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r-rcppgreedysetcover
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public |
A fast implementation of the greedy algorithm for the set cover problem using 'Rcpp'.
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2023-06-16 |
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r-rcppannoy
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'Annoy' is a small C++ library for Approximate Nearest Neighbors written for efficient memory usage as well an ability to load from / save to disk. This package provides an R interface by relying on the 'Rcpp' package, exposing the same interface as the original Python wrapper to 'Annoy'. See <https://github.com/spotify/annoy> for more on 'Annoy'. 'Annoy' is released under Version 2.0 of the Apache License. Also included is a small Windows port of 'mmap' which is released under the MIT license.
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2023-06-16 |
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r-rcdd
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R interface to (some of) cddlib (<http://www.ifor.math.ethz.ch/~fukuda/cdd_home/cdd.html>). Converts back and forth between two representations of a convex polytope: as solution of a set of linear equalities and inequalities and as convex hull of set of points and rays. Also does linear programming and redundant generator elimination (for example, convex hull in n dimensions). All functions can use exact infinite-precision rational arithmetic.
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2023-06-16 |
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r-rbeta2009
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The package contains functions to generate random numbers from the beta distribution and random vectors from the Dirichlet distribution.
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2023-06-16 |
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r-quantregranger
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This is the implementation of quantile regression forests for the fast random forest package 'ranger'.
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2023-06-16 |
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r-pwrfdr
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This is a package for calculating power and sample size in multiple testing situations using the Benjamini-Hochberg (BH) false discovery rate (FDR) procedure. The package computes power and sample size in one of either two ways, using the average power or the lambda-power. See Izmirlian, G. (2018) <arXiv:1801.03989>.
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2023-06-16 |
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r-gllm
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Routines for log-linear models of incomplete contingency tables, including some latent class models, via EM and Fisher scoring approaches. Allows bootstrapping. See Espeland and Hui (1987) <doi:10.2307/2531553> for general approach.
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2023-06-16 |
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r-genepop
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Makes the Genepop software available in R. This software implements a mixture of traditional population genetic methods and some more focused developments: it computes exact tests for Hardy-Weinberg equilibrium, for population differentiation and for genotypic disequilibrium among pairs of loci; it computes estimates of F-statistics, null allele frequencies, allele size-based statistics for microsatellites, etc.; and it performs analyses of isolation by distance from pairwise comparisons of individuals or population samples.
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2023-06-16 |
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r-gckrig
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Provides a variety of functions to analyze and model geostatistical count data with Gaussian copulas, including 1) data simulation and visualization; 2) correlation structure assessment (here also known as the Normal To Anything); 3) calculate multivariate normal rectangle probabilities; 4) likelihood inference and parallel prediction at predictive locations.
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2023-06-16 |
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r-gbm
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An implementation of extensions to Freund and Schapire's AdaBoost algorithm and Friedman's gradient boosting machine. Includes regression methods for least squares, absolute loss, t-distribution loss, quantile regression, logistic, multinomial logistic, Poisson, Cox proportional hazards partial likelihood, AdaBoost exponential loss, Huberized hinge loss, and Learning to Rank measures (LambdaMart). Originally developed by Greg Ridgeway.
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2023-06-16 |
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r-gausscov
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Given the standard linear model the traditional way of deciding whether to include the jth covariate is to apply the F-test to decide whether the corresponding beta coefficient is zero. The Gaussian covariate method is completely different. The question as to whether the beta coefficient is or is not zero is replaced by the question as to whether the covariate is better or worse than i.i.d. Gaussian noise. The P-value for the covariate is the probability that Gaussian noise is better. Surprisingly this can be given exactly and it is the same a the P-value for the classical model based on the F-distribution. The Gaussian covariate P-value is model free, it is the same for any data set. Using the idea it is possible to do covariate selection for a small number of covariates 25 by considering all subsets. Post selection inference causes no problems as the P-values hold whatever the data. The idea extends to stepwise regression again with exact probabilities. In the simplest version the only parameter is a specified cut-off P-value which can be interpreted as the probability of a false positive being included in the final selection. For more information see the website below and the accompanying papers: L. Davies and L. Duembgen, "A Model-free Approach to Linear Least Squares Regression with Exact Probabilities and Applications to Covariate Selection", 2019, <arXiv:1906.01990>. L. Davies, "Lasso, Knockoff and Gaussian covariates: A comparison", 2018, <arXiv:1807.09633v4>.
