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require('tcltk')

calcinp =''
mkput = function(sym){
  function(...){
    calcinp <<- paste(calcinp,sym,sep='')
    tkconfigure(display,text=calcinp)
  }
}
clearit = function(...){

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# required: RGtk2
calculateGUI <- function() {
	## Run on "OK"
	performStatistics <- function(button, user.data) {
		res <- NULL
		d <- NULL
		error <- NULL
		warning <- NULL
		# Get the information about data and the file
		the.file <- filename$getText()

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# improved list of objects by Dirk
.ls.objects <- function (pos = 1, pattern, order.by,
                        decreasing=FALSE, head=FALSE, n=5) {
    napply <- function(names, fn) sapply(names, function(x)
                                         fn(get(x, pos = pos)))
    names <- ls(pos = pos, pattern = pattern)
    obj.class <- napply(names, function(x) as.character(class(x))[1])
    obj.mode <- napply(names, mode)
    obj.type <- ifelse(is.na(obj.class), obj.mode, obj.class)
    obj.size <- napply(names, object.size)

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//
// Based on code sent by Kevin Ushey
// to Rcpp-devel on Tue, 8 Jul 2014
//

#ifndef LIST_BUILDER_H
#define LIST_BUILDER_H

#include <Rcpp.h>
using namespace Rcpp;

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#include <iostream>
#include <fstream>
#include <unistd.h>

void process_mem_usage(double& vm_usage, double& resident_set)
{
    vm_usage     = 0.0;
    resident_set = 0.0;

    // the two fields we want

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#include <iostream>
#include <vector>
#include <algorithm>

using namespace std;

vector<vector<int> > cart_product (const vector<vector<int>>& v) {
    vector<vector<int>> s = {{}};
    for (auto& u : v) {
        vector<vector<int>> r;

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require(reshape2)
require(rgl)

plot_rgl_model <- function(fdata){

  fdata <- fdata[order(fdata[, 1], fdata[, 2]), ]

  orig_names <- colnames(fdata)
  colnames(fdata) <- c("x", "y", "z")
  fdata <- as.data.frame(fdata)

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// callback4.cpp - C++11 Lambda Callback

// To build:
//   g++ -std=c++11 callback4.cpp

// Situation:  A "Caller" class allows another class "Callee"
//             to connect to it via callback.  How to implement this?

// A C++11 lambda function can be used.

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#mlpack on Windows

mlpack, while not designed with Windows as a specific target, can still be built and run on Windows with some configuration. This document details the steps necessary to get MLPACK compiled using MinGW64.

#Prerequisites

• MinGW64: The compiler we use. Please add it into PATH
• boost: This is the boost 1.56 static library compiled by myself using MinGW64. Static library is a better choice on Windows.
• Armadillo
• BLAS and lapack: Static BLAS and lapack library which are needed by Armadillo. Compiled using MinGW64

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set backspace=indent,eol,start
colorscheme default
set nocompatible              " be iMproved, required
filetype off                  " required

" set the runtime path to include Vundle and initialize
set rtp+=~/.vim/bundle/Vundle.vim
call vundle#begin()
" alternatively, pass a path where Vundle should install plugins
"call vundle#begin('~/some/path/here')