|
r-ridge
|
public |
Linear and logistic ridge regression functions. Additionally includes special functions for genome-wide single-nucleotide polymorphism (SNP) data. More details can be found in <doi: 10.1002/gepi.21750> and <doi: 10.1186/1471-2105-12-372>.
|
2024-01-16 |
|
r-rhpcblasctl
|
public |
Control the number of threads on 'BLAS' (Aka 'GotoBLAS', 'OpenBLAS', 'ACML', 'BLIS' and 'MKL'). And possible to control the number of threads in 'OpenMP'. Get a number of logical cores and physical cores if feasible.
|
2024-01-16 |
|
r-revdbayes
|
public |
Provides functions for the Bayesian analysis of extreme value models. The 'rust' package <https://cran.r-project.org/package=rust> is used to simulate a random sample from the required posterior distribution. The functionality of 'revdbayes' is similar to the 'evdbayes' package <https://cran.r-project.org/package=evdbayes>, which uses Markov Chain Monte Carlo ('MCMC') methods for posterior simulation. In addition, there are functions for making inferences about the extremal index, using the models for threshold inter-exceedance times of Suveges and Davison (2010) <doi:10.1214/09-AOAS292> and Holesovsky and Fusek (2020) <doi:10.1007/s10687-020-00374-3>. Also provided are d,p,q,r functions for the Generalised Extreme Value ('GEV') and Generalised Pareto ('GP') distributions that deal appropriately with cases where the shape parameter is very close to zero.
|
2024-01-16 |
|
r-rhor
|
public |
Rho is used to test the generalization of inter rater reliability (IRR) statistics. Calculating rho starts by generating a large number of simulated, fully-coded data sets: a sizable collection of hypothetical populations, all of which have a kappa value below a given threshold -- which indicates unacceptable agreement. Then kappa is calculated on a sample from each of those sets in the collection to see if it is equal to or higher than the kappa in then real sample. If less than five percent of the distribution of samples from the simulated data sets is greater than actual observed kappa, the null hypothesis is rejected and one can conclude that if the two raters had coded the rest of the data, we would have acceptable agreement (kappa above the threshold).
|
2024-01-16 |
|
r-rgl
|
None |
Provides medium to high level functions for 3D interactive graphics, including functions modelled on base graphics (plot3d(), etc.) as well as functions for constructing representations of geometric objects (cube3d(), etc.). Output may be on screen using OpenGL, or to various standard 3D file formats including WebGL, PLY, OBJ, STL as well as 2D image formats, including PNG, Postscript, SVG, PGF.
|
2024-01-16 |
|
r-rglpk
|
public |
R interface to the GNU Linear Programming Kit. 'GLPK' is open source software for solving large-scale linear programming (LP), mixed integer linear programming ('MILP') and other related problems.
|
2024-01-16 |
|
r-rgenoud
|
public |
A genetic algorithm plus derivative optimizer.
|
2024-01-16 |
|
r-rgeode
|
public |
Provides the hybrid Bayesian method Geometric Density Estimation. On the one hand, it scales the dimension of our data, on the other it performs inference. The method is fully described in the paper "Scalable Geometric Density Estimation" by Y. Wang, A. Canale, D. Dunson (2016) <http://proceedings.mlr.press/v51/wang16e.pdf>.
|
2024-01-16 |
|
r-rgbm
|
public |
Provides an implementation of Regularized LS-TreeBoost & LAD-TreeBoost algorithm for Regulatory Network inference from any type of expression data (Microarray/RNA-seq etc).
|
2024-01-16 |
|
r-rgb
|
public |
Classes and methods to efficiently handle (slice, annotate, draw ...) genomic features (such as genes or transcripts), and an interactive interface to browse them. Performances and main features are highlighted in Mareschal et al (2014) <doi:10.1093/bioinformatics/btu185>.
|
2024-01-16 |
|
r-rfit
|
public |
Rank-based (R) estimation and inference for linear models. Estimation is for general scores and a library of commonly used score functions is included.
|
2024-01-16 |
|
r-rfast
|
public |
A collection of fast (utility) functions for data analysis. Column- and row- wise means, medians, variances, minimums, maximums, many t, F and G-square tests, many regressions (normal, logistic, Poisson), are some of the many fast functions. References: a) Tsagris M., Papadakis M. (2018). Taking R to its limits: 70+ tips. PeerJ Preprints 6:e26605v1 <doi:10.7287/peerj.preprints.26605v1>. b) Tsagris M. and Papadakis M. (2018). Forward regression in R: from the extreme slow to the extreme fast. Journal of Data Science, 16(4): 771--780. <doi:10.6339/JDS.201810_16(4).00006>.
