Identifies constant, additive, multiplicative, and user-defined simplivariate components in numeric data matrices using a genetic algorithm. Supports flexible pattern definitions and provides visualization for general biclustering applications across diverse domains. The method builds on simplivariate models as introduced in Hageman et al. (2008) <doi:10.1371/journal.pone.0003259> and is related to biclustering frameworks as reviewed by Madeira and Oliveira (2004) <doi:10.1109/TCBB.2004.2>.

This package provides functions for spatial methods based on generalized estimating equations (GEE) and wavelet-revised methods (WRM), functions for scaling by wavelet multiresolution regression (WMRR), conducting multi-model inference, and stepwise model selection. Further, contains functions for spatially corrected model accuracy measures.

This package provides functions for conducting jackknife Euclidean / empirical likelihood inference for Spearman's rho (de Carvalho and Marques (2012) <doi:10.1080/10920277.2012.10597644>).

sqliter helps users, mainly data munging practioneers, to organize their sql calls in a clean structure. It simplifies the process of extracting and transforming data into useful formats.

This package provides functions that automate accessing, downloading and exploring Soil Moisture and Ocean Salinity (SMOS) Level 4 (L4) data developed by Barcelona Expert Center (BEC). Particularly, it includes functions to search for, acquire, extract, and plot BEC-SMOS L4 soil moisture data downscaled to ~1 km spatial resolution. Note that SMOS is one of Earth Explorer Opportunity missions by the European Space Agency (ESA). More information about SMOS products can be found at <https://earth.esa.int/eogateway/missions/smos/data>.

The computer program is an efficient igneous norm algorithm and rock classification system written in R but run as shiny app.

This package provides tools developed to facilitate the establishment of the rank and social hierarchy for gregarious animals by the Si method developed by Kondo & Hurnik (1990)<doi:10.1016/0168-1591(90)90125-W>. It is also possible to determine the number of agonistic interactions between two individuals, sociometric and dyadics matrix from dataset obtained through electronic bins. In addition, it is possible plotting the results using a bar plot, box plot, and sociogram.

This package contains methods to generate and evaluate semi-artificial data sets. Based on a given data set different methods learn data properties using machine learning algorithms and generate new data with the same properties. The package currently includes the following data generators: i) a RBF network based generator using rbfDDA() from package RSNNS', ii) a Random Forest based generator for both classification and regression problems iii) a density forest based generator for unsupervised data Data evaluation support tools include: a) single attribute based statistical evaluation: mean, median, standard deviation, skewness, kurtosis, medcouple, L/RMC, KS test, Hellinger distance b) evaluation based on clustering using Adjusted Rand Index (ARI) and FM c) evaluation based on classification performance with various learning models, e.g., random forests.

Quasi-Monte-Carlo algorithm for systematic generation of shock scenarios from an arbitrary multivariate elliptical distribution. The algorithm selects a systematic mesh of arbitrary fineness that approximately evenly covers an isoprobability ellipsoid in d dimensions (Flood, Mark D. & Korenko, George G. (2013) <doi:10.1080/14697688.2014.926018>). This package is the R analogy to the Matlab code published by Flood & Korenko in above-mentioned paper.

This package contains functions for estimating the STARTS model of Kenny and Zautra (1995, 2001) <DOI:10.1037/0022-006X.63.1.52>, <DOI:10.1037/10409-008>. Penalized maximum likelihood estimation and Markov Chain Monte Carlo estimation are also provided, see Luedtke, Robitzsch and Wagner (2018) <DOI:10.1037/met0000155>.

Provision of the S4 SpatialGraph class built on top of objects provided by igraph and sp packages, and associated utilities. See the documentation of the SpatialGraph-class within this package for further description. An example of how from a few points one can arrive to a SpatialGraph is provided in the function sl2sg().

Loads and manages statistical genetics data objects including reference panels, genotypes, LD matrices, annotations, and summary statistics. Follows the statgen specification for use across Python', R', and MATLAB'/'Octave runtimes.

Sejong(http://www.sejong.or.kr/) corpus and Hannanum(http://semanticweb.kaist.ac.kr/home/index.php/HanNanum) dictionaries for KoNLP.

This is a compendium of C++ routines useful for Bayesian statistics. We steal other people's C++ code, repurpose it, and export it so developers of R packages can use it in their C++ code. We actually don't steal anything, or claim that Thomas Bayes did, but copy code that is compatible with our GPL 3 licence, fully acknowledging the authorship of the original code.

This package implements a parametric decision-theoretic framework for optimal diagnostic cutoff selection under the family of scale mixtures of skew-normal (SMSN) distributions, including the skew-normal (SN) and skew-t (ST) models as special cases. The optimal cutoff is defined by minimising a weighted misclassification risk that incorporates disease prevalence and asymmetric costs, leading to a likelihood-ratio equation that generalises the Youden criterion. Under a monotone likelihood ratio condition, existence, uniqueness, and global optimality of the cutoff are established. Asymptotic normality and a closed-form plug-in variance estimator are provided via the implicit function theorem and the multivariate delta method. Tools for model fitting, cutoff estimation, confidence intervals, the local identifiability diagnostic, and Monte Carlo simulation are included. The methodology is described in de Paula, Mouriño, and Dias Domingues (2026) <doi:10.48550/arXiv.2605.07829>.

