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r-bayeslife
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Making probabilistic projections of life expectancy for all countries of the world, using a Bayesian hierarchical model <doi:10.1007/s13524-012-0193-x>. Subnational projections are also supported.
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2025-04-22 |
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r-bayeslogit
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Tools for sampling from the PolyaGamma distribution based on Polson, Scott, and Windle (2013) <doi:10.1080/01621459.2013.829001>. Useful for logistic regression.
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2025-04-22 |
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r-bayesiantools
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General-purpose MCMC and SMC samplers, as well as plot and diagnostic functions for Bayesian statistics, with a particular focus on calibrating complex system models. Implemented samplers include various Metropolis MCMC variants (including adaptive and/or delayed rejection MH), the T-walk, two differential evolution MCMCs, two DREAM MCMCs, and a sequential Monte Carlo (SMC) particle filter.
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2025-04-22 |
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r-bayesfm
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Collection of procedures to perform Bayesian analysis on a variety of factor models. Currently, it includes: "Bayesian Exploratory Factor Analysis" (befa) from G. Conti, S. Frühwirth-Schnatter, J.J. Heckman, R. Piatek (2014) <doi:10.1016/j.jeconom.2014.06.008>, an approach to dedicated factor analysis with stochastic search on the structure of the factor loading matrix. The number of latent factors, as well as the allocation of the manifest variables to the factors, are not fixed a priori but determined during MCMC sampling.
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2025-04-22 |
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r-bayesfactor
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A suite of functions for computing various Bayes factors for simple designs, including contingency tables, one- and two-sample designs, one-way designs, general ANOVA designs, and linear regression.
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2025-04-22 |
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r-bayesdfa
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Implements Bayesian dynamic factor analysis with 'Stan'. Dynamic factor analysis is a dimension reduction tool for multivariate time series. 'bayesdfa' extends conventional dynamic factor models in several ways. First, extreme events may be estimated in the latent trend by modeling process error with a student-t distribution. Second, alternative constraints (including proportions are allowed). Third, the estimated dynamic factors can be analyzed with hidden Markov models to evaluate support for latent regimes.
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2025-04-22 |
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r-batchtools
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As a successor of the packages 'BatchJobs' and 'BatchExperiments', this package provides a parallel implementation of the Map function for high performance computing systems managed by schedulers 'IBM Spectrum LSF' (<https://www.ibm.com/products/hpc-workload-management>), 'OpenLava' (<https://www.openlava.org/>), 'Univa Grid Engine'/'Oracle Grid Engine' (<https://www.univa.com/>), 'Slurm' (<https://slurm.schedmd.com/>), 'TORQUE/PBS' (<https://adaptivecomputing.com/cherry-services/torque-resource-manager/>), or 'Docker Swarm' (<https://docs.docker.com/engine/swarm/>). A multicore and socket mode allow the parallelization on a local machines, and multiple machines can be hooked up via SSH to create a makeshift cluster. Moreover, the package provides an abstraction mechanism to define large-scale computer experiments in a well-organized and reproducible way.
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2025-04-22 |
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r-basefun
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Some very simple infrastructure for basis functions.
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2025-04-22 |
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r-bamlss
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Infrastructure for estimating probabilistic distributional regression models in a Bayesian framework. The distribution parameters may capture location, scale, shape, etc. and every parameter may depend on complex additive terms (fixed, random, smooth, spatial, etc.) similar to a generalized additive model. The conceptual and computational framework is introduced in Umlauf, Klein, Zeileis (2019) <doi:10.1080/10618600.2017.1407325> and the R package in Umlauf, Klein, Simon, Zeileis (2021) <doi:10.18637/jss.v100.i04>.
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2025-04-22 |
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r-autothresholdr
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Algorithms for automatically finding appropriate thresholds for numerical data, with special functions for thresholding images. Provides the 'ImageJ' 'Auto Threshold' plugin functionality to R users. See <https://imagej.net/plugins/auto-threshold> and Landini et al. (2017) <DOI:10.1111/jmi.12474>.
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2025-04-22 |
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r-ashr
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The R package 'ashr' implements an Empirical Bayes approach for large-scale hypothesis testing and false discovery rate (FDR) estimation based on the methods proposed in M. Stephens, 2016, "False discovery rates: a new deal", <DOI:10.1093/biostatistics/kxw041>. These methods can be applied whenever two sets of summary statistics---estimated effects and standard errors---are available, just as 'qvalue' can be applied to previously computed p-values. Two main interfaces are provided: ash(), which is more user-friendly; and ash.workhorse(), which has more options and is geared toward advanced users. The ash() and ash.workhorse() also provides a flexible modeling interface that can accommodate a variety of likelihoods (e.g., normal, Poisson) and mixture priors (e.g., uniform, normal).