|
2024-01-16 |
|
r-rferns
|
public |
Provides the random ferns classifier by Ozuysal, Calonder, Lepetit and Fua (2009) <doi:10.1109/TPAMI.2009.23>, modified for generic and multi-label classification and featuring OOB error approximation and importance measure as introduced in Kursa (2014) <doi:10.18637/jss.v061.i10>.
|
2024-01-16 |
|
r-rfast2
|
public |
A collection of fast statistical and utility functions for data analysis. Functions for regression, maximum likelihood, column-wise statistics and many more have been included. C++ has been utilized to speed up the functions.
|
2024-01-16 |
|
r-restrserve
|
public |
Allows to easily create high-performance full featured HTTP APIs from R functions. Provides high-level classes such as 'Request', 'Response', 'Application', 'Middleware' in order to streamline server side application development. Out of the box allows to serve requests using 'Rserve' package, but flexible enough to integrate with other HTTP servers such as 'httpuv'.
|
2024-01-16 |
|
r-rexperigen
|
public |
Provides convenience functions to communicate with an Experigen server: Experigen (<http://github.com/aquincum/experigen>) is an online framework for creating linguistic experiments, and it stores the results on a dedicated server. This package can be used to retrieve the results from the server, and it is especially helpful with registered experiments, as authentication with the server has to happen.
|
2024-01-16 |
|
r-reticulate
|
public |
Interface to 'Python' modules, classes, and functions. When calling into 'Python', R data types are automatically converted to their equivalent 'Python' types. When values are returned from 'Python' to R they are converted back to R types. Compatible with all versions of 'Python' >= 2.7.
|
2024-01-16 |
|
r-remacor
|
public |
Meta-analysis is widely used to summarize estimated effects sizes across multiple statistical tests. Standard fixed and random effect meta-analysis methods assume that the estimated of the effect sizes are statistically independent. Here we relax this assumption and enable meta-analysis when the correlation matrix between effect size estimates is known. Fixed effect meta-analysis uses the method of Lin and Sullivan (2009) <doi:10.1016/j.ajhg.2009.11.001>, and random effects meta-analysis uses the method of Han, et al. <doi:10.1093/hmg/ddw049>.
|
2024-01-16 |
|
r-remote
|
public |
Empirical orthogonal teleconnections in R. 'remote' is short for 'R(-based) EMpirical Orthogonal TEleconnections'. It implements a collection of functions to facilitate empirical orthogonal teleconnection analysis. Empirical Orthogonal Teleconnections (EOTs) denote a regression based approach to decompose spatio-temporal fields into a set of independent orthogonal patterns. They are quite similar to Empirical Orthogonal Functions (EOFs) with EOTs producing less abstract results. In contrast to EOFs, which are orthogonal in both space and time, EOT analysis produces patterns that are orthogonal in either space or time.
|
2024-01-16 |
|
r-reshape
|
None |
Flexibly restructure and aggregate data using just two functions: melt and cast.
|
2024-01-16 |
|
r-reshape2
|
None |
Flexibly restructure and aggregate data using just two functions: melt and 'dcast' (or 'acast').
|
2024-01-16 |
|
r-resemble
|
public |
Functions for dissimilarity analysis and memory-based learning (MBL, a.k.a local modeling) in complex spectral data sets. Most of these functions are based on the methods presented in Ramirez-Lopez et al. (2013) <doi:10.1016/j.geoderma.2012.12.014>.
|
2024-01-16 |
|
r-repolr
|
public |
Fits linear models to repeated ordinal scores using GEE methodology.
|
2024-01-16 |
|
r-rehh
|
public |
Population genetic data such as 'Single Nucleotide Polymorphisms' (SNPs) is often used to identify genomic regions that have been under recent natural or artificial selection and might provide clues about the molecular mechanisms of adaptation. One approach, the concept of an 'Extended Haplotype Homozygosity' (EHH), introduced by (Sabeti 2002) <doi:10.1038/nature01140>, has given rise to several statistics designed for whole genome scans. The package provides functions to compute three of these, namely: 'iHS' (Voight 2006) <doi:10.1371/journal.pbio.0040072> for detecting positive or 'Darwinian' selection within a single population as well as 'Rsb' (Tang 2007) <doi:10.1371/journal.pbio.0050171> and 'XP-EHH' (Sabeti 2007) <doi:10.1038/nature06250>, targeted at differential selection between two populations. Various plotting functions are included to facilitate visualization and interpretation of these statistics.
|
2024-01-16 |
|
r-repfdr
|
public |
Estimation of Bayes and local Bayes false discovery rates for replicability analysis (Heller & Yekutieli, 2014 <doi:10.1214/13-AOAS697> ; Heller at al., 2015 <doi: 10.1093/bioinformatics/btu434>).
