Artifacts using Rcpp version 0.12.5-renjin-7

A fast, consistent tool for working with data frame like objects, both in memory and out of memory.

A set of tools that solves a common set of problems: you need to break a big problem down into manageable pieces, operate on each piece and then put all the pieces back together. For example, you might want to fit a model to each spatial location or time point in your study, summarise data by panels or collapse high-dimensional arrays to simpler summary statistics. The development of 'plyr' has been generously supported by 'Becton Dickinson'.

'Armadillo' is a templated C++ linear algebra library (by Conrad Sanderson) that aims towards a good balance between speed and ease of use. Integer, floating point and complex numbers are supported, as well as a subset of trigonometric and statistics functions. Various matrix decompositions are provided through optional integration with LAPACK and ATLAS libraries. The 'RcppArmadillo' package includes the header files from the templated 'Armadillo' library. Thus users do not need to install 'Armadillo' ...

Flexibly restructure and aggregate data using just two functions: melt and 'dcast' (or 'acast').

Provides a 'tbl_df' class (the 'tibble') that provides stricter checking and better formatting than the traditional data frame.

An evolution of 'reshape2'. It's designed specifically for data tidying (not general reshaping or aggregating) and works well with 'dplyr' data pipelines.

Graphical scales map data to aesthetics, and provide methods for automatically determining breaks and labels for axes and legends.

A complete and consistent functional programming toolkit for R.

The goal of 'readr' is to provide a fast and friendly way to read rectangular data (like 'csv', 'tsv', and 'fwf'). It is designed to flexibly parse many types of data found in the wild, while still cleanly failing when data unexpectedly changes.

Renjin Xml2