This package provides a friendly (flexible) Markov Chain Monte Carlo (MCMC) framework for implementing Metropolis-Hastings algorithm in a modular way allowing users to specify automatic convergence checker, personalized transition kernels, and out-of-the-box multiple MCMC chains using parallel computing. Most of the methods implemented in this package can be found in Brooks et al. (2011, ISBN 9781420079425). Among the methods included, we have: Haario (2001) <doi:10.1007/s11222-011-9269-5> Adaptive Metropolis, Vihola (2012) <doi:10.1007/s11222-011-9269-5> Robust Adaptive Metropolis, and Thawornwattana et al. (2018) <doi:10.1214/17-BA1084> Mirror transition kernels.

This is a set of functions to retrieve information about GIMMS NDVI3g files currently available online; download (and re-arrange, in the case of NDVI3g.v0) the half-monthly data sets; import downloaded files from ENVI binary (NDVI3g.v0) or NetCDF format (NDVI3g.v1) directly into R based on the widespread raster package; conduct quality control; and generate monthly composites (e.g., maximum values) from the half-monthly input data. As a special gimmick, a method is included to conveniently apply the Mann-Kendall trend test upon Raster* images, optionally featuring trend-free pre-whitening to account for lag-1 autocorrelation.

Classical multivariate methods such as Hotelling's T-test are based on the sample mean vector and the sample covariance matrix, which makes them sensitive to outliers and heavy-tailed distributions. The package provides nonparametric alternatives based on marginal ranks, spatial signs and spatial ranks, as reviewed for example in Nordhausen and Oja (2018) <doi:10.1146/annurev-statistics-031017-100247>. It contains tests for location and independence, Hotelling's T-test as a parametric reference, the spatial median and other location estimates, and several estimates of shape. The tests can also be applied to invariant coordinates obtained with the package ICS'.

This package provides a method for detecting outliers with a Kalman filter on impulsed noised outliers and prediction on cleaned data. kfino is a robust sequential algorithm allowing to filter data with a large number of outliers. This algorithm is based on simple latent linear Gaussian processes as in the Kalman Filter method and is devoted to detect impulse-noised outliers. These are data points that differ significantly from other observations. ML (Maximization Likelihood) and EM (Expectation-Maximization algorithm) algorithms were implemented in kfino'. The method is described in full details in the following arXiv e-Print: <arXiv:2208.00961>.

This R package provides an implementation of multivariate extensions of a well-known fractal analysis technique, Detrended Fluctuations Analysis (DFA; Peng et al., 1995<doi:10.1063/1.166141>), for multivariate time series: multivariate DFA (mvDFA). Several coefficients are implemented that take into account the correlation structure of the multivariate time series to varying degrees. These coefficients may be used to analyze long memory and changes in the dynamic structure that would by univariate DFA. Therefore, this R package aims to extend and complement the original univariate DFA (Peng et al., 1995) for estimating the scaling properties of nonstationary time series.

Estimates random effect latent measurement models, wherein the loadings, residual variances, intercepts, latent means, and latent variances all vary across groups. The random effect variances of the measurement parameters are then modeled using a hierarchical inclusion model, wherein the inclusion of the variances (i.e., whether it is effectively zero or non-zero) is informed by similar parameters (of the same type, or of the same item). This additional hierarchical structure allows the evidence in favor of partial invariance to accumulate more quickly, and yields more certain decisions about measurement invariance. Martin, Williams, and Rast (2020) <doi:10.31234/osf.io/qbdjt>.

This package provides methods for estimating and utilizing the multivariate generalized propensity score (mvGPS) for multiple continuous exposures described in Williams, J.R, and Crespi, C.M. (2020) <arxiv:2008.13767>. The methods allow estimation of a dose-response surface relating the joint distribution of multiple continuous exposure variables to an outcome. Weights are constructed assuming a multivariate normal density for the marginal and conditional distribution of exposures given a set of confounders. Confounders can be different for different exposure variables. The weights are designed to achieve balance across all exposure dimensions and can be used to estimate dose-response surfaces.

An inspectable and composable workflow for search-and-replace in text files. Files can be listed, filtered, and searched separately, with inspectable exclusions showing what was excluded and why. Matches are represented as structured vectors that can be printed with context, summarized, and filtered. Replacements can be defined at search time or set and updated after the search. Only matches present in the vector are modified when files are written. Backup and restore helpers are provided for file workflows. Text that has already been read can also be searched and updated directly, giving users control over input, output, and encoding when needed.