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2023-06-16 |
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r-fmstable
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This package implements some basic procedures for dealing with log maximally skew stable distributions, which are also called finite moment log stable distributions.
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2023-06-16 |
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r-flsa
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Implements a path algorithm for the Fused Lasso Signal Approximator. For more details see the help files or the article by Hoefling (2009) <arXiv:0910.0526>.
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2023-06-16 |
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r-fllat
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Fits the Fused Lasso Latent Feature model, which is used for modeling multi-sample aCGH data to identify regions of copy number variation (CNV). Produces a set of features that describe the patterns of CNV and a set of weights that describe the composition of each sample. Also provides functions for choosing the optimal tuning parameters and the appropriate number of features, and for estimating the false discovery rate.
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2023-06-16 |
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r-sparsesvm
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Fast algorithm for fitting solution paths of sparse SVM models with lasso or elastic-net regularization.
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2023-06-16 |
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r-spacejam
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This package provides an extension of conditional independence (CIG) and directed acyclic graph (DAG) estimation to the case where conditional relationships are (non-linear) additive models.
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2023-06-16 |
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r-socialnetworks
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Generates social networks using either of two approaches: using either pairwise distances or territorial area intersections.
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2023-06-16 |
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r-smc
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particle filtering, auxiliary particle filtering and sequential Monte Carlo algorithms
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2023-06-16 |
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r-seqminer
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public |
Integrate sequencing data (Variant call format, e.g. VCF or BCF) or meta-analysis results in R. This package can help you (1) read VCF/BCF/BGEN files by chromosomal ranges (e.g. 1:100-200); (2) read RareMETAL summary statistics files; (3) read tables from a tabix-indexed files; (4) annotate VCF/BCF files; (5) create customized workflow based on Makefile.
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2023-06-16 |
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r-rcppxsimd
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This header-only library provides modern, portable C++ wrappers for SIMD intrinsics and parallelized, optimized math implementations (SSE, AVX, NEON, AVX512). By placing this library in this package, we offer an efficient distribution system for Xsimd <https://github.com/QuantStack/xsimd> for R packages using CRAN.
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2023-06-16 |
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r-rborist
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Scalable implementation of classification and regression forests, as described by Breiman (2001), <DOI:10.1023/A:1010933404324>.
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2023-06-16 |
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r-rcppcwb
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'Rcpp' Bindings for the C code of the 'Corpus Workbench' ('CWB'), an indexing and query engine to efficiently analyze large corpora (<http://cwb.sourceforge.net>). 'RcppCWB' is licensed under the GNU GPL-3, in line with the GPL-3 license of the 'CWB' (<https://www.r-project.org/ Licenses/GPL-3>). The 'CWB' relies on 'pcre' (BSD license, see <https://www.pcre.org/ licence.txt>) and 'GLib' (LGPL license, see <https://www.gnu.org/licenses/lgpl-3.0.en. html>). See the file LICENSE.note for further information. The package includes modified code of the 'rcqp' package (GPL-2, see <https://cran.r-project.org/package=rcqp>). The original work of the authors of the 'rcqp' package is acknowledged with great respect, and they are listed as authors of this package. To achieve cross-platform portability (including Windows), using 'Rcpp' for wrapper code is the approach used by 'RcppCWB'.
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2023-06-16 |
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r-rcppbdt
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This package provides R with access to Boost Date_Time functionality by using Rcpp modules. . Functionality from Boost Date_Time for dates, durations (both for days and datetimes), timezones, and posix time ("ptime") is provided. The posix time implementation can support high-resolution of up to nano-second precision by using 96 bits (instead of R's 64) to present a ptime object.
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2023-06-16 |
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r-rcassandra
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This packages provides a direct interface (without the use of Java) to the most basic functionality of Apache Cassanda such as login, updates and queries.
