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r-adlift
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public |
Adaptive wavelet lifting transforms for signal denoising using optimal local neighbourhood regression, from Nunes et al. (2006) <doi:10.1007/s11222-006-6560-y>.
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2024-01-16 |
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r-anmc
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public |
Computationally efficient method to estimate orthant probabilities of high-dimensional Gaussian vectors. Further implements a function to compute conservative estimates of excursion sets under Gaussian random field priors.
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2024-01-16 |
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r-anacoda
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Is a collection of models to analyze genome scale codon data using a Bayesian framework. Provides visualization routines and checkpointing for model fittings. Currently published models to analyze gene data for selection on codon usage based on Ribosome Overhead Cost (ROC) are: ROC (Gilchrist et al. (2015) <doi:10.1093/gbe/evv087>), and ROC with phi (Wallace & Drummond (2013) <doi:10.1093/molbev/mst051>). In addition 'AnaCoDa' contains three currently unpublished models. The FONSE (First order approximation On NonSense Error) model analyzes gene data for selection on codon usage against of nonsense error rates. The PA (PAusing time) and PANSE (PAusing time + NonSense Error) models use ribosome footprinting data to analyze estimate ribosome pausing times with and without nonsense error rate from ribosome footprinting data.
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2024-01-16 |
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r-amelia
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public |
A tool that "multiply imputes" missing data in a single cross-section (such as a survey), from a time series (like variables collected for each year in a country), or from a time-series-cross-sectional data set (such as collected by years for each of several countries). Amelia II implements our bootstrapping-based algorithm that gives essentially the same answers as the standard IP or EMis approaches, is usually considerably faster than existing approaches and can handle many more variables. Unlike Amelia I and other statistically rigorous imputation software, it virtually never crashes (but please let us know if you find to the contrary!). The program also generalizes existing approaches by allowing for trends in time series across observations within a cross-sectional unit, as well as priors that allow experts to incorporate beliefs they have about the values of missing cells in their data. Amelia II also includes useful diagnostics of the fit of multiple imputation models. The program works from the R command line or via a graphical user interface that does not require users to know R.
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2024-01-16 |
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r-ambient
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Generation of natural looking noise has many application within simulation, procedural generation, and art, to name a few. The 'ambient' package provides an interface to the 'FastNoise' C++ library and allows for efficient generation of perlin, simplex, worley, cubic, value, and white noise with optional perturbation in either 2, 3, or 4 (in case of simplex and white noise) dimensions.
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2024-01-16 |
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r-alphasimr
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The successor to the 'AlphaSim' software for breeding program simulation [Faux et al. (2016) <doi:10.3835/plantgenome2016.02.0013>]. Used for stochastic simulations of breeding programs to the level of DNA sequence for every individual. Contained is a wide range of functions for modeling common tasks in a breeding program, such as selection and crossing. These functions allow for constructing simulations of highly complex plant and animal breeding programs via scripting in the R software environment. Such simulations can be used to evaluate overall breeding program performance and conduct research into breeding program design, such as implementation of genomic selection. Included is the 'Markovian Coalescent Simulator' ('MaCS') for fast simulation of biallelic sequences according to a population demographic history [Chen et al. (2009) <doi:10.1101/gr.083634.108>].
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2024-01-16 |
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r-amap
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public |
Tools for Clustering and Principal Component Analysis (With robust methods, and parallelized functions).
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2024-01-16 |
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r-alpaca
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Provides a routine to partial out factors with many levels during the optimization of the log-likelihood function of the corresponding generalized linear model (glm). The package is based on the algorithm described in Stammann (2018) <arXiv:1707.01815> and is restricted to glm's that are based on maximum likelihood estimation and nonlinear. It also offers an efficient algorithm to recover estimates of the fixed effects in a post-estimation routine and includes robust and multi-way clustered standard errors. Further the package provides analytical bias corrections for binary choice models derived by Fernandez-Val and Weidner (2016) <doi:10.1016/j.jeconom.2015.12.014> and Hinz, Stammann, and Wanner (2020) <arXiv:2004.12655>.
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2024-01-16 |
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r-airgr
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Hydrological modelling tools developed at INRAE-Antony (HYCAR Research Unit, France). The package includes several conceptual rainfall-runoff models (GR4H, GR5H, GR4J, GR5J, GR6J, GR2M, GR1A) that can be applied either on a lumped or semi-distributed way. A snow accumulation and melt model (CemaNeige) and the associated functions for the calibration and evaluation of models are also included. Use help(airGR) for package description and references.
