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r-ramcmc
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Function for adapting the shape of the random walk Metropolis proposal as specified by robust adaptive Metropolis algorithm by Vihola (2012) <DOI:10.1007/s11222-011-9269-5>. Package also includes fast functions for rank-one Cholesky update and downdate. These functions can be used directly from R or the corresponding C++ header files can be easily linked to other R packages.
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2023-06-16 |
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r-pyinit
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Deterministic Pena-Yohai initial estimator for robust S estimators of regression. The procedure is described in detail in Pena, D., & Yohai, V. (1999) <doi:10.2307/2670164>.
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2023-06-16 |
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r-gifi
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Implements categorical principal component analysis ('PRINCALS'), multiple correspondence analysis ('HOMALS'), monotone regression analysis ('MORALS'). It replaces the 'homals' package.
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2023-06-16 |
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r-geojsonr
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Includes functions for processing GeoJson objects <https://en.wikipedia.org/wiki/GeoJSON> relying on 'RFC 7946' <https://tools.ietf.org/pdf/rfc7946.pdf>. The geojson encoding is based on 'json11', a tiny JSON library for 'C++11' <https://github.com/dropbox/json11>. Furthermore, the source code is exported in R through the 'Rcpp' and 'RcppArmadillo' packages.
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2023-06-16 |
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r-gensvm
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The GenSVM classifier is a generalized multiclass support vector machine (SVM). This classifier aims to find decision boundaries that separate the classes with as wide a margin as possible. In GenSVM, the loss function is very flexible in the way that misclassifications are penalized. This allows the user to tune the classifier to the dataset at hand and potentially obtain higher classification accuracy than alternative multiclass SVMs. Moreover, this flexibility means that GenSVM has a number of other multiclass SVMs as special cases. One of the other advantages of GenSVM is that it is trained in the primal space, allowing the use of warm starts during optimization. This means that for common tasks such as cross validation or repeated model fitting, GenSVM can be trained very quickly. Based on: G.J.J. van den Burg and P.J.F. Groenen (2018) <http://www.jmlr.org/papers/v17/14-526.html>.
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2023-06-16 |
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r-gensurv
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Generation of survival data with one (binary) time-dependent covariate. Generation of survival data arising from a progressive illness-death model.
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2023-06-16 |
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r-fts
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Fast operations for time series objects.
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2023-06-16 |
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r-flock
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Implements synchronization between R processes (spawned by using the "parallel" package for instance) using file locks. Supports both exclusive and shared locking.
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2023-06-16 |
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r-float
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R comes with a suite of utilities for linear algebra with "numeric" (double precision) vectors/matrices. However, sometimes single precision (or less!) is more than enough for a particular task. This package extends R's linear algebra facilities to include 32-bit float (single precision) data. Float vectors/matrices have half the precision of their "numeric"-type counterparts but are generally faster to numerically operate on, for a performance vs accuracy trade-off. The internal representation is an S4 class, which allows us to keep the syntax identical to that of base R's. Interaction between floats and base types for binary operators is generally possible; in these cases, type promotion always defaults to the higher precision. The package ships with copies of the single precision 'BLAS' and 'LAPACK', which are automatically built in the event they are not available on the system.
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2023-06-16 |
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r-shp2graph
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Functions for converting network data from a SpatialLinesDataFrame object to an 'igraph'-Class object.
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2023-06-16 |
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r-sdnet
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Fitting discrete Bayesian networks using soft-discretized data. Soft-discretization is based on mixture of normal distributions. Also implemented is a supervised Bayesian network learning employing Kullback-Leibler divergence. For more information see Balov (2013) <DOI:10.1186/1755-8794-6-S3-S1>.
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2023-06-16 |
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r-sdat
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Test the global null in linear models using marginal approach.
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2023-06-16 |
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r-rcppquantuccia
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'QuantLib' bindings are provided for R using 'Rcpp' and the header-only 'Quantuccia' variant (put together by Peter Caspers) offering an essential subset of 'QuantLib'. See the included file 'AUTHORS' for a full list of contributors to both 'QuantLib' and 'Quantuccia'.
