High-performance machine learning algorithms, including
matrix factorization and divisive clustering for large sparse and
dense matrices.

Fast machine learning algorithms including matrix factorization
and divisive clustering for large sparse and dense matrices.

MLPACK is an intuitive, fast, scalable C++ machine learning
library, meant to be a machine learning analog to LAPACK. It
aims to implement a wide array of machine learning methods
and function as a Swiss army knife for machine learning
researchers: MLPACK is from http://www.mlpack.org/: sources are included in the package.

unix SystemRequirements: A C++11 compiler. Version 4.6.* of
g++ (as currently in Rtools) is insufficient; versions 4.8.*,
4.9.* or later will be fine.

A C++11 compiler. Version 4.6.* of g++ (as
currently in Rtools) is insufficient; versions 4.8.*, 4.9.* or
later will be fine.

'Rcpp' Integration for the 'MLPACK' Library
'MLPACK' is an intuitive, fast, scalable C++ machine learning
library, meant to be a machine learning analog to 'LAPACK'. It
aims to implement a wide array of machine learning methods
and function as a Swiss army knife for machine learning
researchers: 'MLPACK' is available from <http://www.mlpack.org/>;
sources are included in the package.
A C++11 compiler. Versions 4.8.*, 4.9.* or later of
GCC will be fine.

c(person("Junhewk", "Kim", role = c("aut", "cre"),
email = "junhewk.kim@gmail.com"),
person("Taku", "Kudo", role = c("aut"),
email = "taku@chasen.org"))
R package based on 'Rcpp' for 'MeCab': Yet Another Part-of-Speech and Morphological Analyzer.
The purpose of this package is providing a seamless developing and analyzing environment for CJK texts.
This package utilizes parallel programming for providing highly efficient text preprocessing 'posParallel()' function.
For installation, please refer to README.md file.
MeCab 0.996 (or mecab-ko 0.9.2) or higher, GNU make

Provides several efficient functions to
calculate common moving (or rolling, running) statistics for both evenly and unevenly spaced time
series: moving average, moving median, moving maximum (minimum), and so on. Built on 'C++', these
functions are apparently more efficient than those written in a traditional 'R' way and also faster
than others using package 'Rcpp'.