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2024-01-16 |
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r-algdesign
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public |
Algorithmic experimental designs. Calculates exact and approximate theory experimental designs for D,A, and I criteria. Very large designs may be created. Experimental designs may be blocked or blocked designs created from a candidate list, using several criteria. The blocking can be done when whole and within plot factors interact.
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2024-01-16 |
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r-akima
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public |
Several cubic spline interpolation methods of H. Akima for irregular and regular gridded data are available through this package, both for the bivariate case (irregular data: ACM 761, regular data: ACM 760) and univariate case (ACM 433 and ACM 697). Linear interpolation of irregular gridded data is also covered by reusing D. J. Renkas triangulation code which is part of Akimas Fortran code. A bilinear interpolator for regular grids was also added for comparison with the bicubic interpolator on regular grids.
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2024-01-16 |
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r-airthermo
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public |
Deals with many computations related to the thermodynamics of atmospheric processes. It includes many functions designed to consider the density of air with varying degrees of water vapour in it, saturation pressures and mixing ratios, conversion of moisture indices, computation of atmospheric states of parcels subject to dry or pseudoadiabatic vertical evolutions and atmospheric instability indices that are routinely used for operational weather forecasts or meteorological diagnostics.
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2024-01-16 |
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r-ahocorasicktrie
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public |
Aho-Corasick is an optimal algorithm for finding many keywords in a text. It can locate all matches in a text in O(N+M) time; i.e., the time needed scales linearly with the number of keywords (N) and the size of the text (M). Compare this to the naive approach which takes O(N*M) time to loop through each pattern and scan for it in the text. This implementation builds the trie (the generic name of the data structure) and runs the search in a single function call. If you want to search multiple texts with the same trie, the function will take a list or vector of texts and return a list of matches to each text. By default, all 128 ASCII characters are allowed in both the keywords and the text. A more efficient trie is possible if the alphabet size can be reduced. For example, DNA sequences use at most 19 distinct characters and usually only 4; protein sequences use at most 26 distinct characters and usually only 20. UTF-8 (Unicode) matching is not currently supported.
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2024-01-16 |
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r-ahaz
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public |
Computationally efficient procedures for regularized estimation with the semiparametric additive hazards regression model.
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2024-01-16 |
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r-adespatial
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public |
Tools for the multiscale spatial analysis of multivariate data. Several methods are based on the use of a spatial weighting matrix and its eigenvector decomposition (Moran's Eigenvectors Maps, MEM). Several approaches are described in the review Dray et al (2012) <doi:10.1890/11-1183.1>.
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2024-01-16 |
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r-agop
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public |
Tools supporting multi-criteria and group decision making, including variable number of criteria, by means of aggregation operators, spread measures, fuzzy logic connectives, fusion functions, and preordered sets. Possible applications include, but are not limited to, quality management, scientometrics, software engineering, etc.
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2024-01-16 |
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r-adpss
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public |
Provides the functions for planning and conducting a clinical trial with adaptive sample size determination. Maximal statistical efficiency will be exploited even when dramatic or multiple adaptations are made. Such a trial consists of adaptive determination of sample size at an interim analysis and implementation of frequentist statistical test at the interim and final analysis with a prefixed significance level. The required assumptions for the stage-wise test statistics are independent and stationary increments and normality. Predetermination of adaptation rule is not required.
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2024-01-16 |
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r-adehabitatlt
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public |
A collection of tools for the analysis of animal movements.
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2024-01-16 |
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r-admit
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public |
Provides functions to perform the fitting of an adaptive mixture of Student-t distributions to a target density through its kernel function as described in Ardia et al. (2009) <doi:10.18637/jss.v029.i03>. The mixture approximation can then be used as the importance density in importance sampling or as the candidate density in the Metropolis-Hastings algorithm to obtain quantities of interest for the target density itself.
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2024-01-16 |
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r-admisc
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public |
Contains functions used across packages 'DDIwR', 'QCA' and 'venn'. Interprets and translates, factorizes and negates SOP - Sum of Products expressions, for both binary and multi-value crisp sets, and extracts information (set names, set values) from those expressions. Other functions perform various other checks if possibly numeric (even if all numbers reside in a character vector) and coerce to numeric, or check if the numbers are whole. It also offers, among many others, a highly versatile recoding routine and some more flexible alternatives to the base functions 'with()' and 'within()'. SOP simplification functions in this package use related minimization from package 'QCA', which is recommended to be installed despite not being listed in the Imports field, due to circular dependency issues.