|
2024-01-16 |
|
r-regsem
|
public |
Uses both ridge and lasso penalties (and extensions) to penalize specific parameters in structural equation models. The package offers additional cost functions, cross validation, and other extensions beyond traditional structural equation models. Also contains a function to perform exploratory mediation (XMed).
|
2024-01-16 |
|
r-rem
|
public |
Calculate endogenous network effects in event sequences and fit relational event models (REM): Using network event sequences (where each tie between a sender and a target in a network is time-stamped), REMs can measure how networks form and evolve over time. Endogenous patterns such as popularity effects, inertia, similarities, cycles or triads can be calculated and analyzed over time.
|
2024-01-16 |
|
r-relsurv
|
public |
Contains functions for analysing relative survival data, including nonparametric estimators of net (marginal relative) survival, relative survival ratio, crude mortality, methods for fitting and checking additive and multiplicative regression models, transformation approach, methods for dealing with population mortality tables. Work has been described in Pohar Perme, Pavlic (2018) <doi:10.18637/jss.v087.i08>.
|
2024-01-16 |
|
r-reins
|
public |
Functions from the book "Reinsurance: Actuarial and Statistical Aspects" (2017) by Hansjoerg Albrecher, Jan Beirlant and Jef Teugels <https://www.wiley.com/en-us/Reinsurance%3A+Actuarial+and+Statistical+Aspects-p-9780470772683>.
|
2024-01-16 |
|
r-relatedness
|
public |
Inference of relatedness coefficients from a bi-allelic genotype matrix using a Maximum Likelihood estimation, Laporte, F., Charcosset, A. and Mary-Huard, T. (2017) <doi:10.1111/biom.12634>.
|
2024-01-16 |
|
r-reddyproc
|
public |
Standard and extensible Eddy-Covariance data post-processing (Wutzler et al. (2018) <doi:10.5194/bg-15-5015-2018>) includes uStar-filtering, gap-filling, and flux-partitioning. The Eddy-Covariance (EC) micrometeorological technique quantifies continuous exchange fluxes of gases, energy, and momentum between an ecosystem and the atmosphere. It is important for understanding ecosystem dynamics and upscaling exchange fluxes. (Aubinet et al. (2012) <doi:10.1007/978-94-007-2351-1>). This package inputs pre-processed (half-)hourly data and supports further processing. First, a quality-check and filtering is performed based on the relationship between measured flux and friction velocity (uStar) to discard biased data (Papale et al. (2006) <doi:10.5194/bg-3-571-2006>). Second, gaps in the data are filled based on information from environmental conditions (Reichstein et al. (2005) <doi:10.1111/j.1365-2486.2005.001002.x>). Third, the net flux of carbon dioxide is partitioned into its gross fluxes in and out of the ecosystem by night-time based and day-time based approaches (Lasslop et al. (2010) <doi:10.1111/j.1365-2486.2009.02041.x>).
|
2024-01-16 |
|
r-recordlinkage
|
public |
Provides functions for linking and deduplicating data sets. Methods based on a stochastic approach are implemented as well as classification algorithms from the machine learning domain. For details, see our paper "The RecordLinkage Package: Detecting Errors in Data" Sariyar M / Borg A (2010) <doi:10.32614/RJ-2010-017>.
|
2024-01-16 |
|
r-readxlsb
|
public |
Import data from 'Excel' binary (.xlsb) workbooks into R.
|
2024-01-16 |
|
r-regnet
|
public |
Network-based regularization has achieved success in variable selection for high-dimensional biological data due to its ability to incorporate correlations among genomic features. This package provides procedures of network-based variable selection for generalized linear models (Ren et al. (2017) <doi:10.1186/s12863-017-0495-5> and Ren et al.(2019) <doi:10.1002/gepi.22194>). Continuous, binary, and survival response are supported. Robust network-based methods are available for continuous and survival responses.
|
2024-01-16 |
|
r-reglogit
|
public |
Regularized (polychotomous) logistic regression by Gibbs sampling. The package implements subtly different MCMC schemes with varying efficiency depending on the data type (binary v. binomial, say) and the desired estimator (regularized maximum likelihood, or Bayesian maximum a posteriori/posterior mean, etc.) through a unified interface. For details, see Gramacy & Polson (2012 <doi:10.1214/12-BA719>).
|
2024-01-16 |
|
r-refinr
|
public |
These functions take a character vector as input, identify and cluster similar values, and then merge clusters together so their values become identical. The functions are an implementation of the key collision and ngram fingerprint algorithms from the open source tool Open Refine <https://openrefine.org/>. More info on key collision and ngram fingerprint can be found here <https://docs.openrefine.org/next/technical-reference/clustering-in-depth/>.