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2025-04-22 |
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r-arfima
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Simulates, fits, and predicts long-memory and anti-persistent time series, possibly mixed with ARMA, regression, transfer-function components. Exact methods (MLE, forecasting, simulation) are used. Bug reports should be done via GitHub (at <https://github.com/JQVeenstra/arfima>), where the development version of this package lives; it can be installed using devtools.
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2025-04-22 |
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r-apollo
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Choice models are a widely used technique across numerous scientific disciplines. The Apollo package is a very flexible tool for the estimation and application of choice models in R. Users are able to write their own model functions or use a mix of already available ones. Random heterogeneity, both continuous and discrete and at the level of individuals and choices, can be incorporated for all models. There is support for both standalone models and hybrid model structures. Both classical and Bayesian estimation is available, and multiple discrete continuous models are covered in addition to discrete choice. Multi-threading processing is supported for estimation and a large number of pre and post-estimation routines, including for computing posterior (individual-level) distributions are available. For examples, a manual, and a support forum, visit <http://www.ApolloChoiceModelling.com>. For more information on choice models see Train, K. (2009) <isbn:978-0-521-74738-7> and Hess, S. & Daly, A.J. (2014) <isbn:978-1-781-00314-5> for an overview of the field.
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2025-04-22 |
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r-archive
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Bindings to 'libarchive' <http://www.libarchive.org> the Multi-format archive and compression library. Offers R connections and direct extraction for many archive formats including 'tar', 'ZIP', '7-zip', 'RAR', 'CAB' and compression formats including 'gzip', 'bzip2', 'compress', 'lzma' and 'xz'.
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2025-04-22 |
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r-anticlust
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The method of anticlustering partitions a pool of elements into groups (i.e., anticlusters) with the goal of maximizing between-group similarity or within-group heterogeneity. The anticlustering approach thereby reverses the logic of cluster analysis that strives for high within-group homogeneity and clear separation between groups. Computationally, anticlustering is accomplished by maximizing instead of minimizing a clustering objective function, such as the intra-cluster variance (used in k-means clustering) or the sum of pairwise distances within clusters. The main function anticlustering() gives access to exact and heuristic anticlustering methods described in Papenberg and Klau (2021; <doi:10.1037/met0000301>), Brusco et al. (2020; <doi:10.1111/bmsp.12186>), and Papenberg (2023; <doi:10.1111/bmsp.12315>). The exact algorithms require that an integer linear programming solver is installed, either the GNU linear programming kit (<https://www.gnu.org/software/glpk/glpk.html>) together with the interface package 'Rglpk' (<https://cran.R-project.org/package=Rglpk>), or the SYMPHONY ILP solver (<https://github.com/coin-or/SYMPHONY>) together with the interface package 'Rsymphony' (<https://cran.r-project.org/package=Rsymphony>). Full access to the bicriterion anticlustering method proposed by Brusco et al. (2020) is given via the function bicriterion_anticlustering(), while kplus_anticlustering() implements the full functionality of the k-plus anticlustering approach proposed by Papenberg (2023). Some other functions are available to solve classical clustering problems. The function balanced_clustering() applies a cluster analysis under size constraints, i.e., creates equal-sized clusters. The function matching() can be used for (unrestricted, bipartite, or K-partite) matching. The function wce() can be used optimally solve the (weighted) cluster editing problem, also known as correlation clustering, clique partitioning problem or transitivity clustering.
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2025-04-22 |
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r-aorsf
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Fit, interpret, and make predictions with oblique random survival forests. Oblique decision trees are notoriously slow compared to their axis based counterparts, but 'aorsf' runs as fast or faster than axis-based decision tree algorithms for right-censored time-to-event outcomes. Methods to accelerate and interpret the oblique random survival forest are described in Jaeger et al., (2023) <DOI:10.1080/10618600.2023.2231048>.
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2025-04-22 |
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r-analogue
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Fits Modern Analogue Technique and Weighted Averaging transfer function models for prediction of environmental data from species data, and related methods used in palaeoecology.
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2025-04-22 |
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r-alphashape3d
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Implementation in R of the alpha-shape of a finite set of points in the three-dimensional space. The alpha-shape generalizes the convex hull and allows to recover the shape of non-convex and even non-connected sets in 3D, given a random sample of points taken into it. Besides the computation of the alpha-shape, this package provides users with functions to compute the volume of the alpha-shape, identify the connected components and facilitate the three-dimensional graphical visualization of the estimated set.
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2025-04-22 |
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r-adlift
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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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2025-04-22 |
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r-adespatial
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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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2025-04-22 |
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r-adehabitatlt
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A collection of tools for the analysis of animal movements.
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2025-04-22 |
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r-adephylo
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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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2025-04-22 |
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r-adehabitatma
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A collection of tools to deal with raster maps.
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2025-04-22 |
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r-adehabitaths
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A collection of tools for the analysis of habitat selection.
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2025-04-22 |
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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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2025-04-22 |
