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r-smaa
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public |
Implementation of the Stochastic Multi-Criteria Acceptability Analysis (SMAA) family of Multiple Criteria Decision Analysis (MCDA) methods.
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2023-06-16 |
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r-slfm
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Set of tools to find coherent patterns in microarray data using a Bayesian Sparse Latent Factor Model - SLFM; see Duarte and Mayrink (2015) <DOI:10.1007/978-3-319-12454-4_15>. Considerable effort has been put into making slfm fast and memory efficient, turning it an interesting alternative to simpler methods in terms of execution time. It implements versions of the SLFM based on two type of mixtures priors for the loadings: one relying on a degenerate component at zero and the other using a small variance normal distribution for the spike part of the mixture. It also implements additional functions to allow pre-processing procedures for the data and to fit the model for a large number of probesets or genes.
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2023-06-16 |
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r-sdpt3r
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Solves the general Semi-Definite Linear Programming formulation using an R implementation of SDPT3 (K.C. Toh, M.J. Todd, and R.H. Tutuncu (1999) <doi:10.1080/10556789908805762>). This includes problems such as the nearest correlation matrix problem (Higham (2002) <doi:10.1093/imanum/22.3.329>), D-optimal experimental design (Smith (1918) <doi:10.2307/2331929>), Distance Weighted Discrimination (Marron and Todd (2012) <doi:10.1198/016214507000001120>), as well as graph theory problems including the maximum cut problem. Technical details surrounding SDPT3 can be found in R.H Tutuncu, K.C. Toh, and M.J. Todd (2003) <doi:10.1007/s10107-002-0347-5>.
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2023-06-16 |
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r-rcppdist
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The 'Rcpp' package provides a C++ library to make it easier to use C++ with R. R and 'Rcpp' provide functions for a variety of statistical distributions. Several R packages make functions available to R for additional statistical distributions. However, to access these functions from C++ code, a costly call to the R functions must be made. 'RcppDist' provides a header-only C++ library with functions for additional statistical distributions that can be called from C++ when writing code using 'Rcpp' or 'RcppArmadillo'. Functions are available that return a 'NumericVector' as well as doubles, and for multivariate or matrix distributions, 'Armadillo' vectors and matrices. 'RcppDist' provides functions for the following distributions: the four parameter beta distribution; the location- scale t distribution; the truncated normal distribution; the truncated t distribution; a truncated location-scale t distribution; the triangle distribution; the multivariate normal distribution*; the multivariate t distribution*; the Wishart distribution*; and the inverse Wishart distribution*. Distributions marked with an asterisk rely on 'RcppArmadillo'.
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2023-06-16 |
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r-rblpapi
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public |
An R Interface to 'Bloomberg' is provided via the 'Blp API'.
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2023-06-16 |
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r-rapidatetime
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Access to the C-level R date and datetime code is provided for C-level API use by other packages via registration of native functions. Client packages simply include a single header 'RApiDatetime.h' provided by this package, and also 'import' it. The R Core group is the original author of the code made available with slight modifications by this package.
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2023-06-16 |
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r-rann
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Finds the k nearest neighbours for every point in a given dataset in O(N log N) time using Arya and Mount's ANN library (v1.1.3). There is support for approximate as well as exact searches, fixed radius searches and 'bd' as well as 'kd' trees. The distance is computed using the L2 (Euclidean) metric. Please see package 'RANN.L1' for the same functionality using the L1 (Manhattan, taxicab) metric.
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2023-06-16 |
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r-rankcluster
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Implementation of a model-based clustering algorithm for ranking data (C. Biernacki, J. Jacques (2013) <doi:10.1016/j.csda.2012.08.008>). Multivariate rankings as well as partial rankings are taken into account. This algorithm is based on an extension of the Insertion Sorting Rank (ISR) model for ranking data, which is a meaningful and effective model parametrized by a position parameter (the modal ranking, quoted by mu) and a dispersion parameter (quoted by pi). The heterogeneity of the rank population is modelled by a mixture of ISR, whereas conditional independence assumption is considered for multivariate rankings.
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2023-06-16 |
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r-randomforestsrc
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Fast OpenMP parallel computing of Breiman's random forests for survival, competing risks, regression and classification based on Ishwaran and Kogalur's popular random survival forests (RSF) package. Handles missing data and now includes multivariate, unsupervised forests, quantile regression and solutions for class imbalanced data. New fast interface using subsampling and confidence regions for variable importance.