|
2024-01-16 |
|
r-redm
|
public |
An implementation of 'EDM' algorithms based on research software developed for internal use at the Sugihara Lab ('UCSD/SIO'). The package is implemented with 'Rcpp' wrappers around the 'cppEDM' library. It implements the 'simplex' projection method from Sugihara & May (1990) <doi:10.1038/344734a0>, the 'S-map' algorithm from Sugihara (1994) <doi:10.1098/rsta.1994.0106>, convergent cross mapping described in Sugihara et al. (2012) <doi:10.1126/science.1227079>, and, 'multiview embedding' described in Ye & Sugihara (2016) <doi:10.1126/science.aag0863>.
|
2024-01-16 |
|
r-recurse
|
public |
Computes revisitation metrics for trajectory data, such as the number of revisitations for each location as well as the time spent for that visit and the time since the previous visit. Also includes functions to plot data.
|
2024-01-16 |
|
r-recosystem
|
public |
R wrapper of the 'libmf' library <https://www.csie.ntu.edu.tw/~cjlin/libmf/> for recommender system using matrix factorization. It is typically used to approximate an incomplete matrix using the product of two matrices in a latent space. Other common names for this task include "collaborative filtering", "matrix completion", "matrix recovery", etc. High performance multi-core parallel computing is supported in this package.
|
2024-01-16 |
|
r-readods
|
public |
Read ODS (OpenDocument Spreadsheet) into R as data frame. Also support writing data frame into ODS file.
|
2024-01-16 |
|
r-reconstructr
|
public |
Functions to reconstruct sessions from web log or other user trace data and calculate various metrics around them, producing tabular, output that is compatible with 'dplyr' or 'data.table' centered processes.
|
2024-01-16 |
|
r-rebmix
|
public |
Random univariate and multivariate finite mixture model generation, estimation, clustering, latent class analysis and classification. Variables can be continuous, discrete, independent or dependent and may follow normal, lognormal, Weibull, gamma, Gumbel, binomial, Poisson, Dirac, uniform or circular von Mises parametric families.
|
2024-01-16 |
|
r-realvams
|
public |
Fits a multivariate value-added model (VAM), see Broatch, Green, and Karl (2018) <doi:10.32614/RJ-2018-033> and Broatch and Lohr (2012) <doi:10.3102/1076998610396900>, with normally distributed test scores and a binary outcome indicator. A pseudo-likelihood approach, Wolfinger (1993) <doi:10.1080/00949659308811554>, is used for the estimation of this joint generalized linear mixed model. The inner loop of the pseudo-likelihood routine (estimation of a linear mixed model) occurs in the framework of the EM algorithm presented by Karl, Yang, and Lohr (2013) <DOI:10.1016/j.csda.2012.10.004>. This material is based upon work supported by the National Science Foundation under grants DRL-1336027 and DRL-1336265.
|
2024-01-16 |
|
r-readstata13
|
public |
Function to read and write the 'Stata' file format.
|
2024-01-16 |
|
r-readxl
|
public |
Import excel files into R. Supports '.xls' via the embedded 'libxls' C library <https://github.com/libxls/libxls> and '.xlsx' via the embedded 'RapidXML' C++ library <https://rapidxml.sourceforge.net/>. Works on Windows, Mac and Linux without external dependencies.
|
2024-01-16 |
|
r-readsdmx
|
public |
Read Statistical Data and Metadata Exchange (SDMX) XML data. This the main transmission format used in official statistics. Data can be imported from local SDMX-ML files or a SDMX web-service and will be read in 'as is' into a dataframe object. The 'RapidXML' C++ library <https://rapidxml.sourceforge.net/> is used to parse the XML data.
|
2024-01-16 |
|
r-readr
|
public |
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.
|
2024-01-16 |
|
r-rcppsimdjson
|
public |
The 'JSON' format is ubiquitous for data interchange, and the 'simdjson' library written by Daniel Lemire (and many contributors) provides a high-performance parser for these files which by relying on parallel 'SIMD' instruction manages to parse these files as faster than disk speed. See the <arXiv:1902.08318> paper for more details about 'simdjson'. This package parses 'JSON' from string, file, or remote URLs under a variety of settings.
|
2024-01-16 |
|
r-readobj
|
public |
Wraps 'tiny_obj_loader' C++ library for reading the 'Wavefront' OBJ 3D file format including both mesh objects and materials files. The resultant R objects are either structured to match the 'tiny_obj_loader' internal data representation or in a form directly compatible with the 'rgl' package.
|
2024-01-16 |
|
r-read.dbc
|
public |
Functions for reading and decompressing the DBC (compressed DBF) files. Please note that this is the file format used by the Brazilian Ministry of Health (DATASUS) to publish healthcare datasets. It is not related to the FoxPro or CANdb DBC file formats.
|
2024-01-16 |