This package provides a Bayesian framework for inferring influenza infection status from serial antibody measurements. Jointly estimates season-specific infection probabilities, antibody boosting and waning after infection, and baseline hemagglutination inhibition (HAI) titer distributions via Markov chain Monte Carlo (MCMC). Supports multi-season analysis and subgroup comparisons via a group_by interface. See Tsang et al. (2022) <doi:10.1038/s41467-022-29310-8> for methodological details.

This package provides functions for calculating species richness for rarefaction and extrapolation, primarily non-parametric species richness such as jackknife, Chao1, and ACE. Also available are functions for plotting species richness and extrapolation curves, and computing standard diversity and entropy indices.

Exporting shiny applications with shinylive allows you to run them entirely in a web browser, without the need for a separate R server. The traditional way of deploying shiny applications involves in a separate server and client: the server runs R and shiny', and clients connect via the web browser. When an application is deployed with shinylive', R and shiny run in the web browser (via webR'): the browser is effectively both the client and server for the application. This allows for your shiny application exported by shinylive to be hosted by a static web server.

Extends the functionality of the package Synth as detailed in Abadie, Diamond, and Hainmueller (2011) <doi:10.18637/jss.v042.i13>. Includes generating and plotting placebos, post/pre-MSPE (Mean Squared Prediction Error) significance tests and plots, and calculating average treatment effects for multiple treated units.

The Statistical Learning Theory (SLT) provides the theoretical background to ensure that a supervised algorithm generalizes the mapping f:X -> Y given f is selected from its search space bias F. This formal result depends on the Shattering coefficient function N(F,2n) to upper bound the empirical risk minimization principle, from which one can estimate the necessary training sample size to ensure the probabilistic learning convergence and, most importantly, the characterization of the capacity of F, including its under and overfitting abilities while addressing specific target problems. In this context, we propose a new approach to estimate the maximal number of hyperplanes required to shatter a given sample, i.e., to separate every pair of points from one another, based on the recent contributions by Har-Peled and Jones in the dataset partitioning scenario, and use such foundation to analytically compute the Shattering coefficient function for both binary and multi-class problems. As main contributions, one can use our approach to study the complexity of the search space bias F, estimate training sample sizes, and parametrize the number of hyperplanes a learning algorithm needs to address some supervised task, what is specially appealing to deep neural networks. Reference: de Mello, R.F. (2019) "On the Shattering Coefficient of Supervised Learning Algorithms" <arXiv:1911.05461>; de Mello, R.F., Ponti, M.A. (2018, ISBN: 978-3319949888) "Machine Learning: A Practical Approach on the Statistical Learning Theory".

This package provides tools for accessing and processing datasets prepared by the Foundation SmarterPoland.pl. Among all: access to API of Google Maps, Central Statistical Office of Poland, MojePanstwo, Eurostat, WHO and other sources.

Fits single-species (univariate) and multi-species (multivariate) non-spatial and spatial abundance models in a Bayesian framework using Markov Chain Monte Carlo (MCMC). Spatial models are fit using Nearest Neighbor Gaussian Processes (NNGPs). Details on NNGP models are given in Datta, Banerjee, Finley, and Gelfand (2016) <doi:10.1080/01621459.2015.1044091> and Finley, Datta, and Banerjee (2022) <doi:10.18637/jss.v103.i05>. Fits single-species and multi-species spatial and non-spatial versions of generalized linear mixed models (Gaussian, Poisson, Negative Binomial), N-mixture models (Royle 2004 <doi:10.1111/j.0006-341X.2004.00142.x>) and hierarchical distance sampling models (Royle, Dawson, Bates (2004) <doi:10.1890/03-3127>). Multi-species spatial models are fit using a spatial factor modeling approach with NNGPs for computational efficiency.

Computes the optimal alignment of two character sequences. Visualizes the result of the alignment in a matrix plot. Needleman, Saul B.; Wunsch, Christian D. (1970) "A general method applicable to the search for similarities in the amino acid sequence of two proteins" <doi:10.1016/0022-2836(70)90057-4>.

This package provides tools for analysing the agreement of two or more rankings of the same items. Examples are importance rankings of predictor variables and risk predictions of subjects. Benchmarks for agreement are computed based on random permutation and bootstrap. See Ekstrøm CT, Gerds TA, Jensen, AK (2018). "Sequential rank agreement methods for comparison of ranked lists." _Biostatistics_, *20*(4), 582-598 <doi:10.1093/biostatistics/kxy017> for more information.