cran maintenance

.Rbuildignore

@@ -12,3 +12,4 @@
 ^CRAN-RELEASE$
 ^codecov\.yml$
 ^\.github$
+^CRAN-SUBMISSION$

CRAN-SUBMISSION

@@ -0,0 +1,3 @@
+Version: 6.0.3
+Date: 2024-07-30 11:20:38 UTC
+SHA: 3f444cf6d6aca23a3a7f2d482df2e33bb078dc55

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: hts
 Title: Hierarchical and Grouped Time Series
-Version: 6.0.2
+Version: 6.0.3
 Authors@R: c(
     person("Rob", "Hyndman", role = "aut", comment = "Package creator"), 
     person("Alan", "Lee", role = "aut", comment = "Fast computation using recursive methods"),
@@ -39,6 +39,6 @@ URL: https://pkg.earo.me/hts/
 BugReports: https://github.com/earowang/hts/issues
 License: GPL (>= 2)
 VignetteBuilder: knitr
-RoxygenNote: 7.1.1
+RoxygenNote: 7.2.3
 Roxygen: list(markdown = TRUE, roclets=c('rd', 'collate', 'namespace'))
 Encoding: UTF-8

NAMESPACE

@@ -27,7 +27,6 @@ export(smatrix)
 export(summary.gts)
 export(summary.hts)
 import(Matrix)
-import(SparseM)
 import(forecast)
 import(grDevices)
 import(methods)

NEWS.md

@@ -1,3 +1,7 @@
+# hts 6.0.3
+
+* Cran maintenance
+
 # hts 6.0.2
 
 * Removed dependency on matrixcalc

R/MinT.R

@@ -80,7 +80,7 @@ shrink.estim <- function(x, tar)
 #' \code{\link[hts]{forecast.gts}}, \code{\link[hts]{combinef}}
 #' @references Wickramasuriya, S. L., Athanasopoulos, G., & Hyndman, R. J. (2019).
 #' Optimal forecast reconciliation for hierarchical and grouped time series through trace minimization.
-#' \emph{Journal of the American Statistical Association}, \bold{114}(526), 804--819. \url{https://robjhyndman.com/working-papers/mint/}
+#' \emph{Journal of the American Statistical Association}, \bold{114}(526), 804--819. \url{https://robjhyndman.com/publications/mint/}
 #'
 #' Wickramasuriya, S. L., Turlach, B. A., & Hyndman, R. J. (to appear). Optimal non-negative forecast reconciliation.
 #' \emph{Statistics and Computing}. \url{https://robjhyndman.com/publications/nnmint/}

R/gts.R

@@ -270,7 +270,7 @@ CreateGmat <- function(bnames, characters) {
   gmatrix <- gmatrix[!duplicated(gmatrix), , drop = FALSE]
   # Remove bottom names if it has
   check <- try(which(gmatrix == bnames, arr.ind = TRUE)[1L, 1L], silent = TRUE)
-  if (class(check) != "try-error") {
+    if (class(check) != "try-error") {
     gmatrix <- gmatrix[-check, ]
   }
   return(gmatrix)

R/imports.R

@@ -3,8 +3,8 @@
 #' @importFrom utils combn
 #' @import methods
 #' @importFrom stats window as.ts fitted frequency is.ts na.omit residuals time ts tsp tsp<-
-#' @import Matrix 
-#' @import SparseM
+#' @import Matrix
+#' @importFrom(SparseM,as.matrix.csr)
 #' @import forecast
 #' @import parallel
 #' @useDynLib hts

