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Simple Data Frames
Provides a 'tbl_df' class (the 'tibble') that provides stricter checking and better formatting than the traditional data frame.
Parallel Programming Tools for 'Rcpp'
High level functions for parallel programming with 'Rcpp'. For example, the 'parallelFor()' function can be used to convert the work of a standard serial "for" loop into a parallel one and the 'parallelReduce()' function can be used for accumulating aggregate or other values.
Faster Dendrogram Manipulation using 'Rcpp'
Offers faster manipulation of dendrogram objects in R. A dendrogram object in R is a list structure with attributes in its nodes and leaves. Working with dendrogram objects often require a function to recursively go through all (or most) element in the list object. Naturally, such function are rather slow in R, but can become much faster thanks to 'Rcpp'.
SQL Server R Database Interface (DBI) and 'dplyr' SQL Backend
Utilises The 'jTDS' project's 'JDBC' 3.0 'SQL Server' driver to extend 'DBI' classes and methods. The package also implements a 'SQL' backend to the 'dplyr' package.
Read Rectangular Text Data
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.
Using GPUs in Statistical Genomics
Can be used to carry out permutation resampling inference in the context of RNA microarray studies.
Tools for Descriptive Statistics
A collection of miscellaneous basic statistic functions and convenience wrappers for efficiently describing data. The author's intention was to create a toolbox, which facilitates the (notoriously time consuming) first descriptive tasks in data analysis, consisting of calculating descriptive statistics, drawing graphical summaries and reporting the results. The package contains furthermore functions to produce documents using MS Word (or PowerPoint) and functions to import data from Excel. Many of the included functions can be found scattered in other packages and other sources written partly by Titans of R. The reason for collecting them here, was primarily to have them consolidated in ONE instead of dozens of packages (which themselves might depend on other packages which are not needed at all), and to provide a common and consistent interface as far as function and arguments naming, NA handling, recycling rules etc. are concerned. Google style guides were used as naming rules (in absence of convincing alternatives). The 'camel style' was consequently applied to functions borrowed from contributed R packages as well.
SciViews GUI API - Tools (wrapper for packages tools and codetools)
Set of tools aimed at wrapping some of the functionalities of the packages tools, utils and codetools into a nicer format so that an IDE can use them
Rcpp bindings for the Boost Date_Time library
This package provides R with access to Boost Date_Time functionality by using Rcpp modules. Functionality from Boost Date_Time for dates, durations (both for days and datetimes), timezones, and posix time ("ptime") is provided. The posix time implementation can support high-resolution of up to nano-second precision by using 96 bits (instead of R's 64) to present a ptime object.
Examples using RcppClassic to interface R and C++
The Rcpp package contains a C++ library that facilitates the integration of R and C++ in various ways via a rich API. This API was preceded by an earlier version which has been deprecated since 2010 (but is still supported to provide backwards compatability in the package RcppClassic). This package RcppClassicExamples provides usage examples for the older, deprecated API. There is also a corresponding package RcppExamples package with examples for the newer, current API which we strongly recommend as the basis for all new development.