The library allows to perform a multivariate time series classification based on the use of Discrete Wavelet Transform for feature extraction, a step wise discriminant to select the most relevant features and finally, the use of a linear or quadratic discriminant for classification. Note that all these steps can be done separately which allows to implement new steps. Velasco, I., Sipols, A., de Blas, C. S., Pastor, L., & Bayona, S. (2023) <doi:10.1186/S12938-023-01079-X>. Percival, D. B., & Walden, A. T. (2000,ISBN:0521640687). Maharaj, E. A., & Alonso, A. M. (2014) <doi:10.1016/j.csda.2013.09.006>.

Reads, navigates, and writes XML documents using a bundled copy of the Expat parser (<https://libexpat.github.io/>), so that no system XML library is required. Documents are parsed into a faithful immutable tree exposed through a small vectorized navigation interface. Parsing is strict by default: document type declarations are rejected, external entity resolution is not compiled in, and configurable limits bound nesting depth and memory use. A registered C interface allows other packages to parse XML without linking against the parser themselves, and to feed it incrementally so that a large document need not be held in memory all at once.

Several fast random number generators are provided as C++ header-only libraries: the PCG family as well as Xoroshiro128+ and Xoshiro256+. Additionally, fast functions for generating random numbers according to a uniform, normal and exponential distribution are included. The latter two use the Ziggurat algorithm originally proposed by Marsaglia and Tsang. These functions are exported to R and as a C++ interface and are enabled for use with the default 64 bit generator from the PCG family, Xoroshiro128+ and Xoshiro256+ as well as the 64 bit version of the 20 rounds Threefry engine (Salmon et al., 2011) as provided by the package sitmo.

This package provides a wrapper built around the libLBFGS optimization library by Naoaki Okazaki. The lbfgs package implements both the Limited-memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) and the Orthant-Wise Quasi-Newton Limited-Memory (OWL-QN) optimization algorithms. The L-BFGS algorithm solves the problem of minimizing an objective, given its gradient, by iteratively computing approximations of the inverse Hessian matrix. The OWL-QN algorithm finds the optimum of an objective plus the L1-norm of the problem's parameters. The package offers a fast and memory-efficient implementation of these optimization routines, which is particularly suited for high-dimensional problems.

SPIAT (**Sp**atial **I**mage **A**nalysis of **T**issues) is an R package with a suite of data processing, quality control, visualization and data analysis tools. SPIAT is compatible with data generated from single-cell spatial proteomics platforms (e.g. OPAL, CODEX, MIBI, cellprofiler). SPIAT reads spatial data in the form of X and Y coordinates of cells, marker intensities and cell phenotypes. SPIAT includes six analysis modules that allow visualization, calculation of cell colocalization, categorization of the immune microenvironment relative to tumor areas, analysis of cellular neighborhoods, and the quantification of spatial heterogeneity, providing a comprehensive toolkit for spatial data analysis.

This package provides functions to standardise the analysis of Differential Allelic Representation (DAR). DAR compromises the integrity of Differential Expression analysis results as it can bias expression, influencing the classification of genes (or transcripts) as being differentially expressed. DAR analysis results in an easy-to-interpret value between 0 and 1 for each genetic feature of interest, where 0 represents identical allelic representation and 1 represents complete diversity. This metric can be used to identify features prone to false-positive calls in Differential Expression analysis, and can be leveraged with statistical methods to alleviate the impact of such artefacts on RNA-seq data.

Exclusion-based parentage assignment is essential for studies in biodiversity conservation and breeding programs - Kang Huang, Rui Mi, Derek W Dunn, Tongcheng Wang, Baoguo Li, (2018), <doi:10.1534/genetics.118.301592>. The tool compares multilocus genotype data of potential parents and offspring, identifying likely parentage relationships while accounting for genotyping errors, missing data, and duplicate genotypes. acoRn includes two algorithms: one generates synthetic genotype data based on user-defined parameters, while the other analyzes existing genotype data to identify parentage patterns. The package is versatile, applicable to diverse organisms, and offers clear visual outputs, making it a valuable resource for researchers.

This package provides a tool for causal meta-analysis. This package implements the aggregation formulas and inference methods proposed in Berenfeld et al. (2025) <doi:10.48550/arXiv.2505.20168>. Users can input aggregated data across multiple studies and compute causally meaningful aggregated effects of their choice (risk difference, risk ratio, odds ratio, etc) under user-specified population weighting. The built-in function camea() allows to obtain precise variance estimates for these effects and to compare the latter to a classical meta-analysis aggregate, the random effect model, as implemented in the metafor package <https://CRAN.R-project.org/package=metafor>.