R/tracemin.R

@@ -22,22 +22,22 @@ LU <- function(fcasts, S, weights, allow.changes = FALSE) {
   nbts <- ncol(S)
   nagg <- nts - nbts
   seqagg <- 1L:nagg
-  
+
   if (!allow.changes) {
     utmat <- cbind2(sparseMatrix(i = seqagg, j = seqagg, x = 1),
-                    -1 * S[1L:nagg, ])
+                    -1 * S[1L:nagg, , drop = TRUE])
   } else {
     # Identifying rows with one 1 element to make the Identity matrix in S
     indx <- rowSums(S)
-    idx <- tail(which(indx == 1L), nbts) 
-    
+    idx <- tail(which(indx == 1L), nbts)
+
     # Permulation vector to rearrange rows of S, rows/col of W and forecasts
     pvec <- c(setdiff(1:nts, idx) , idx)
     S2 <- S[pvec, ]
     weights <- weights[pvec, pvec]
     fcasts <- fcasts[pvec, ]
-    utmat <- cbind2(sparseMatrix(i = seqagg, j = seqagg, x = 1), 
-                    -1 * S2[1L:nagg, ])
+    utmat <- cbind2(sparseMatrix(i = seqagg, j = seqagg, x = 1),
+                    -1 * S2[1L:nagg, , drop = TRUE])
   }
   jmat <- sparseMatrix(i = 1L:nbts, j = (nagg + 1L):nts, x = rep(1L, nbts),
                        dims = c(nbts, nts))
@@ -74,14 +74,14 @@ CG <- function(fcasts, S, weights, allow.changes = FALSE) {
   } else {
     # Identifying rows with one 1 element to make the Identity matrix in S
     indx <- rowSums(S)
-    idx <- tail(which(indx == 1L), nbts) 
-    
+    idx <- tail(which(indx == 1L), nbts)
+
     # Permulation vector to rearrange rows of S, rows/col of W and forecasts
     pvec <- c(setdiff(1:nts, idx) , idx)
     S2 <- S[pvec, ]
     weights <- weights[pvec, pvec]
     fcasts <- fcasts[pvec, ]
-    utmat <- cbind2(Matrix::sparseMatrix(i = seqagg, j = seqagg, x = 1), 
+    utmat <- cbind2(Matrix::sparseMatrix(i = seqagg, j = seqagg, x = 1),
                     -1 * S2[1L:nagg, ])
   }
   jmat <- Matrix::sparseMatrix(i = 1L:nbts, j = (nagg + 1L):nts, x = rep(1L, nbts),
@@ -118,8 +118,8 @@ CHOL <- function(fcasts, S, weights, allow.changes = FALSE) {
     # Identifying rows with one 1 element to make the Identity matrix in S
     Sm <- as(S, "dgCMatrix")
     indx <- rowSums(Sm)
-    idx <- tail(which(indx == 1L), nbts) 
-    
+    idx <- tail(which(indx == 1L), nbts)
+
     # Permulation vector to rearrange rows of S, rows/col of W and forecasts
     pvec <- c(setdiff(1:nts, idx) , idx)
     S2 <- S[pvec, ]

cran-comments.md

@@ -1,11 +1,6 @@
 ## Comments
 
-This is a resubmission that urls have been fixed.
-
-## Test environments
-* local R installation Ubuntu 21.04, R 4.1.0
-* macOS release, windows release, windows 3.6, ubuntu 16.04 devel, ubuntu 16.04 release, ubuntu 16.04 oldrel, ubuntu 16.04 3.5 on github actions
-* win-builder (devel)
+This is a submission to fix CRAN errors.
 
 ## R CMD check results
 

src/RcppExports.cpp

@@ -6,6 +6,11 @@
 
 using namespace Rcpp;
 
+#ifdef RCPP_USE_GLOBAL_ROSTREAM
+Rcpp::Rostream<true>&  Rcpp::Rcout = Rcpp::Rcpp_cout_get();
+Rcpp::Rostream<false>& Rcpp::Rcerr = Rcpp::Rcpp_cerr_get();
+#endif
+
 // cgm_c
 Eigen::MatrixXd cgm_c(SEXP As, SEXP bs);
 RcppExport SEXP _hts_cgm_c(SEXP AsSEXP, SEXP bsSEXP) {

vignettes/hts.R

@@ -0,0 +1 @@
+