Fixes #574 and other CRAN submission issues

Author: kudkudak

DESCRIPTION

@@ -1,21 +1,20 @@
 Package: gmum.r
 Version: 0.2
-Date: 2015-08-05
+Date: 2015-09-25
 Title: Efficient C++ Implementations of SVM, GNG and CEC Models
 Type: Package
 Author: Wojciech Czarnecki, Stanislaw Jastrzebski, Marcin Data, Igor Sieradzki, Mateusz Bruno-Kaminski, Karol Jurek, Piotr Kowenzowski, Michal Pletty, Konrad Talik, Maciej Zgliczynski
 Maintainer: Stanislaw Jastrzebski <[email protected]>
 Description: Package focusing on efficiency (C++ implementations) and intuitive API. gmum.r is a close collaboration between GMUM group members (http://gmum.net) and students.
 License: MIT + file LICENSE
 Repository: CRAN
-Depends:
+Imports:
     ggplot2 (>= 1.0.0),
     stats, 
     igraph,
     SparseM,
-    httr,
     Matrix,
-    MASS (>= 7.3),
+    httr,
     Rcpp (>= 0.11.6)
 LinkingTo: Rcpp, RcppArmadillo, BH
 NeedsCompilation: yes

NAMESPACE

@@ -80,4 +80,22 @@ importFrom(ggplot2,scale_colour_brewer)
 importFrom(ggplot2,scale_fill_brewer)
 importFrom(ggplot2,scale_size_continuous)
 importFrom(ggplot2,scale_size_identity)
+importFrom(grDevices,rainbow)
+importFrom(graphics,hist)
+importFrom(graphics,pairs)
+importFrom(graphics,par)
+importFrom(graphics,plot)
+importFrom(graphics,plot.new)
+importFrom(graphics,points)
+importFrom(graphics,title)
+importFrom(httr,GET)
+importFrom(httr,content)
+importFrom(stats,na.omit)
+importFrom(stats,prcomp)
+importFrom(stats,predict)
+importFrom(stats,rnorm)
+importFrom(stats,runif)
+importFrom(stats,update)
+importFrom(utils,data)
+importFrom(utils,read.csv)
 useDynLib(gmum.r)

R/gmum.R

@@ -1,3 +1,9 @@
+#' @importFrom grDevices rainbow
+#' @importFrom graphics hist pairs par plot plot.new points title
+#' @importFrom stats na.omit prcomp predict rnorm runif update
+#' @importFrom utils data read.csv
+NULL
+
 # Lazy loading to allow for discovery of all files
 evalqOnLoad( {
   # Autocompletion override
@@ -12,7 +18,7 @@ evalqOnLoad( {
   .DollarNames.Rcpp_CecModel <<- autocompl
 
   # Workaround RStudio bug
-  if(exists(".rs.getAnywhere")) {
+  if(exists(".rs.getAnywhere") && !exists(".rs.getAnywhere.original")) {
     .rs.getAnywhere.original <<- .rs.getAnywhere
     .rs.getAnywhere <<- function(a, envir=.GlobalEnv){ .rs.getAnywhere.original(a, .GlobalEnv) }
   }

R/gng.R

@@ -4,7 +4,6 @@ library(methods)
 #' @importFrom ggplot2 scale_size_continuous scale_size_identity geom_point aes ggplot geom_tile scale_fill_brewer scale_alpha_identity scale_colour_brewer geom_abline
 NULL
 
-
 #' Use first two spatial coordinates as position in layout
 #' 
 #' @note You can pass any igraph layout algorithm to plot
@@ -113,8 +112,10 @@ gngLoad <- NULL
 #' @param community.detection.algorithm Used algorithm from igraph package, by default spinglass.community
 #' 
 #' @examples
+#' \dontrun{
 #' gng <- GNG(gng.preset.sphere(100))
 #' print(node(gng, calculateCentroids(gng)[1])$pos)
+#' }
 calculateCentroids <- NULL
 
 #' Find closest node
@@ -128,11 +129,12 @@ calculateCentroids <- NULL
 #' @param x Can be either \code{vector} or \code{data.frame.}
 #' 
 #' @examples
+#' \dontrun{
 #' gng <- GNG(gng.preset.sphere(100))
 #' # Find closest centroid to c(1,1,1)
 #' found.centroids <- calculateCentroids(gng)
 #' findClosests(gng, found.centroids, c(1,1,1))
-#' 
+#' }
 findClosests <- NULL
 
 #' Check if GNG is running
@@ -421,6 +423,7 @@ clustering.Rcpp_GNGServer <- NULL
 #' @param seed Seed for internal randomization
 #' 
 #' @examples
+#' \dontrun{
 #' X <- gng.preset.sphere(100)
 #' y <- round(runif(100))
 #' # Train in an offline manner
@@ -436,7 +439,7 @@ clustering.Rcpp_GNGServer <- NULL
 #' terminate(gng)
 #' # Plot
 #' plot(gng)
-#'
+#' }
 GNG <- NULL
 
 #' @title convertToIGraph

R/gng.utils.R

@@ -1,3 +1,5 @@
+#' @importFrom httr GET content
+
 #' @export
 #' @rdname print.gng
 #' @method print Rcpp_GNGServer

man/calculateCentroids.Rd

@@ -19,7 +19,9 @@ Using passed community.detection finds communities and for each community pick n
 Get centroids
 }
 \examples{
+\dontrun{
 gng <- GNG(gng.preset.sphere(100))
 print(node(gng, calculateCentroids(gng)[1])$pos)
 }
+}
 

man/findClosests.Rd

@@ -20,9 +20,11 @@ Finds closest node from given list to vector. Often used together with calculate
 Find closest node
 }
 \examples{
+\dontrun{
 gng <- GNG(gng.preset.sphere(100))
 # Find closest centroid to c(1,1,1)
 found.centroids <- calculateCentroids(gng)
 findClosests(gng, found.centroids, c(1,1,1))
 }
+}
 

man/gng.Rd

@@ -52,6 +52,7 @@ see \url{http://sund.de/netze/applets/gng/full/tex/DemoGNG/node20.html}}
 Construct GNG object. Can be used to train offline, or online.
 }
 \examples{
+\dontrun{
 X <- gng.preset.sphere(100)
 y <- round(runif(100))
 # Train in an offline manner
@@ -68,4 +69,5 @@ terminate(gng)
 # Plot
 plot(gng)
 }
+}
 

src/packThis.sh

@@ -43,3 +43,6 @@ rm -r $destination/..Rcheck
 rm -r $destination/.git
 rm -r $destination/.idea
 rm -r pkg/pkg
+rm -r pkg/doc
+rm -r pkg/cmake
+rm -r pkg/libs

src/svmlight/svm_learn.c

@@ -3714,7 +3714,7 @@ CFLOAT *kernel_cache_clean_and_malloc(KERNEL_CACHE *kernel_cache,
   }
   kernel_cache->invindex[result]=docnum;
   kernel_cache->lru[kernel_cache->index[docnum]]=kernel_cache->time; /* lru */
-  return((CFLOAT *)((long)kernel_cache->buffer
+  return((CFLOAT *)((intptr_t)kernel_cache->buffer
 		    +(kernel_cache->activenum*sizeof(CFLOAT)*
 		      kernel_cache->index[docnum])));
 }