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2023-06-16 |
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r-rcpphungarian
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Header library and R functions to solve minimum cost bipartite matching problem using Huhn-Munkres algorithm (Hungarian algorithm; <https://en.wikipedia.org/wiki/Hungarian_algorithm>; Kuhn (1955) doi:10.1002/nav.3800020109). This is a repackaging of code written by Cong Ma in the GitHub repo <https://github.com/mcximing/hungarian-algorithm-cpp>.
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2023-06-16 |
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r-randomfieldsutils
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Various utilities are provided that might be used in spatial statistics and elsewhere. It delivers a method for solving linear equations that checks the sparsity of the matrix before any algorithm is used. Furthermore, it includes the Struve functions.
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2023-06-16 |
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r-qrencoder
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Quick Response codes (QR codes) are a type of matrix bar code and can be used to authenticate transactions, provide access to multi-factor authentication services and enable general data transfer in an image. QR codes use four standardized encoding modes (numeric, alphanumeric, byte/binary, and kanji) to efficiently store data. Matrix barcode generation is performed efficiently in C via the included 'libqrencoder' library created by Kentaro Fukuchi.
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2023-06-16 |
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r-qgam
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Smooth additive quantile regression models, fitted using the methods of Fasiolo et al. (2017) <arXiv:1707.03307>. Differently from 'quantreg', the smoothing parameters are estimated automatically by marginal loss minimization, while the regression coefficients are estimated using either PIRLS or Newton algorithm. The learning rate is determined so that the Bayesian credible intervals of the estimated effects have approximately the correct coverage. The main function is qgam() which is similar to gam() in 'mgcv', but fits non-parametric quantile regression models.
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2023-06-16 |
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r-glcm
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Enables calculation of image textures (Haralick 1973) <doi:10.1109/TSMC.1973.4309314> from grey-level co-occurrence matrices (GLCMs). Supports processing images that cannot fit in memory.
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2023-06-16 |
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r-geostatsp
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Geostatistical modelling facilities using Raster and SpatialPoints objects are provided. Non-Gaussian models are fit using INLA, and Gaussian geostatistical models use Maximum Likelihood Estimation. For details see Brown (2015) <doi:10.18637/jss.v063.i12>.
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2023-06-16 |
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r-gconcord
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Estimates a sparse inverse covariance matrix from a convex pseudo-likelihood function with L1 penalty
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2023-06-16 |
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r-sparsio
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Fast 'SVMlight' reader and writer. 'SVMlight' is most commonly used format for storing sparse matrices (possibly with some target variable) on disk. For additional information about 'SVMlight' format see <http://svmlight.joachims.org/>.
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2023-06-16 |
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r-sme
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Fit smoothing-splines mixed-effects models to replicated functional data sets and visualise the results.
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2023-06-16 |
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r-slp
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Interface for creation of 'slp' class smoother objects for use in Generalized Additive Models (as implemented by packages 'gam' and 'mgcv').
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2023-06-16 |
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r-sfs
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An implementation of the Similarity-First Search algorithm (SFS), a combinatorial algorithm which can be used to solve the seriation problem and to recognize some structured weighted graphs. The SFS algorithm represents a generalization to weighted graphs of the graph search algorithm Lexicographic Breadth-First Search (Lex-BFS), a variant of Breadth-First Search. The SFS algorithm reduces to Lex-BFS when applied to binary matrices (or, equivalently, unweighted graphs). Hence this library can be also considered for Lex-BFS applications such as recognition of graph classes like chordal or unit interval graphs. In fact, the SFS seriation algorithm implemented in this package is a multisweep algorithm, which consists in repeating a finite number of SFS iterations (at most n sweeps for a matrix of size n). If the data matrix has a Robinsonian structure, then the ranking returned by the multistep SFS algorithm is a Robinson ordering of the input matrix. Otherwise the algorithm can be used as a heuristic to return a ranking partially satisfying the Robinson property.
