The functionality provided by this package is an expansion of the code of the statebins package, created by B. Rudis (2022), <doi:10.32614/CRAN.package.statebins>. It allows for the creation of square choropleths for the entire world, provided an appropriate specified grid is supplied.

Fits a multivariate linear mixed effects model that uses a polygenic term, after Zhou & Stephens (2014) (<https://www.nature.com/articles/nmeth.2848>). Of particular interest is the estimation of variance components with restricted maximum likelihood (REML) methods. Genome-wide efficient mixed-model association (GEMMA), as implemented in the package gemma2', uses an expectation-maximization algorithm for variance components inference for use in quantitative trait locus studies.

Identifying spatially variable genes is critical in linking molecular cell functions with tissue phenotypes. This package implemented a granularity-based dimension-agnostic tool for the identification of spatially variable genes. The detailed description of this method is available at Wang, J. and Li, J. et al. 2023 (Wang, J. and Li, J. (2023), <doi:10.1038/s41467-023-43256-5>).

This package performs end-to-end analysis of gene clustersâ such as photosynthesis, carbon/nitrogen/sulfur cycling, carotenoid, antibiotic, or viral marker genes (e.g., capsid, polymerase, integrase)â from genomes and metagenomes. It parses Basic Local Alignment Search Tool (BLAST) results in tab-delimited format produced by tools like NCBI BLAST+ and Diamond BLASTp, filters Open Reading Frames (ORFs) by length, detects contiguous clusters of reference genes, optionally extracts genomic coordinates, merges functional annotations, and generates publication-ready arrow plots. The package works seamlessly with or without the coding sequences input and skips plotting when no functional groups are found. For more details see Li et al. (2023) <doi:10.1038/s41467-023-42193-7>.

Generator and density function for the Generalized Inverse Gaussian (GIG) distribution.

Model and estimate the model parameters for the spatial model of individual-level infectious disease transmission in Susceptible-Infected-Recovered (SIR) framework.

When the response variable Y takes one of R > 1 values, the function glsm() computes the maximum likelihood estimates (MLEs) of the parameters under four models: null, complete, saturated, and logistic. It also calculates the log-likelihood values for each model. This method assumes independent, non-identically distributed variables. For grouped data with a multinomial outcome, where observations are divided into J populations, the function glsm() provides estimation for any number K of explanatory variables.

Analyze small-sample clustered or longitudinal data with binary outcome using modified generalized estimating equations (GEE) with bias-adjusted covariance estimator. The package provides any combination of three GEE methods and 12 covariance estimators.

Helper functions provide an accurate imputation algorithm for reconstructing the missing segment in a multi-variate data streams. Inspired by single-shot learning, it reconstructs the missing segment by identifying the first similar segment in the stream. Nevertheless, there should be one column of data available, i.e. a constraint column. The values of columns can be characters (A, B, C, etc.). The result of the imputed dataset will be returned a .csv file. For more details see Reza Rawassizadeh (2019) <doi:10.1109/TKDE.2019.2914653>.

This package implements Bayesian spatial and spatiotemporal models that optionally allow for extreme spatial deviations through time. glmmfields uses a predictive process approach with random fields implemented through a multivariate-t distribution instead of the usual multivariate normal. Sampling is conducted with Stan'. References: Anderson and Ward (2019) <doi:10.1002/ecy.2403>.

This package provides methods for processing spatial data for decision-making. This package is an R implementation of methods provided by the open source software GeoFIS <https://www.geofis.org> (Leroux et al. 2018) <doi:10.3390/agriculture8060073>. The main functionalities are the management zone delineation (Pedroso et al. 2010) <doi:10.1016/j.compag.2009.10.007> and data aggregation (Mora-Herrera et al. 2020) <doi:10.1016/j.compag.2020.105624>.

Set of functions to create datasets using a correlation matrix.

Generalized promotion time cure model (GPTCM) via Bayesian hierarchical modeling for multiscale data integration (Zhao et al. (2025) <doi:10.48550/arXiv.2509.01001>). The Bayesian GPTCMs are applicable for both low- and high-dimensional data.

Create a user-friendly plotting GUI for R'. In addition, one purpose of creating the R package is to facilitate third-party software to call R for drawing, for example, Phoenix WinNonlin software calls R to draw the drug concentration versus time curve.

Routines that allow the user to run goodness of fit tests based on empirical distribution functions for formal model evaluation in a general likelihood model. In addition, functions are provided to test if a sample follows Normal or Gamma distributions, validate the normality assumptions in a linear model, and examine the appropriateness of a Gamma distribution in generalized linear models with various link functions. Michael Arthur Stephens (1976) <http://www.jstor.org/stable/2958206>.

Estimates within and between time point interactions in experience sampling data, using the Graphical vector autoregression model in combination with regularization. See also Epskamp, Waldorp, Mottus & Borsboom (2018) <doi:10.1080/00273171.2018.1454823>.