The amplitude-dependent exponential autoregressive (EXPAR) time series model, initially proposed by Haggan and Ozaki (1981) <doi:10.2307/2335819> has been implemented in this package. Throughout various studies, the model has been found to adequately capture the cyclical nature of datasets. Parameter estimation of such family of models has been tackled by the approach of minimizing the residual sum of squares (RSS). Model selection among various candidate orders has been implemented using various information criteria, viz., Akaike information criteria (AIC), corrected Akaike information criteria (AICc) and Bayesian information criteria (BIC). An illustration utilizing data of egg price indices has also been provided.

This package provides a unified interface to download, harmonise and extract a wide range of environmental and socio-economic variables from established open data web services (such as WorldClim <https://www.worldclim.org/>, CHELSA <https://chelsa-climate.org/> and Bio-ORACLE <https://www.bio-oracle.org/>, among others) for use in macroecology and biogeography. The package handles spatial subsetting to a study area, reprojection to a common coordinate reference system, and extraction of values at sampling points, so that predictors from heterogeneous sources can be assembled within a single reproducible workflow. Helper functions for collinearity checking and variable exploration are also included.

This package provides a gate-keeping procedure to test a primary and a secondary endpoint in a group sequential design with multiple interim looks. Computations related to group sequential primary and secondary boundaries. Refined secondary boundaries are calculated for a gate-keeping test on a primary and a secondary endpoint in a group sequential design with multiple interim looks. The choices include both the standard boundaries and the boundaries using error spending functions. See Tamhane et al. (2018), "A gatekeeping procedure to test a primary and a secondary endpoint in a group sequential design with multiple interim looks", Biometrics, 74(1), 40-48.

Bayesian network learning using the orthogonal matching pursuit to perform variable selection and then application of the Hill-Climbing greedy search or the TABU search to orient the edges. Some relevant papers are Tsagris M. and Adam C. (2026) <doi:10.13140/RG.2.2.26278.28489>, Tsagris M. (2021) <doi:10.1007/s10614-020-10065-7>, Tsagris M. (2022) <doi:10.3390/math10152604>, Tsagris et al. (2022) <doi:10.1109/TCBB.2020.3029952>, Tsamardinos I., Brown L.E. and Aliferis C.F. (2006) <doi:10.1007/s10994-006-6889-7>, Mallat S. G. and Zhang Z. (1993) <doi:10.1109/78.258082>.

This package provides an Interface to Web-Services defined as standards by the Open Geospatial Consortium (OGC), including Web Feature Service (WFS) for vector data, Web Coverage Service (WCS), Catalogue Service (CSW) for ISO/OGC metadata, Web Processing Service (WPS) for data processes, and associated standards such as the common web-service specification (OWS) and OGC Filter Encoding. Partial support is provided for the Web Map Service (WMS). The purpose is to add support for additional OGC service standards such as Web Coverage Processing Service (WCPS), the Sensor Observation Service (SOS), or even new standard services emerging such OGC API or SensorThings.

Constructs a model-averaged propensity score as a convex combination of candidate propensity score models, with mixing weights selected on a simplex grid to optimize covariate or prognostic-score balance, implementing the method of Kabata, Stuart and Shintani (2024) <doi:10.1186/s12874-024-02350-y>. Prognostic scores follow Hansen (2008) <doi:10.1093/biomet/asn004>: outcome models are fit on untreated units only. The resulting score is designed to be supplied directly to the matchit() function of MatchIt as a distance measure or to the weightit() function of WeightIt as a propensity score, with balance assessment via cobalt'.

Raptor is a C library providing a set of parsers and serialisers that generate Resource Description Framework (RDF) triples by parsing syntaxes or serialise the triples into a syntax. The supported parsing syntaxes are RDF/XML, N-Quads, N-Triples 1.0 and 1.1, TRiG, Turtle 2008 and 2013, RDFa 1.0 and 1.1, RSS tag soup including all versions of RSS, Atom 1.0 and 0.3, GRDDL and microformats for HTML, XHTML and XML. The serialising syntaxes are RDF/XML (regular, abbreviated, XMP), Turtle 2013, N-Quads, N-Triples 1.1, Atom 1.0, RSS 1.0, GraphViz DOT, HTML and JSON.

Implementation of the autocorrelated conditioned Latin Hypercube Sampling (acLHS) algorithm for 1D (time-series) and 2D (spatial) data. The acLHS algorithm is an extension of the conditioned Latin Hypercube Sampling (cLHS) algorithm that allows sampled data to have similar correlative and statistical features of the original data. Only a properly formatted dataframe needs to be provided to yield subsample indices from the primary function. For more details about the cLHS algorithm, see Minasny and McBratney (2006), <doi:10.1016/j.cageo.2005.12.009>. For acLHS, see Le and Vargas (2024) <doi:10.1016/j.cageo.2024.105539>.