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2023-06-16 |
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r-segmentr
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Given a likelihood provided by the user, this package applies it to a given matrix dataset in order to find change points in the data that maximize the sum of the likelihoods of all the segments. This package provides a handful of algorithms with different time complexities and assumption compromises so the user is able to choose the best one for the problem at hand. The implementation of the segmentation algorithms in this package are based on the paper by Bruno M. de Castro, Florencia Leonardi (2018) <arXiv:1501.01756>. The Berlin weather sample dataset was provided by Deutscher Wetterdienst <https://dwd.de/>. You can find all the references in the Acknowledgments section of this package's repository via the URL below.
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2023-06-16 |
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r-segmentor3isback
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Performs a fast exact segmentation on data and allows for use of various cost functions.
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2023-06-16 |
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r-rcpptoml
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The configuration format defined by 'TOML' (which expands to "Tom's Obvious Markup Language") specifies an excellent format (described at <https://github.com/toml-lang/toml>) suitable for both human editing as well as the common uses of a machine-readable format. This package uses 'Rcpp' to connect the 'cpptoml' parser written by Chase Geigle (in modern C++11) to R.
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2023-06-16 |
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r-rcpptn
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R-level and C++-level functionality to generate random deviates from and calculate moments of a Truncated Normal distribution using the algorithm of Robert (1995) <DOI:10.1007/BF00143942>. In addition to RNG, functions for calculating moments, densities, and entropies are provided at both levels.
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2023-06-16 |
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r-rbvs
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Implements the Ranking-Based Variable Selection algorithm for variable selection in high-dimensional data.
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2023-06-16 |
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r-rapror
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Calculate sketches of a data set reducing the number of observations using random projections. These can be used for Bayesian or frequentist linear regression on large data sets as described in Geppert et. al (2017) <doi:10.1007/s11222-015-9608-z>.
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2023-06-16 |
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r-randomizr
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Generates random assignments for common experimental designs and random samples for common sampling designs.
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2023-06-16 |
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r-quic
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Use Newton's method and coordinate descent to solve the regularized inverse covariance matrix estimation problem. Please refer to: Sparse Inverse Covariance Matrix Estimation Using Quadratic Approximation, Cho-Jui Hsieh, Matyas A. Sustik, Inderjit S. Dhillon, Pradeep Ravikumar, Advances in Neural Information Processing Systems 24, 2011, p. 2330--2338.
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2023-06-16 |
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r-qualv
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Qualitative methods for the validation of dynamic models. It contains (i) an orthogonal set of deviance measures for absolute, relative and ordinal scale and (ii) approaches accounting for time shifts. The first approach transforms time to take time delays and speed differences into account. The second divides the time series into interval units according to their main features and finds the longest common subsequence (LCS) using a dynamic programming algorithm.
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2023-06-16 |
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r-glmpath
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A path-following algorithm for L1 regularized generalized linear models and Cox proportional hazards model.
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2023-06-16 |
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r-geodist
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Dependency-free, ultra fast calculation of geodesic distances. Includes the reference nanometre-accuracy geodesic distances of Karney (2013) <doi:10.1007/s00190-012-0578-z>, as used by the 'sf' package, as well as Haversine and Vincenty distances. Default distance measure is the "Mapbox cheap ruler" which is generally more accurate than Haversine or Vincenty for distances out to a few hundred kilometres, and is considerably faster. The main function accepts one or two inputs in almost any generic rectangular form, and returns either matrices of pairwise distances, or vectors of sequential distances.
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2023-06-16 |
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r-gdmp
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Manage and analyze high-dimensional SNP data from chips with multiple densities.
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2023-06-16 |
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r-gcpm
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Analyze the default risk of credit portfolios. Commonly known models, like CreditRisk+ or the CreditMetrics model are implemented in their very basic settings. The portfolio loss distribution can be achieved either by simulation or analytically in case of the classic CreditRisk+ model. Models are only implemented to respect losses caused by defaults, i.e. migration risk is not included. The package structure is kept flexible especially with respect to distributional assumptions in order to quantify the sensitivity of risk figures with respect to several assumptions. Therefore the package can be used to determine the credit risk of a given portfolio as well as to quantify model sensitivities.