Implementation of global envelopes for a set of general d-dimensional vectors T in various applications. A 100(1-alpha)% global envelope is a band bounded by two vectors such that the probability that T falls outside this envelope in any of the d points is equal to alpha. Global means that the probability is controlled simultaneously for all the d elements of the vectors. The global envelopes can be used for graphical Monte Carlo and permutation tests where the test statistic is a multivariate vector or function (e.g. goodness-of-fit testing for point patterns and random sets, functional analysis of variance, functional general linear model, n-sample test of correspondence of distribution functions), for central regions of functional or multivariate data (e.g. outlier detection, functional boxplot) and for global confidence and prediction bands (e.g. confidence band in polynomial regression, Bayesian posterior prediction). See Myllymäki and MrkviÄ ka (2024) <doi:10.18637/jss.v111.i03>, Myllymäki et al. (2017) <doi:10.1111/rssb.12172>, MrkviÄ ka and Myllymäki (2023) <doi:10.1007/s11222-023-10275-7>, MrkviÄ ka et al. (2016) <doi:10.1016/j.spasta.2016.04.005>, MrkviÄ ka et al. (2017) <doi:10.1007/s11222-016-9683-9>, MrkviÄ ka et al. (2020) <doi:10.14736/kyb-2020-3-0432>, MrkviÄ ka et al. (2021) <doi:10.1007/s11009-019-09756-y>, Myllymäki et al. (2021) <doi:10.1016/j.spasta.2020.100436>, MrkviÄ ka et al. (2022) <doi:10.1002/sim.9236>, Dai et al. (2022) <doi:10.5772/intechopen.100124>, DvoŠák and MrkviÄ ka (2022) <doi:10.1007/s00180-021-01134-y>, MrkviÄ ka et al. (2023) <doi:10.48550/arXiv.2309.04746>, and Konstantinou et al. (2024) <doi: 10.1007/s00180-024-01569-z>.

Spatio-temporal causal inference based on point process data. You provide the raw data of locations and timings of treatment and outcome events, specify counterfactual scenarios, and the package estimates causal effects over specified spatial and temporal windows. See Papadogeorgou, et al. (2022) <doi:10.1111/rssb.12548> and Mukaigawara, et al. (2024) <doi:10.31219/osf.io/5kc6f>.

This package provides a pipeline with high specificity and sensitivity in extracting proteins from the RefSeq database (National Center for Biotechnology Information). Manual identification of gene families is highly time-consuming and laborious, requiring an iterative process of manual and computational analysis to identify members of a given family. The pipelines implements an automatic approach for the identification of gene families based on the conserved domains that specifically define that family. See Die et al. (2018) <doi:10.1101/436659> for more information and examples.

Readable, complete and pretty graphs for correspondence analysis made with FactoMineR'. They can be rendered as interactive HTML plots, showing useful informations at mouse hover. The interest is not mainly visual but statistical: it helps the reader to keep in mind the data contained in the cross-table or Burt table while reading the correspondence analysis, thus preventing over-interpretation. Most graphs are made with ggplot2', which means that you can use the + syntax to manually add as many graphical pieces you want, or change theme elements. 3D graphs are made with plotly'.

Generic Machine Learning Inference on heterogeneous treatment effects in randomized experiments as proposed in Chernozhukov, Demirer, Duflo and Fernández-Val (2020) <arXiv:1712.04802>. This package's workhorse is the mlr3 framework of Lang et al. (2019) <doi:10.21105/joss.01903>, which enables the specification of a wide variety of machine learners. The main functionality, GenericML(), runs Algorithm 1 in Chernozhukov, Demirer, Duflo and Fernández-Val (2020) <arXiv:1712.04802> for a suite of user-specified machine learners. All steps in the algorithm are customizable via setup functions. Methods for printing and plotting are available for objects returned by GenericML(). Parallel computing is supported.

The goal of gsDesign2 is to enable fixed or group sequential design under non-proportional hazards. To enable highly flexible enrollment, time-to-event and time-to-dropout assumptions, gsDesign2 offers piecewise constant enrollment, failure rates, and dropout rates for a stratified population. This package includes three methods for designs: average hazard ratio, weighted logrank tests in Yung and Liu (2019) <doi:10.1111/biom.13196>, and MaxCombo tests. Substantial flexibility on top of what is in the gsDesign package is intended for selecting boundaries.

This package provides a nonparametric empirical Bayes method for recovering gradients (or growth velocities) from observations of smooth functions (e.g., growth curves) at isolated time points.

This package provides functions for Gaussian and Non Gaussian (bivariate) spatial and spatio-temporal data analysis are provided for a) (fast) simulation of random fields, b) inference for random fields using standard likelihood and a likelihood approximation method called weighted composite likelihood based on pairs and b) prediction using (local) best linear unbiased prediction. Weighted composite likelihood can be very efficient for estimating massive datasets. Both regression and spatial (temporal) dependence analysis can be jointly performed. Flexible covariance models for spatial and spatial-temporal data on Euclidean domains and spheres are provided. There are also many useful functions for plotting and performing diagnostic analysis. Different non Gaussian random fields can be considered in the analysis. Among them, random fields with marginal distributions such as Skew-Gaussian, Student-t, Tukey-h, Sin-Arcsin, Two-piece, Weibull, Gamma, Log-Gaussian, Binomial, Negative Binomial and Poisson. See the URL for the papers associated with this package, as for instance, Bevilacqua and Gaetan (2015) <doi:10.1007/s11222-014-9460-6>, Bevilacqua et al. (2016) <doi:10.1007/s13253-016-0256-3>, Vallejos et al. (2020) <doi:10.1007/978-3-030-56681-4>, Bevilacqua et. al (2020) <doi:10.1002/env.2632>, Bevilacqua et. al (2021) <doi:10.1111/sjos.12447>, Bevilacqua et al. (2022) <doi:10.1016/j.jmva.2022.104949>, Morales-Navarrete et al. (2023) <doi:10.1080/01621459.2022.2140053>, and a large class of examples and tutorials.