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2024-01-16 |
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r-adjclust
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Implements a constrained version of hierarchical agglomerative clustering, in which each observation is associated to a position, and only adjacent clusters can be merged. Typical application fields in bioinformatics include Genome-Wide Association Studies or Hi-C data analysis, where the similarity between items is a decreasing function of their genomic distance. Taking advantage of this feature, the implemented algorithm is time and memory efficient. This algorithm is described in Chapter 4 of Alia Dehman (2015) <https://hal.archives-ouvertes.fr/tel-01288568v1>.
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2024-01-16 |
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r-adimpro
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public |
Implements tools for manipulation of digital images and the Propagation Separation approach by Polzehl and Spokoiny (2006) <DOI:10.1007/s00440-005-0464-1> for smoothing digital images, see Polzehl and Tabelow (2007) <DOI:10.18637/jss.v019.i01>.
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2024-01-16 |
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r-adehabitatma
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public |
A collection of tools to deal with raster maps.
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2024-01-16 |
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r-adephylo
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public |
Multivariate tools to analyze comparative data, i.e. a phylogeny and some traits measured for each taxa. The package contains functions to represent comparative data, compute phylogenetic proximities, perform multivariate analysis with phylogenetic constraints and test for the presence of phylogenetic autocorrelation. The package is described in Jombart et al (2010) <doi:10.1093/bioinformatics/btq292>.
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2024-01-16 |
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r-adehabitaths
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public |
A collection of tools for the analysis of habitat selection.
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2024-01-16 |
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r-adehabitathr
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A collection of tools for the estimation of animals home range.
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2024-01-16 |
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r-adegenet
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public |
Toolset for the exploration of genetic and genomic data. Adegenet provides formal (S4) classes for storing and handling various genetic data, including genetic markers with varying ploidy and hierarchical population structure ('genind' class), alleles counts by populations ('genpop'), and genome-wide SNP data ('genlight'). It also implements original multivariate methods (DAPC, sPCA), graphics, statistical tests, simulation tools, distance and similarity measures, and several spatial methods. A range of both empirical and simulated datasets is also provided to illustrate various methods.
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2024-01-16 |
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r-adbcdrivermanager
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public |
Provides a developer-facing interface to 'Arrow' Database Connectivity ('ADBC') for the purposes of driver development, driver testing, and building high-level database interfaces for users. 'ADBC' <https://arrow.apache.org/adbc/> is an API standard for database access libraries that uses 'Arrow' for result sets and query parameters.
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2024-01-16 |
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r-abess
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Extremely efficient toolkit for solving the best subset selection problem <https://www.jmlr.org/papers/v23/21-1060.html>. This package is its R interface. The package implements and generalizes algorithms designed in <doi:10.1073/pnas.2014241117> that exploits a novel sequencing-and-splicing technique to guarantee exact support recovery and globally optimal solution in polynomial times for linear model. It also supports best subset selection for logistic regression, Poisson regression, Cox proportional hazard model, Gamma regression, multiple-response regression, multinomial logistic regression, ordinal regression, (sequential) principal component analysis, and robust principal component analysis. The other valuable features such as the best subset of group selection <doi:10.1287/ijoc.2022.1241> and sure independence screening <doi:10.1111/j.1467-9868.2008.00674.x> are also provided.
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2024-01-16 |
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r-ade4
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public |
Tools for multivariate data analysis. Several methods are provided for the analysis (i.e., ordination) of one-table (e.g., principal component analysis, correspondence analysis), two-table (e.g., coinertia analysis, redundancy analysis), three-table (e.g., RLQ analysis) and K-table (e.g., STATIS, multiple coinertia analysis). The philosophy of the package is described in Dray and Dufour (2007) <doi:10.18637/jss.v022.i04>.
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2024-01-16 |
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r-adaptivetau
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Implements adaptive tau leaping to approximate the trajectory of a continuous-time stochastic process as described by Cao et al. (2007) The Journal of Chemical Physics <doi:10.1063/1.2745299> (aka. the Gillespie stochastic simulation algorithm). This package is based upon work supported by NSF DBI-0906041 and NIH K99-GM104158 to Philip Johnson and NIH R01-AI049334 to Rustom Antia.