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2023-06-16 |
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r-randaes
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The deterministic part of the Fortuna cryptographic pseudorandom number generator, described by Schneier & Ferguson "Practical Cryptography"
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2023-06-16 |
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r-qtlrel
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This software provides tools for quantitative trait mapping in populations such as advanced intercross lines where relatedness among individuals should not be ignored. It can estimate background genetic variance components, impute missing genotypes, simulate genotypes, perform a genome scan for putative quantitative trait loci (QTL), and plot mapping results. It also has functions to calculate identity coefficients from pedigrees, especially suitable for pedigrees that consist of a large number of generations, or estimate identity coefficients from genotypic data in certain circumstances.
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2023-06-16 |
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r-qtl
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Analysis of experimental crosses to identify genes (called quantitative trait loci, QTLs) contributing to variation in quantitative traits.
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2023-06-16 |
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r-qcapro
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Provides advanced functionality for performing configurational comparative research with Qualitative Comparative Analysis (QCA), including crisp-set, multi-value, and fuzzy-set QCA. It also offers advanced tools for sensitivity diagnostics and methodological evaluations of QCA.
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2023-06-16 |
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r-glmmsr
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Conduct inference about generalized linear mixed models, with a choice about which method to use to approximate the likelihood. In addition to the Laplace and adaptive Gaussian quadrature approximations, which are borrowed from 'lme4', the likelihood may be approximated by the sequential reduction approximation, or an importance sampling approximation. These methods provide an accurate approximation to the likelihood in some situations where it is not possible to use adaptive Gaussian quadrature.
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2023-06-16 |
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r-glmgraph
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We propose to use sparse regression model to achieve variable selection while accounting for graph-constraints among coefficients. Different linear combination of a sparsity penalty(L1) and a smoothness(MCP) penalty has been used, which induces both sparsity of the solution and certain smoothness on the linear coefficients.
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2023-06-16 |
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r-glmaspu
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Several tests for high dimensional generalized linear models have been proposed recently. In this package, we implemented a new test called adaptive sum of powered score (aSPU) for high dimensional generalized linear models, which is often more powerful than the existing methods in a wide scenarios. We also implemented permutation based version of several existing methods for research purpose. We recommend users use the aSPU test for their real testing problem. You can learn more about the tests implemented in the package via the following papers: 1. Pan, W., Kim, J., Zhang, Y., Shen, X. and Wei, P. (2014) <DOI:10.1534/genetics.114.165035> A powerful and adaptive association test for rare variants, Genetics, 197(4). 2. Guo, B., and Chen, S. X. (2016) <DOI:10.1111/rssb.12152>. Tests for high dimensional generalized linear models. Journal of the Royal Statistical Society: Series B. 3. Goeman, J. J., Van Houwelingen, H. C., and Finos, L. (2011) <DOI:10.1093/biomet/asr016>. Testing against a high-dimensional alternative in the generalized linear model: asymptotic type I error control. Biometrika, 98(2).
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2023-06-16 |
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r-gglasso
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A unified algorithm, blockwise-majorization-descent (BMD), for efficiently computing the solution paths of the group-lasso penalized least squares, logistic regression, Huberized SVM and squared SVM. The package is an implementation of Yang, Y. and Zou, H. (2015) DOI: <doi:10.1007/s11222-014-9498-5>.
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2023-06-16 |
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r-gdpc
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Functions to compute the Generalized Dynamic Principal Components introduced in Peña and Yohai (2016) <DOI:10.1080/01621459.2015.1072542>.
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2023-06-16 |
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r-gam
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Functions for fitting and working with generalized additive models, as described in chapter 7 of "Statistical Models in S" (Chambers and Hastie (eds), 1991), and "Generalized Additive Models" (Hastie and Tibshirani, 1990).
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2023-06-16 |
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r-fpop
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A dynamic programming algorithm for the fast segmentation of univariate signals into piecewise constant profiles. The 'fpop' package is a wrapper to a C++ implementation of the fpop (Functional Pruning Optimal Partioning) algorithm described in Maidstone et al. 2017 <doi:10.1007/s11222-016-9636-3>. The problem of detecting changepoints in an univariate sequence is formulated in terms of minimising the mean squared error over segmentations. The fpop algorithm exactly minimizes the mean squared error for a penalty linear in the number of changepoints.
