new version on R cran

Author: Mamba413

R-package/DESCRIPTION

@@ -1,8 +1,8 @@
 Package: abess
 Type: Package
 Title: Fast Best Subset Selection
-Version: 0.4.8
-Date: 2023-09-19
+Version: 0.4.9
+Date: 2024-09-09
 Authors@R: c(
   person(given = "Jin", family = "Zhu", email = "[email protected]", role = c("aut", "cre"), comment = c(ORCID = "0000-0001-8550-5822")),
   person(given = "Zezhi", family = "Wang", email = "[email protected]", role = c("aut")),

R-package/NEWS.md

@@ -1,3 +1,8 @@
+# abess 0.4.9
+* Fix bug in Cpp level
+* Fix error in: https://www.stats.ox.ac.uk/pub/bdr/clang19/abess.log
+* Fix notes in https://cran.r-project.org/web/checks/check_results_abess.html
+
 # abess 0.4.8
 * Support no-intercept GLM model by param 'fit.intercept'.
 * Allow to restrict the range of estimation for beta by param 'beta.high' and 'beta.low'.

R-package/R/abess.R

@@ -174,10 +174,10 @@ abess <- function(x, ...) UseMethod("abess")
 #' 
 #' \code{abess} support some well-known advanced statistical methods to analyze data, including 
 #' \itemize{
-#'   \item{sure independent screening: } {helpful for ultra-high dimensional predictors (i.e., \eqn{p \gg n}). Use the parameter \code{screening.num} to retain the marginally most important predictors. See Fan et al (2008) for more details. }
-#'   \item{best subset of group selection: } {helpful when predictors have group structure. Use the parameter \code{group.index} to specify the group structure of predictors. See Zhang et al (2021) for more details. }
-#'   \item{\eqn{l_2} regularization best subset selection: } {helpful when signal-to-ratio is relatively small. Use the parameter \code{lambda} to control the magnitude of the regularization term.}
-#'   \item{nuisance selection: } {helpful when the prior knowledge of important predictors is available. Use the parameter \code{always.include} to retain the important predictors.}
+#'   \item sure independent screening: helpful for ultra-high dimensional predictors (i.e., \eqn{p \gg n}). Use the parameter \code{screening.num} to retain the marginally most important predictors. See Fan et al (2008) for more details. 
+#'   \item best subset of group selection: helpful when predictors have group structure. Use the parameter \code{group.index} to specify the group structure of predictors. See Zhang et al (2021) for more details. 
+#'   \item \eqn{l_2} regularization best subset selection: helpful when signal-to-ratio is relatively small. Use the parameter \code{lambda} to control the magnitude of the regularization term.
+#'   \item nuisance selection: helpful when the prior knowledge of important predictors is available. Use the parameter \code{always.include} to retain the important predictors.
 #' }
 #' The arbitrary combination of the four methods are definitely support. 
 #' Please see [online vignettes](https://abess-team.github.io/abess/articles/v07-advancedFeatures.html) for more details about the advanced features support by \code{abess}. 

R-package/R/coef.abesspca.R

@@ -9,12 +9,12 @@
 #' the coefficient fitted at given \code{support.size}.
 #' If \code{support.size = NULL}, then all coefficients would be returned.
 #' Default: \code{support.size = NULL}.
-#' This parameter is omitted if {sparse.type = "kpc"}.
+#' This parameter is omitted if \code{sparse.type = "kpc"}.
 #' @param kpc An integer vector specifies
 #' the coefficient fitted at given principal component.
 #' If \code{kpc = NULL}, then all coefficients would be returned.
 #' Default: \code{kpc = NULL}.
-#' This parameter is omitted if {sparse.type = "fpc"}.
+#' This parameter is omitted if \code{sparse.type = "fpc"}.
 #'
 #' @param sparse A logical value, specifying whether the coefficients should be
 #' presented as sparse matrix or not. Default: \code{sparse = TRUE}.

R-package/R/generate.data.R

@@ -22,7 +22,7 @@
 #' where the covariance matrix has \eqn{(i,j)} entry equals \eqn{I(i \neq j)}.
 #' \code{cortype = 2} denotes the exponential structure,
 #' where the covariance matrix has \eqn{(i,j)} entry equals \eqn{rho^{|i-j|}}.
-#' code{cortype = 3} denotes the constant structure,
+#' \code{cortype = 3} denotes the constant structure,
 #' where the non-diagonal entries of covariance
 #' matrix are \eqn{rho} and diagonal entries are 1.
 #' @param snr A numerical value controlling the signal-to-noise ratio (SNR). The SNR is defined as

R-package/cran-comments.md

@@ -1,7 +1,3 @@
-## Comment
-
-`abess` is remove from CRAN because incorrect C++ flags in configure scripts. This submission has address this incorrectness.
-
 ## Test environments
 * local R installation, R 4.1.0
 * win-builder (devel)