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2024-01-16 |
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r-adaptivesparsity
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Implements Figueiredo EM algorithm for adaptive sparsity (Jeffreys prior) (see Figueiredo, M.A.T.; , "Adaptive sparseness for supervised learning," Pattern Analysis and Machine Intelligence, IEEE Transactions on , vol.25, no.9, pp. 1150- 1159, Sept. 2003) and Wong algorithm for adaptively sparse gaussian geometric models (see Wong, Eleanor, Suyash Awate, and P. Thomas Fletcher. "Adaptive Sparsity in Gaussian Graphical Models." In Proceedings of the 30th International Conference on Machine Learning (ICML-13), pp. 311-319. 2013.)
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2024-01-16 |
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r-actuar
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Functions and data sets for actuarial science: modeling of loss distributions; risk theory and ruin theory; simulation of compound models, discrete mixtures and compound hierarchical models; credibility theory. Support for many additional probability distributions to model insurance loss size and frequency: 23 continuous heavy tailed distributions; the Poisson-inverse Gaussian discrete distribution; zero-truncated and zero-modified extensions of the standard discrete distributions. Support for phase-type distributions commonly used to compute ruin probabilities. Main reference: <doi:10.18637/jss.v025.i07>. Implementation of the Feller-Pareto family of distributions: <doi:10.18637/jss.v103.i06>.
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2024-01-16 |
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r-acet
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Twin models that are able to estimate the dynamic behaviour of the variance components in the classical twin models with respect to age using B-splines and P-splines.
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2024-01-16 |
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r-acepack
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None |
Two nonparametric methods for multiple regression transform selection are provided. The first, Alternative Conditional Expectations (ACE), is an algorithm to find the fixed point of maximal correlation, i.e. it finds a set of transformed response variables that maximizes R^2 using smoothing functions [see Breiman, L., and J.H. Friedman. 1985. "Estimating Optimal Transformations for Multiple Regression and Correlation". Journal of the American Statistical Association. 80:580-598. <doi:10.1080/01621459.1985.10478157>]. Also included is the Additivity Variance Stabilization (AVAS) method which works better than ACE when correlation is low [see Tibshirani, R.. 1986. "Estimating Transformations for Regression via Additivity and Variance Stabilization". Journal of the American Statistical Association. 83:394-405. <doi:10.1080/01621459.1988.10478610>]. A good introduction to these two methods is in chapter 16 of Frank Harrel's "Regression Modeling Strategies" in the Springer Series in Statistics.
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2024-01-16 |
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r-abcoptim
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An implementation of Karaboga (2005) Artificial Bee Colony Optimization algorithm <http://mf.erciyes.edu.tr/abc/pub/tr06_2005.pdf>. This (working) version is a Work-in-progress, which is why it has been implemented using pure R code. This was developed upon the basic version programmed in C and distributed at the algorithm's official website.
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2024-01-16 |
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r-geojsonlint
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Tools for linting 'GeoJSON'. Includes tools for interacting with the online tool <http://geojsonlint.com>, the 'Javascript' library 'geojsonhint' (<https://www.npmjs.com/package/geojsonhint>), and validating against a 'GeoJSON' schema via the 'Javascript' library (<https://www.npmjs.com/package/is-my-json-valid>). Some tools work locally while others require an internet connection.
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2023-06-18 |
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r-tbart
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Solves Teitz and Bart's p-median problem - given a set of points attempts to find subset of size p such that summed distances of any point in the set to the nearest point in p is minimised. Although generally effective, this algorithm does not guarantee that a globally optimal subset is found.
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2023-06-18 |
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r-systemicrisk
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A toolbox for systemic risk based on liabilities matrices. Contains a Gibbs sampler for liabilities matrices where only row and column sums of the liabilities matrix as well as some other fixed entries are observed. Includes models for power law distribution on the degree distribution.
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2023-06-18 |
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r-svmmatch
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Causal effect estimation in observational data often requires identifying a set of untreated observations that are comparable to some treated group of interest. This package provides a suite of functions for identifying such a set of observations and for implementing standard and new diagnostics tools. The primary function, svmmatch(), uses support vector machines to identify a region of common support between treatment and control groups. A sensitivity analysis, balance checking, and assessment of the region of overlap between treated and control groups is included. The Bayesian implementation allows for recovery of uncertainty estimates for the treatment effect and all other parameters.