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2023-06-16 |
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r-rann.l1
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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 L1 (Manhattan, taxicab) metric. Please see package 'RANN' for the same functionality using the L2 (Euclidean) metric.
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2023-06-16 |
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r-pvar
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The calculation of p-variation of the finite sample data. This package is a realisation of the procedure described in Butkus, V. & Norvaisa, R. Lith Math J (2018). <doi: 10.1007/s10986-018-9414-3> The formal definitions and reference into literature are given in vignette.
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2023-06-16 |
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r-glmmml
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Binomial and Poisson regression for clustered data, fixed and random effects with bootstrapping.
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2023-06-16 |
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r-gelnet
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Implements several extensions of the elastic net regularization scheme. These extensions include individual feature penalties for the L1 term, feature-feature penalties for the L2 term, as well as translation coefficients for the latter.
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2023-06-16 |
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r-fmrs
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Provides parameter estimation as well as variable selection in Finite Mixture of Accelerated Failure Time Regression and Finite Mixture of Regression Models. Furthermore, this package provides Ridge Regression and Elastic Net.
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2023-06-16 |
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r-spbayes
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Fits univariate and multivariate spatio-temporal random effects models for point-referenced data using Markov chain Monte Carlo (MCMC). Details are given in Finley, Banerjee, and Gelfand (2015) <doi:10.18637/jss.v063.i13> and Finley, Banerjee, and Cook (2014) <doi:10.1111/2041-210X.12189>.
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2023-06-16 |
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r-spatialpack
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Tools to assess the association between two spatial processes. Currently, several methodologies are implemented: A modified t-test to perform hypothesis testing about the independence between the processes, a suitable nonparametric correlation coefficient, the codispersion coefficient, and an F test for assessing the multiple correlation between one spatial process and several others. Functions for image processing and computing the spatial association between images are also provided. SpatialPack gives methods to complement methodologies that are available in geoR for one spatial process.
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2023-06-16 |
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r-sparsesem
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Sparse-aware maximum likelihood for structural equation models in inferring gene regulatory networks
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2023-06-16 |
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r-softrandomforest
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Performs random forests for soft decision trees for a classification problem. Current limitations are for a maximum depth of 5 resulting in 16 terminal nodes. Some data cleaning is required before input. Final graphic output requires currently requires exporting to 'Microsoft Excel' for visualization. Method based on Irsoy, Yildiz and Alpaydin (2012, ISBN: 978-4-9906441-1-6).
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2023-06-16 |
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r-snnr
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Solve the problem of over-parameterization in neural networks for genomic selection. Daniel Gianola, Hayrettin OkutEmail, Kent A Weigel and Guilherme JM Rosa (2011) <doi:10.1186/1471-2156-12-87>.
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2023-06-16 |
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r-smitidvisu
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Visualisation tools for 'SMITIDstruct' package. Allow to visualize host timeline, transmission tree, index diversities and variant graph using HTMLwidgets. It mainly using D3JS javascript framework.
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2023-06-16 |
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r-similar
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An implementation of a novel method to quantify the similarity the code-base of R functions by means of program dependence graphs. Possible use cases include detection of code clones for improving software quality and of plagiarism among students' homework assignments.
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2023-06-16 |
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r-sglasso
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RCON(V, E) models are a kind of restriction of the Gaussian Graphical Models defined by a set of equality constraints on the entries of the concentration matrix. 'sglasso' package implements the structured graphical lasso (sglasso) estimator proposed in Abbruzzo et al. (2014) for the weighted l1-penalized RCON(V, E) model. Two cyclic coordinate algorithms are implemented to compute the sglasso estimator, i.e. a cyclic coordinate minimization (CCM) and a cyclic coordinate descent (CCD) algorithm.
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2023-06-16 |
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r-sets
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Data structures and basic operations for ordinary sets, generalizations such as fuzzy sets, multisets, and fuzzy multisets, customizable sets, and intervals.
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2023-06-16 |
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r-semver
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Tools and functions for parsing, rendering and operating on semantic version strings. Semantic versioning is a simple set of rules and requirements that dictate how version numbers are assigned and incremented as outlined at <http://semver.org>.
