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R excels at computing with dates, and times. Using typed representation for your data is highly recommended not only because of the functionality offered but also because of the added safety stemming from proper representation.anytime() aims to be that general purpose converter returning a proper POSIXct (or Date) object no matter the input (provided it was somewhat parseable), relying on Boost date_time for the (efficient, performant) conversion. anydate() is an additional wrapper returning a Date object instead.

r posixct date datetime boost conversions cran c-plus-plus-11 rcppThis package provides R with access to Boost header files. Boost provides free peer-reviewed portable C++ source libraries. A large part of Boost is provided as C++ template code which is resolved entirely at compile-time without linking.This package aims to provide the most useful subset of Boost libraries for template use among CRAN packages. By placing these libraries in this package, we offer a more efficient distribution system for CRAN as replication of this code in the sources of other packages is avoided.

r c-plus-plus boost rcpp r-packageCCTZ (C++ Time Zone) is an excellent (yet small) C++11 library for translating between absolute times and civil times using the rules defined by a time zone. See its repository (as well as code) for very detailed documentation. CCTZ is being developed by Google but not an officially endorsed product.This package wraps CCTZ for use by R via Rcpp.

rcpp c-plus-plus-11 timezones timezone datetime datetimes cctz cran rThis package uses Rcpp to connect the R system to the GNU GSL, a collection of numerical routines for scientific computing, particularly its vector and matrix classes.On CRAN, here and on its package page.

gnu-gsl rcpp c-plus-plus r-package cranCompute global terminator (day/night) bands which can be overlayed as day and night regions on a ggplot2 world map. This is an Rcpp port of a pure R port (by @JoGall) of a JS leaflet plugin.

r rstats terminator geospatial daynight rcppCurrently, rstats is ONLY supported for Unix operating systems. To evaluate an R expression and directly capture its return value, one can use the parseEval function.

statistics r rcppMy main goal in this educational endeavor is to be able to use the MLPACK, Shark, and dlib C++ machine learning libraries in R for computationally intensive problems. Now, there is a RcppMLPACK, but that one apparently uses version 1 of MLPACK (which is now in version 2) and doesn't include any supervised learning methods, just unsupervised learning methods. If sudo apt-get install libshark-dev is no go, we have to build the library ourselves. See these installation instructions for more details.

c-plus-plus rcpp r notes learning-by-doingRcpp is a powerful tool to write fast C++ code to speed up R programs. However, it is not easy, or at least not straightforward, to compute numerical integration or do optimization using pure C++ code inside Rcpp. RcppNumerical integrates a number of open source numerical computing libraries into Rcpp, so that users can call convenient functions to accomplish such tasks.

rcpp optimization integration numerical-methodsThis is an implementation of Karaboga (2005) ABC optimization algorithm. It was developed upon the basic version programmed in C and distributed at the algorithm's official website (see the references). Any evident (precision) error should be blamed to the package author (not to the algorithm itself).

optimization artificial-bee-colony stochastic-optimizers r-programming r rcppThe RcppZiggurat package regroups a number of implementations of the 'Ziggurat' random number generator by Marsaglia and Tsang (JSS, 2000), both in the original version and via the update by Leong, Zhang, Lee, Luk and Villasenor (JSS, 2005). The purpose of the package is to both study and time these implementations in order to provide a fast N(0,1) generator for use by R. The generators included in R are excellent and very diligently implemented, but not generally the fastest. This package aims to close the performance gap.

ziggurat random-number-generators r-package cran rcppCheck original sparsepp repository and write-up which compares hashmap implementations.

hashmap header-only r rcpp sparseppThis repository contains an R package which is an Rcpp wrapper around the UDPipe C++ library (http://ufal.mff.cuni.cz/udpipe, https://github.com/ufal/udpipe). The package is available under the Mozilla Public License Version 2.0. Installation can be done as follows. Please visit the package documentation at https://bnosac.github.io/udpipe/en and look at the R package vignettes for further details.

udpipe nlp natural-language-processing r r-package pos-tagging dependency-parser tokenizer rcpp r-pkg conll lemmatization text-miningR interface to matplotlib via Rcpp using Python 2.7 or 3. Contains basic working interface to some basic with few options. Tested with Ubuntu 14.10 (System Python 2.7, 3.4) and Windows 7 (Anaconda Python 2.7, 3.4).

r matplotlib rcppSingle-cell mRNA sequencing can uncover novel cell-to-cell heterogeneity in gene expression levels within seemingly homogeneous populations of cells. However, these experiments are prone to high levels of technical noise, creating new challenges for identifying genes that show genuine heterogeneous expression within the group of cells under study. For a single group of cells [1]: BASiCS provides a criterion to identify highly (and lowly) variable genes within the group.

bayesian single-cell scrna-seq r rcpp rcpparmadillo gene-expression bioconductor-packageJust install it the usual way. If you want more control, unarchive it, cd to the source directory, then type R CMD INSTALL . in the console.

openmp-support r pkg rcppKernel Regularized Least Squares (KRLS) is a kernel-based, complexity-penalized method developed by Hainmueller and Hazlett (2013), and designed to minimize parametric assumptions while maintaining interpretive clarity. Here, we introduce bigKRLS, an updated version of the original KRLS R package with algorithmic and implementation improvements designed to optimize speed and memory usage. These improvements allow users to straightforwardly estimate pairwise regression models with KRLS once N > 2500. Since April 15, 2017, bigKRLS has been available on CRAN. You may also be interested in our working paper, which has been accepted by Political Analysis, and which demonstrates the utility of bigKRLS by analyzing the 2016 US presidential election. Our replication materials can be found on Dataverse and our Github repo contains examples too. C++ integration. We re-implement most major computations in the model in C++ via Rcpp and RcppArmadillo. These changes produce up to a 50% runtime decrease compared to the original R implementation.

rstats rcpp bigmemory rcpparmadillo kernel cran regularizationDensity, distribution function, quantile function and random generation for a number of univariate and multivariate distributions. This package follows naming convention that is consistent with base R, where density (or probability mass) functions, distribution functions, quantile functions and random generation functions names are followed by d*, p*, q*, and r* prefixes.

distribution multivariate-distributions r rcpp c-plus-plus c-plus-plus-11 random-generation probability statisticsNote It does not perform a 1-to-1 mapping of a palette to values. It interpolates the colours from a given palette. I’m aware there are other methods for mapping colours to values. And which do it quick too. But I can never remember them, and I find the interfaces a bit cumbersome. For example, scales::col_numeric(palette = viridisLite::viridis(5), domain = range(1:5))(1:5).

r rcpp viridis coloursVcfR is an R package intended to allow easy manipulation and visualization of variant call format (VCF) data. Functions are provided to rapidly read from and write to VCF files. Once VCF data is read into R a parser function extracts matrices from the VCF data for use with typical R functions. This information can then be used for quality control or other purposes. Additional functions provide visualization of genomic data. Once processing is complete data may be written to a VCF file or converted into other popular R objects (e.g., genlight, DNAbin). VcfR provides a link between VCF data and the R environment connecting familiar software with genomic data. VcfR is built upon two data structures.

vcf-data rstats genomics visualization rcpp population-genetics population-genomics
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