(stats)
 useDynLib(tensorPredictors, .registration = TRUE)

tensorPredictors/R/approx_kronecker.R

@@ -13,15 +13,13 @@
 #'
 #' @examples
 #' A <- matrix(seq(14), 7, 2)
-#' B <- matrix(c(T, F), 3, 4)
+#' B <- matrix(c(1, 0), 3, 4)
 #' C <- kronecker(A, B) # the same as 'C <- A %x% B'
 #' approx.kronecker(C, dim(A), dim(B))
 #'
 #' @seealso C.F. Van Loan / Journal of Computational and Applied Mathematics
 #'          123 (2000) 85-100 (pp. 93-95)
 #'
-#' @imports RSpectra
-#'
 #' @export
 approx.kronecker <- function(C, dimA, dimB) {
 
@@ -29,8 +27,9 @@ approx.kronecker <- function(C, dimA, dimB) {
     R <- aperm(C, c(2L, 4L, 1L, 3L))
     dim(R) <- c(prod(dimA), prod(dimB))
 
-    svdR <- try(RSpectra::svds(R, 1L), silent = TRUE)
-    if (is(svdR, 'try-error')) {
+    if (requireNamespace("RSpectra", quietly = TRUE)) {
+        svdR <- RSpectra::svds(R, 1L)
+    } else {
         svdR <- svd(R, 1L, 1L)
     }
 

tensorPredictors/R/matpow.R

@@ -21,7 +21,7 @@
 #'
 #' @seealso \code{\link{solve}}, \code{\link{qr}}, \code{\link{svd}}.
 #'
-#' @examples
+#' @examples \dontrun{
 #' # General full rank square matrices.
 #' A <- matrix(rnorm(121), 11, 11)
 #' all.equal(matpow(A,  1), A)
@@ -54,6 +54,7 @@
 #' all.equal(B %*% B %*% A %*% B %*% B, B %*% B)
 #' all.equal(B %*% A, t(B %*% A))
 #' all.equal(A %*% B, t(A %*% B))
+#' }
 #'
 #' @keywords internal
 matpow <- function(A, pow, tol = 1e-7) {

tensorPredictors/R/multi_assign.R

@@ -7,7 +7,7 @@
 #' @details The parameter names \code{lhs} and \code{rhs} stand for "Left Hand
 #'  Side" and "Right Hand Side", respectively.
 #'
-#' @examples
+#' @examples \dontrun{
 #' c(a, b) %<-% list(1, 2)
 #' # is equivalent to
 #' ## a <- 1
@@ -15,12 +15,13 @@
 #'
 #' # Switching the values of a, b could be done by.
 #' c(a, b) %<-% list(b, a)
-#' note the usage of 'list' on the right side, otherwise an exraction of the
-#' first two values of the concatenated object is performed. See next:
+#' # note the usage of 'list' on the right side, otherwise an exraction of the
+#' # first two values of the concatenated object is performed. See next:
 #'
 #' # Extract values.
 #' c(d1, d2, d3) %<-% 1:10
-#' extracting the first three valus from the vector of length 10.
+#' # extracting the first three valus from the vector of length 10.
+#' }
 #'
 #' @keywords internal
 "%<-%" <- function(lhs, rhs) {
@@ -30,4 +31,5 @@
     for (i in seq_along(var.names)) {
         assign(var.names[i], values[[i]], envir = env)
     }
+    lhs
 }

tensorPredictors/R/pca2d.R

@@ -1,10 +1,14 @@
 #'2-Dimensional Principal Component Analysis
 #'
-#' @param X Matrix of dim (n, p * t) with each row the vectorized (p, t) observation.
+#' @param X Matrix of \code{dim (n, p * t)} with each row the vectorized
+#'  \eqn{p \times t} observation.
 #' @param p nr. predictors
 #' @param t nr. timepoints
 #' @param ppc reduced nr. predictors (p-principal components)
 #' @param tpc reduced nr. timepoints (t-principal components)
+#' @param scale passed to \code{\link{scale}} before processing \code{X}.
+#'
+#' @return list with 2d pca estimated reduction estimates.
 #'
 #' @details The `i`th observation is stored in a row such that its matrix equiv
 #'  is given by `matrix(X[i, ], p, t)`.

tensorPredictors/R/poi.R

@@ -19,13 +19,10 @@ POI <- function(A, B, d,
     # TODO:
     stopifnot(method == 'FastPOI-C')
 
-    if (nrow(A) < 100) {
-        Delta <- eigen(A, symmetric = TRUE)$vectors[, 1:d, drop = FALSE]
+    if (requireNamespace("RSpectra", quietly = TRUE)) {
+        Delta <- RSpectra::eigs_sym(A, d)$vectors
     } else {
-        Delta <- try(RSpectra::eigs_sym(A, d)$vectors, silent = TRUE)
-        if (is(Delta, 'try-error')) {
-            Delta <- eigen(A, symmetric = TRUE)$vectors[1:d, , drop = FALSE]
-        }
+        Delta <- eigen(A, symmetric = TRUE)$vectors[, 1:d, drop = FALSE]
     }
 
     # Set initial value.

tensorPredictors/R/random.R

@@ -9,11 +9,11 @@
 #'
 #' @return a \eqn{n\times p}{n x p} matrix with samples in its rows.
 #'
-#' @examples
-#' \dontrun{
+#' @examples \dontrun{
 #' rmvnorm(20, sigma = matrix(c(2, 1, 1, 2), 2))
 #' rmvnorm(20, mu = c(3, -1, 2))
 #' }
+#'
 #' @keywords internal
 rmvnorm <- function(n = 1, mu = rep(0, p), sigma = diag(p)) {
     if (!missing(sigma)) {

tensorPredictors/R/tensor_predictors.R

@@ -5,6 +5,8 @@
 #'
 #' @author Daniel Kapla
 #'
+#' @import stats
+#'
 #' @docType package
 #' @useDynLib tensorPredictors, .registration = TRUE
 "_PACKAGE"
@@ -158,7 +160,13 @@ tensor_predictor <- function(X, Fy, p, t, k = 1L, r = 1L, d1 = 1L, d2 = 1L,
     ))
 }
 
-#' TODO: Write this properly!
+#' Reduce new data using estimated reduction.
+#'
+#' @param object estimated reduction object.
+#' @param data new data to apply reduce
+#' @param use which type of reduction/internal variables should be used
+#'
+#' @export
 reduce <- function(object, data, use = 'Gamma') {
     if (use == 'Gamma') {
         projection <- object$Gamma

tensorPredictors/src/poi.c

@@ -74,13 +74,14 @@ extern SEXP FastPOI_C_sub(SEXP in_A, SEXP in_B, SEXP in_Delta, SEXP in_lambda, S
     return out_Z;
 }
 
+/* List of registered routines (e.g. C entry points) */
 static const R_CallMethodDef CallEntries[] = {
     {"FastPOI_C_sub", (DL_FUNC) &FastPOI_C_sub, 5},
     {NULL, NULL, 0}
 };
 
 /* Restrict C entry points to registered routines. */
-void R_init_CVE(DllInfo *dll) {
+void R_init_tensorPredictors(DllInfo *dll) {
     R_registerRoutines(dll, NULL, CallEntries, NULL, NULL);
     R_useDynamicSymbols(dll, FALSE);
 }