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2023-06-16 |
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r-secure
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Sequential factor extraction via co-sparse unit-rank estimation (SeCURE).
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2023-06-16 |
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r-rdist
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A common framework for calculating distance matrices.
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2023-06-16 |
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r-rcppnumerical
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A collection of open source libraries for numerical computing (numerical integration, optimization, etc.) and their integration with 'Rcpp'.
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2023-06-16 |
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r-rcpphnsw
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'Hnswlib' is a C++ library for Approximate Nearest Neighbors. This package provides a minimal R interface by relying on the 'Rcpp' package. See <https://github.com/nmslib/hnswlib> for more on 'hnswlib'. 'hnswlib' is released under Version 2.0 of the Apache License.
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2023-06-16 |
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r-pwrgsd
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Tools for the evaluation of interim analysis plans for sequentially monitored trials on a survival endpoint; tools to construct efficacy and futility boundaries, for deriving power of a sequential design at a specified alternative, template for evaluating the performance of candidate plans at a set of time varying alternatives. See Izmirlian, G. (2014) <doi:10.4310/SII.2014.v7.n1.a4>.
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2023-06-16 |
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r-gamlr
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The gamma lasso algorithm provides regularization paths corresponding to a range of non-convex cost functions between L0 and L1 norms. As much as possible, usage for this package is analogous to that for the glmnet package (which does the same thing for penalization between L1 and L2 norms). For details see: Taddy (2017 JCGS), One-Step Estimator Paths for Concave Regularization, <arXiv:1308.5623>.
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2023-06-16 |
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r-funchisq
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Statistical hypothesis testing methods for inferring model-free functional dependency using asymptotic chi-squared or exact distributions. Functional test statistics are asymmetric and functionally optimal, unique from other related statistics. Tests in this package reveal evidence for causality based on the causality-by-functionality principle. They include asymptotic functional chi-squared tests ('Zhang & Song' 2013) <arXiv:1311.2707> and an exact functional test ('Zhong & Song' 2019) <doi:10.1109/TCBB.2018.2809743>. The normalized functional chi-squared test was used by Best Performer 'NMSUSongLab' in HPN-DREAM (DREAM8) Breast Cancer Network Inference Challenges ('Hill et al' 2016) <doi:10.1038/nmeth.3773>. A function index ('Zhong & Song' in press) ('Kumar et al' 2018) <doi:10.1109/BIBM.2018.8621502> derived from the functional test statistic offers a new effect size measure for the strength of functional dependency, a better alternative to conditional entropy in many aspects. For continuous data, these tests offer an advantage over regression analysis when a parametric functional form cannot be assumed; for categorical data, they provide a novel means to assess directional dependency not possible with symmetrical Pearson's chi-squared or Fisher's exact tests.
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2023-06-16 |
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r-sdmtools
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This packages provides a set of tools for post processing the outcomes of species distribution modeling exercises. It includes novel methods for comparing models and tracking changes in distributions through time. It further includes methods for visualizing outcomes, selecting thresholds, calculating measures of accuracy and landscape fragmentation statistics, etc.. This package was made possible in part by financial support from the Australian Research Council & ARC Research Network for Earth System Science.
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2023-06-16 |
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r-spatstat.utils
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Contains utility functions for the 'spatstat' package which may also be useful for other purposes.
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2023-06-16 |
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r-spam
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Set of functions for sparse matrix algebra. Differences with other sparse matrix packages are: (1) we only support (essentially) one sparse matrix format, (2) based on transparent and simple structure(s), (3) tailored for MCMC calculations within G(M)RF. (4) and it is fast and scalable (with the extension package spam64).
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2023-06-16 |
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r-softimpute
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Iterative methods for matrix completion that use nuclear-norm regularization. There are two main approaches.The one approach uses iterative soft-thresholded svds to impute the missing values. The second approach uses alternating least squares. Both have an "EM" flavor, in that at each iteration the matrix is completed with the current estimate. For large matrices there is a special sparse-matrix class named "Incomplete" that efficiently handles all computations. The package includes procedures for centering and scaling rows, columns or both, and for computing low-rank SVDs on large sparse centered matrices (i.e. principal components)
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2023-06-16 |