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2023-06-16 |
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r-gcat
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These are two-sample tests for categorical data utilizing similarity information among the categories. They are useful when there is underlying structure on the categories.
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2023-06-16 |
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r-gap
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It is designed as an integrated package for genetic data analysis of both population and family data. Currently, it contains functions for sample size calculations of both population-based and family-based designs, probability of familial disease aggregation, kinship calculation, statistics in linkage analysis, and association analysis involving genetic markers including haplotype analysis with or without environmental covariates.
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2023-06-16 |
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r-gamlss.dist
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A set of distributions which can be used for modelling the response variables in Generalized Additive Models for Location Scale and Shape, Rigby and Stasinopoulos (2005), <doi:10.1111/j.1467-9876.2005.00510.x>. The distributions can be continuous, discrete or mixed distributions. Extra distributions can be created, by transforming, any continuous distribution defined on the real line, to a distribution defined on ranges 0 to infinity or 0 to 1, by using a ''log'' or a ''logit' transformation respectively.
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2023-06-16 |
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r-gadag
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Sparse large Directed Acyclic Graphs learning with a combination of a convex program and a tailored genetic algorithm (see Champion et al. (2017) <https://hal.archives-ouvertes.fr/hal-01172745v2/document>).
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2023-06-16 |
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r-fst
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Multithreaded serialization of compressed data frames using the 'fst' format. The 'fst' format allows for random access of stored data and compression with the LZ4 and ZSTD compressors created by Yann Collet. The ZSTD compression library is owned by Facebook Inc.
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2023-06-16 |
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r-fsinteract
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Performs fast detection of interactions in large-scale data using the method of random intersection trees introduced in Shah, R. D. and Meinshausen, N. (2014) <http://www.jmlr.org/papers/v15/shah14a.html>. The algorithm finds potentially high-order interactions in high-dimensional binary two-class classification data, without requiring lower order interactions to be informative. The search is particularly fast when the matrices of predictors are sparse. It can also be used to perform market basket analysis when supplied with a single binary data matrix. Here it will find collections of columns which for many rows contain all 1's.
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2023-06-16 |
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r-frlr
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When fitting a set of linear regressions which have some same variables, we can separate the matrix and reduce the computation cost. This package aims to fit a set of repeated linear regressions faster. More details can be found in this blog Lijun Wang (2017) <https://stats.hohoweiya.xyz/2017/09/26/An-R-Package-Fit-Repeated-Linear-Regressions/>.
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2023-06-16 |
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r-freeknotsplines
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Algorithms for fitting free-knot splines for data with one independent variable and one dependent variable. Four free-knot spline algorithms are provided for the case where the number of knots is known in advance. A knot-search algorithm is provided for the case where the number of knots is not known in advance. In addition, methods are available to compute the fitted values, the residuals, and the coefficients of the splines, and to plot the results, along with a method to summarize the results.
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2023-06-16 |
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r-sparsehessianfd
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Estimates Hessian of a scalar-valued function, and returns it in a sparse Matrix format. The sparsity pattern must be known in advance. The algorithm is especially efficient for hierarchical models with a large number of heterogeneous units. See Braun, M. (2017) <doi:10.18637/jss.v082.i10>.
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2023-06-16 |
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r-soobench
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Collection of different single objective test functions useful for benchmarks and algorithm development.
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2023-06-16 |
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r-sobolsequence
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R implementation of S. Joe and F. Y. Kuo(2008) <DOI:10.1137/070709359>. The implementation is based on the data file new-joe-kuo-6.21201 <http://web.maths.unsw.edu.au/~fkuo/sobol/>.
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2023-06-16 |
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r-snappier
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Compression and decompression with 'Snappy'.
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2023-06-16 |
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r-simctest
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Algorithms for the implementation and evaluation of Monte Carlo tests, as well as for their use in multiple testing procedures.
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2023-06-16 |