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2023-06-18 |
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r-sundialr
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Provides a way to call the functions in 'SUNDIALS' C ODE solving library (<https://computing.llnl.gov/projects/sundials>). Currently the serial version of ODE solver, 'CVODE', sensitivity calculator 'CVODES' and differential algebraic solver 'IDA' from the 'SUNDIALS' library are implemented. The package requires ODE to be written as an 'R' or 'Rcpp' function and does not require the 'SUNDIALS' library to be installed on the local machine.
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2023-06-18 |
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r-sunder
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public |
Quantification of the effect of geographic versus environmental isolation on genetic differentiation
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2023-06-18 |
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r-sspline
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R package for computing the spherical smoothing splines
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2023-06-18 |
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r-splustimedate
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A collection of classes and methods for working with times and dates. The code was originally available in S-PLUS.
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2023-06-18 |
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r-spatstat.core
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public |
Functionality for data analysis and modelling of spatial data, mainly spatial point patterns, in the 'spatstat' family of packages. (Excludes analysis of spatial data on a linear network, which is covered by the separate package 'spatstat.linnet'.) Exploratory methods include quadrat counts, K-functions and their simulation envelopes, nearest neighbour distance and empty space statistics, Fry plots, pair correlation function, kernel smoothed intensity, relative risk estimation with cross-validated bandwidth selection, mark correlation functions, segregation indices, mark dependence diagnostics, and kernel estimates of covariate effects. Formal hypothesis tests of random pattern (chi-squared, Kolmogorov-Smirnov, Monte Carlo, Diggle-Cressie-Loosmore-Ford, Dao-Genton, two-stage Monte Carlo) and tests for covariate effects (Cox-Berman-Waller-Lawson, Kolmogorov-Smirnov, ANOVA) are also supported. Parametric models can be fitted to point pattern data using the functions ppm(), kppm(), slrm(), dppm() similar to glm(). Types of models include Poisson, Gibbs and Cox point processes, Neyman-Scott cluster processes, and determinantal point processes. Models may involve dependence on covariates, inter-point interaction, cluster formation and dependence on marks. Models are fitted by maximum likelihood, logistic regression, minimum contrast, and composite likelihood methods. A model can be fitted to a list of point patterns (replicated point pattern data) using the function mppm(). The model can include random effects and fixed effects depending on the experimental design, in addition to all the features listed above. Fitted point process models can be simulated, automatically. Formal hypothesis tests of a fitted model are supported (likelihood ratio test, analysis of deviance, Monte Carlo tests) along with basic tools for model selection (stepwise(), AIC()) and variable selection (sdr). Tools for validating the fitted model include simulation envelopes, residuals, residual plots and Q-Q plots, leverage and influence diagnostics, partial residuals, and added variable plots.
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2023-06-18 |
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r-scat
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public |
Conditional association test based on summary data from genome-wide association study (GWAS). SCAT adjusts for heterogeneity in SNP coverage that exists in summary data if SNPs are not present in all of the participating studies of a GWAS meta-analysis. This commonly happens when different reference panels are used in participating studies for genotype imputation. This could happen when ones simply do not have data for some SNPs (e.g. different array, or imputated data is not available). Without properly adjusting for this kind of heterogeneity leads to inflated false positive rate. SCAT can also be used to conduct conventional conditional analysis when coverage heterogeneity is absent. For more details, refer to Zhang et al. (2018) Brief Bioinform. 19(6):1337-1343. <doi: 10.1093/bib/bbx072>.
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2023-06-18 |
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r-sbsa
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public |
Simplified Bayesian Sensitivity Analysis
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2023-06-18 |
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r-rscimark
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public |
The SciMark 2.0 benchmark was originally developed in Java as a benchmark for numerical and scientific computational performance. It measures the performance of several computational kernels which are frequently occurring in scientific applications. This package is a simple wrapper around the ANSI C implementation of the benchmark.
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2023-06-18 |
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r-rrdrand
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public |
Make use of the hardware random number accessed by the 'RdRand' instruction in recent Intel CPUs (Ivy Bridge and later). 'DRNG' is "Digital Random Number Generator".
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2023-06-18 |
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r-rpicosat
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public |
Bindings for the 'PicoSAT' solver to solve Boolean satisfiability problems (SAT). The boolean satisfiability problem asks the question if a given boolean formula can be TRUE; i.e. does there exist an assignment of TRUE/FALSE for each variable such that the whole formula is TRUE? The package bundles 'PicoSAT' solver release 965 <http://www.fmv.jku.at/picosat/>.
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2023-06-18 |
