Conform to CRAN expectations

.Rbuildignore

@@ -1,3 +1,4 @@
 ^\.clang-format$
 ^LICENSE\.md$
-^.vscode$
+^.vscode$
+^math$

.gitignore

@@ -2,4 +2,5 @@
 config.log
 config.status
 *.tar.gz
-*.Rcheck
+*.Rcheck
+.DS_Store

DESCRIPTION

@@ -9,12 +9,12 @@ Description: A generalization of principal component analysis for integrative
   analysis. The method finds principal components that describe single matrices
   or that are common to several matrices. The solutions are sparse. Rank of
   solutions is automatically selected using cross validation. The method is
-  described in Kallus, Johansson, Nelander and Jörnsten (2019)
-  <arXiv:1911.04927>.
+  described in Kallus et al. (2019) <arXiv:1911.04927>.
 Depends: R (>= 3.3.0)
 Imports: digest (>= 0.6.0), Rcpp (>= 1.0.8)
 LinkingTo: Rcpp, RcppEigen, RcppGSL
 SystemRequirements: C++11
+Biarch: true
 NeedsCompilation: yes
 License: GPL (>= 3)
 Encoding: UTF-8

NEWS.md

@@ -0,0 +1,4 @@
+# mmpca 2.0.2 (2022-09-03)
+
+* Package has been cleaned up to be published on CRAN again
+* Removed all legacy C code and use Rcpp fully

tests/test.R

@@ -14,10 +14,12 @@ test_lambda_selection <- function() {
   X[[3]] <- v[[3]][, 1:2] %*% t(v[[4]][, 3:4])
   X[[4]] <- v[[1]][, c(1, 3)] %*% t(v[[5]][, 1:2])
   X[[1]][matrix(runif(8 * 8), 8, 8) < 0.2] <- NA
-  mmpca::mmpca(X, inds, 3, parallel = FALSE, trace = 3)
+  mmpca::mmpca(X, inds, 3, parallel = TRUE)
   # only checking for errors, not checking the result
   return(TRUE)
 }
-options(mc.cores = 2)
+opt <- options(mc.cores = 2)
 set.seed(1)
 test_lambda_selection()
+# Reset options
+options(opt)