|
r-git2r
|
public |
Interface to the 'libgit2' library, which is a pure C implementation of the 'Git' core methods. Provides access to 'Git' repositories to extract data and running some basic 'Git' commands.
|
2025-03-25 |
|
r-fracdiff
|
public |
Maximum likelihood estimation of the parameters of a fractionally differenced ARIMA(p,d,q) model (Haslett and Raftery, Appl.Statistics, 1989).
|
2025-03-25 |
|
r-foreign
|
public |
Reading and writing data stored by some versions of 'Epi Info', 'Minitab', 'S', 'SAS', 'SPSS', 'Stata', 'Systat', 'Weka', and for reading and writing some 'dBase' files.
|
2025-03-25 |
|
r-forecast
|
public |
Methods and tools for displaying and analysing univariate time series forecasts including exponential smoothing via state space models and automatic ARIMA modelling.
|
2025-03-25 |
|
r-feather
|
public |
Read and write feather files, a lightweight binary columnar data store designed for maximum speed.
|
2025-03-25 |
|
r-essentials
|
public |
Some essential packages for working with R
|
2025-03-25 |
|
r-e1071
|
public |
Functions for latent class analysis, short time Fourier transform, fuzzy clustering, support vector machines, shortest path computation, bagged clustering, naive Bayes classifier, ...
|
2025-03-25 |
|
r-dplyr
|
public |
A fast, consistent tool for working with data frame like objects, both in memory and out of memory.
|
2025-03-25 |
|
r-dimred
|
public |
A collection of dimensionality reduction techniques from R packages and provides a common interface for calling the methods.
|
2025-03-25 |
|
r-digest
|
public |
Implementation of a function 'digest()' for the creation of hash digests of arbitrary R objects (using the 'md5', 'sha-1', 'sha-256', 'crc32', 'xxhash' and 'murmurhash' algorithms) permitting easy comparison of R language objects, as well as a function 'hmac()' to create hash-based message authentication code. Please note that this package is not meant to be deployed for cryptographic purposes for which more comprehensive (and widely tested) libraries such as 'OpenSSL' should be used.
|
2025-03-25 |
|
r-devtools
|
public |
Collection of package development tools.
|
2025-03-25 |
|
r-ddalpha
|
public |
Contains procedures for depth-based supervised learning, which are entirely non-parametric, in particular the DDalpha-procedure (Lange, Mosler and Mozharovskyi, 2014 <doi:10.1007/s00362-012-0488-4>). The training data sample is transformed by a statistical depth function to a compact low-dimensional space, where the final classification is done. It also offers an extension to functional data and routines for calculating certain notions of statistical depth functions. 50 multivariate and 5 functional classification problems are included.
|
2025-03-25 |
|
r-data.table
|
public |
Fast aggregation of large data (e.g. 100GB in RAM), fast ordered joins, fast add/modify/delete of columns by group using no copies at all, list columns, friendly and fast character-separated-value read/write. Offers a natural and flexible syntax, for faster development.
|
2025-03-25 |
|
r-curl
|
public |
The curl() and curl_download() functions provide highly configurable drop-in replacements for base url() and download.file() with better performance, support for encryption (https, ftps), gzip compression, authentication, and other 'libcurl' goodies. The core of the package implements a framework for performing fully customized requests where data can be processed either in memory, on disk, or streaming via the callback or connection interfaces. Some knowledge of 'libcurl' is recommended; for a more-user-friendly web client see the 'httr' package which builds on this package with http specific tools and logic.
|
2025-03-25 |
|
r-commonmark
|
public |
The CommonMark specification defines a rationalized version of markdown syntax. This package uses the 'cmark' reference implementation for converting markdown text into various formats including html, latex and groff man. In addition it exposes the markdown parse tree in xml format. The latest version of this package also adds support for Github extensions including tables, autolinks and strikethrough text.
|
2025-03-25 |
|
r-colorspace
|
public |
Carries out mapping between assorted color spaces including RGB, HSV, HLS, CIEXYZ, CIELUV, HCL (polar CIELUV), CIELAB and polar CIELAB. Qualitative, sequential, and diverging color palettes based on HCL colors are provided along with an interactive palette picker (with either a Tcl/Tk or a shiny GUI).
|
2025-03-25 |
|
r-coin
|
public |
Conditional inference procedures for the general independence problem including two-sample, K-sample (non-parametric ANOVA), correlation, censored, ordered and multivariate problems.
|
2025-03-25 |
|
r-cluster
|
public |
Methods for Cluster analysis. Much extended the original from Peter Rousseeuw, Anja Struyf and Mia Hubert, based on Kaufman and Rousseeuw (1990) "Finding Groups in Data".
|
2025-03-25 |
|
r-classint
|
public |
Selected commonly used methods for choosing univariate class intervals for mapping or other graphics purposes.
|
2025-03-25 |
|
r-class
|
public |
Various functions for classification, including k-nearest neighbour, Learning Vector Quantization and Self-Organizing Maps.
|
2025-03-25 |
|
r-chron
|
public |
Provides chronological objects which can handle dates and times.
|
2025-03-25 |
|
r-checkmate
|
public |
Tests and assertions to perform frequent argument checks. A substantial part of the package was written in C to minimize any worries about execution time overhead.
|
2025-03-25 |
|
r-catools
|
public |
Contains several basic utility functions including: moving (rolling, running) window statistic functions, read/write for GIF and ENVI binary files, fast calculation of AUC, LogitBoost classifier, base64 encoder/decoder, round-off-error-free sum and cumsum, etc.
|
2025-03-25 |
|
r-caret
|
public |
Misc functions for training and plotting classification and regression models.
|
2025-03-25 |
|
r-brglm
|
public |
Fit generalized linear models with binomial responses using either an adjusted-score approach to bias reduction or maximum penalized likelihood where penalization is by Jeffreys invariant prior. These procedures return estimates with improved frequentist properties (bias, mean squared error) that are always finite even in cases where the maximum likelihood estimates are infinite (data separation). Fitting takes place by fitting generalized linear models on iteratively updated pseudo-data. The interface is essentially the same as 'glm'. More flexibility is provided by the fact that custom pseudo-data representations can be specified and used for model fitting. Functions are provided for the construction of confidence intervals for the reduced-bias estimates.
|
2025-03-25 |