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2023-06-16 |
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r-spam64
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Provides the Fortran code of the R package 'spam' with 64-bit integers. Loading this package together with the R package spam enables the sparse matrix class spam to handle huge sparse matrices with more than 2^31-1 non-zero elements.
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2023-06-16 |
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r-som
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Self-Organizing Map (with application in gene clustering).
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2023-06-16 |
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r-soda
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Functions, examples and other software related to the book "Software for Data Analysis: Programming with R". See package?SoDA for an overview.
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2023-06-16 |
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r-smma
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Efficient design matrix free procedure for solving a soft maximin problem for large scale array-tensor structured models. Currently Lasso and SCAD penalized estimation is implemented.
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2023-06-16 |
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r-slope
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Efficient procedures for Sorted L1 Penalized Estimation (SLOPE). The sorted L1 norm is useful for statistical estimation and testing, particularly for variable selection in the linear model.
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2023-06-16 |
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r-simcdm
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Provides efficient R and 'C++' routines to simulate cognitive diagnostic model data for Deterministic Input, Noisy "And" Gate ('DINA') and reduced Reparameterized Unified Model ('rRUM') from Culpepper and Hudson (2017) <doi: 10.1177/0146621617707511>, Culpepper (2015) <doi:10.3102/1076998615595403>, and de la Torre (2009) <doi:10.3102/1076998607309474>.
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2023-06-16 |
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r-sgl
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Fit a regularized generalized linear model via penalized maximum likelihood. The model is fit for a path of values of the penalty parameter. Fits linear, logistic and Cox models.
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2023-06-16 |
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r-sequoia
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Fast multi-generational pedigree inference from incomplete data on hundreds of SNPs, including parentage assignment and sibship clustering. See Huisman (2017) (DOI:10.1111/1755-0998.12665, citation('sequoia')) for more information.
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2023-06-16 |
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r-searchtrees
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This package provides an implementation of the QuadTree data structure. It uses this to implement fast k-Nearest Neighbor and Rectangular range lookups in 2 dimenions. The primary target is high performance interactive graphics.
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2023-06-16 |
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r-rcppgetconf
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The 'getconf' command-line tool provided by 'libc' allows querying of a large number of system variables. This package provides similar functionality.
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2023-06-16 |
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r-rcsdp
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R interface to the CSDP semidefinite programming library. Installs version 6.1.1 of CSDP from the COIN-OR website if required. An existing installation of CSDP may be used by passing the proper configure arguments to the installation command. See the INSTALL file for further details.
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2023-06-16 |
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r-rcppde
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An efficient C++ based implementation of the 'DEoptim' function which performs global optimization by differential evolution. Its creation was motivated by trying to see if the old approximation "easier, shorter, faster: pick any two" could in fact be extended to achieving all three goals while moving the code from plain old C to modern C++. The initial version did in fact do so, but a good part of the gain was due to an implicit code review which eliminated a few inefficiencies which have since been eliminated in 'DEoptim'.
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2023-06-16 |
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r-rbeast
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Interpretation of time series data is affected by model choices. Different models can give different or even contradicting estimates of patterns, trends, and mechanisms for the same data--a limitation alleviated by the Bayesian estimator of abrupt change,seasonality, and trend (BEAST) of this package. BEAST seeks to improve time series decomposition by forgoing the "single-best-model" concept and embracing all competing models into the inference via a Bayesian model averaging scheme. It is a flexible tool to uncover abrupt changes (i.e., change-points), cyclic variations (e.g., seasonality), and nonlinear trends in time-series observations. BEAST not just tells when changes occur but also quantifies how likely the detected changes are true. It detects not just piecewise linear trends but also arbitrary nonlinear trends. BEAST is applicable to real-valued time series data of all kinds, be it for remote sensing, economics, climate sciences, ecology, and hydrology. Example applications include its use to identify regime shifts in ecological data, map forest disturbance and land degradation from satellite imagery, detect market trends in economic data, pinpoint anomaly and extreme events in climate data, and unravel system dynamics in biological data. Details on BEAST are reported in Zhao et al. (2019).
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2023-06-16 |
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r-rasterkernelestimates
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Performs kernel based estimates on in-memory raster images from the raster package. These kernel estimates include local means variances, modes, and quantiles. All results are in the form of raster images, preserving original resolution and projection attributes.
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2023-06-16 |
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r-rapiserialize
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This package provides other packages with access to the internal R serialization code. Access to this code is provided at the C function level by using the registration of native function mechanism. Client packages simply include a single header file RApiSerializeAPI.h provided by this package. . This packages builds on the Rhpc package by Junji Nakano and Ei-ji Nakama which also includes a (partial) copy of the file src/main/serialize.c from R itself. . The R Core group is the original author of the serialization code made available by this package.
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2023-06-16 |
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r-gigrvg
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Generator and density function for the Generalized Inverse Gaussian (GIG) distribution.
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2023-06-16 |
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r-getpass
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A micro-package for reading "passwords", i.e. reading user input with masking, so that the input is not displayed as it is typed. Currently we have support for 'RStudio', the command line (every OS), and any platform where 'tcltk' is present.
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2023-06-16 |
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r-geosphere
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Spherical trigonometry for geographic applications. That is, compute distances and related measures for angular (longitude/latitude) locations.
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2023-06-16 |
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r-genkern
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Computes generalised KDEs
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2023-06-16 |
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r-geigen
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Functions to compute generalized eigenvalues and eigenvectors, the generalized Schur decomposition and the generalized Singular Value Decomposition of a matrix pair, using Lapack routines.
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2023-06-16 |
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r-geepack
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Generalized estimating equations solver for parameters in mean, scale, and correlation structures, through mean link, scale link, and correlation link. Can also handle clustered categorical responses.
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2023-06-16 |
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r-gamboost
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This package provides routines for fitting generalized linear and and generalized additive models by likelihood based boosting, using penalized B-splines
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2023-06-16 |
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r-fourpno
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Estimate Barton & Lord's (1981) <doi:10.1002/j.2333-8504.1981.tb01255.x> four parameter IRT model with lower and upper asymptotes using Bayesian formulation described by Culpepper (2016) <doi:10.1007/s11336-015-9477-6>.
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2023-06-16 |
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r-flashclust
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Fast implementation of hierarchical clustering
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2023-06-16 |
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r-spatiotemporal
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Utilities that estimate, predict and cross-validate the spatio-temporal model developed for MESA Air.
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2023-06-16 |
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r-soundexbr
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The SoundexBR package provides an algorithm for decoding names into phonetic codes, as pronounced in Portuguese. The goal is for homophones to be encoded to the same representation so that they can be matched despite minor differences in spelling. The algorithm mainly encodes consonants; a vowel will not be encoded unless it is the first letter. The soundex code resultant consists of a four digits long string composed by one letter followed by three numerical digits: the letter is the first letter of the name, and the digits encode the remaining consonants.
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2023-06-16 |
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r-smoothhazard
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Estimation of two-state (survival) models and irreversible illness- death models with possibly interval-censored,left-truncated and right-censored data. Proportional intensities regression models can be specified to allow for covariates effects separately for each transition. We use either a parametric approach with Weibull baseline intensities or a semi-parametric approach with M-splines approximation of baseline intensities in order to obtain smooth estimates of the hazard functions. Parameter estimates are obtained by maximum likelihood in the parametric approach and by penalized maximum likelihood in the semi-parametric approach.
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2023-06-16 |
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r-sim.diffproc
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It provides users with a wide range of tools to simulate, estimate, analyze, and visualize the dynamics of stochastic differential systems in both forms Ito and Stratonovich. Statistical analysis with parallel Monte Carlo and moment equations methods of SDE's. Enabled many searchers in different domains to use these equations to modeling practical problems in financial and actuarial modeling and other areas of application, e.g., modeling and simulate of first passage time problem in shallow water using the attractive center (Boukhetala K, 1996) ISBN:1-56252-342-2.
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2023-06-16 |
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r-sequences
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Educational package used in R courses to illustrate object-oriented programming and package development. Using biological sequences (DNA and RNA) as a working example.
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2023-06-16 |
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r-seqcbs
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This is a method for DNA Copy Number Profiling using Next-Generation Sequencing. It has new model and test statistics based on non-homogeneous Poisson Processes with change point models. It uses an adaptation of Circular Binary Segmentation. Also included are methods for point-wise Bayesian Confidence Interval and model selection method for the change-point model. A case and a control sample reads (normal and tumor) are required.
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2023-06-16 |
