Integration of Earth system data from various sources is a challenging task. Except for their qualitative heterogeneity, different data records exist for describing similar Earth system process at different spatio-temporal scales. Data inter-comparison and validation are usually performed at a single spatial or temporal scale, which could hamper the identification of potential discrepancies in other scales. csa package offers a simple, yet efficient, graphical method for synthesizing and comparing observed and modelled data across a range of spatio-temporal scales. Instead of focusing at specific scales, such as annual means or original grid resolution, we examine how their statistical properties change across spatio-temporal continuum.

This package provides tools for clustering high-dimensional data. In particular, it contains the methods described in <doi:10.1093/bioinformatics/btaa243>, <arXiv:2010.00950>.

This package provides a covariate-dependent approach to Gaussian graphical modeling as described in Dasgupta et al. (2022). Employs a novel weighted pseudo-likelihood approach to model the conditional dependence structure of data as a continuous function of an extraneous covariate. The main function, covdepGE::covdepGE(), estimates a graphical representation of the conditional dependence structure via a block mean-field variational approximation, while several auxiliary functions (inclusionCurve(), matViz(), and plot.covdepGE()) are included for visualizing the resulting estimates.

This package provides methods and data for color science - color conversions by observer, illuminant, and gamma. Color matching functions and chromaticity diagrams. Color indices, color differences, and spectral data conversion/analysis. This package is deprecated and will someday be removed; for reasons and details please see the README file.

This package provides means of plots for comparing utilization data of compute systems.

The goal of cvsem is to provide functions that allow for comparing Structural Equation Models (SEM) using cross-validation. Users can specify multiple SEMs using lavaan syntax. cvsem computes the Kullback Leibler (KL) Divergence between 1) the model implied covariance matrix estimated from the training data and 2) the sample covariance matrix estimated from the test data described in Cudeck, Robert & Browne (1983) <doi:10.18637/jss.v048.i02>. The KL Divergence is computed for each of the specified SEMs allowing for the models to be compared based on their prediction errors.

Formal psychological models of categorization and learning, independently-replicated data sets against which to test them, and simulation archives.

This package provides an object class for dealing with many multivariate probability distributions at once, useful for simulation.

This package provides a daily summary of the Coronavirus (COVID-19) cases in Italy by country, region and province level. Data source: Presidenza del Consiglio dei Ministri - Dipartimento della Protezione Civile <https://www.protezionecivile.it/>.

This package provides a new robust principal component analysis algorithm is implemented that relies upon the Cauchy Distribution. The algorithm is suitable for high dimensional data even if the sample size is less than the number of variables. The methodology is described in this paper: Fayomi A., Pantazis Y., Tsagris M. and Wood A.T.A. (2024). "Cauchy robust principal component analysis with applications to high-dimensional data sets". Statistics and Computing, 34: 26. <doi:10.1007/s11222-023-10328-x>.

This package provides tools for fitting the copCAR (Hughes, 2015) <DOI:10.1080/10618600.2014.948178> regression model for discrete areal data. Three types of estimation are supported (continuous extension, composite marginal likelihood, and distributional transform), for three types of outcomes (Bernoulli, negative binomial, and Poisson).

Includes the 100 datasets simulated by Congreve and Lamsdell (2016) <doi:10.1111/pala.12236>, and analyses of the partition and quartet distance of reconstructed trees from the generative tree, as analysed by Smith (2019) <doi:10.1098/rsbl.2018.0632>.

This package provides functions for the input/output and visualization of medical imaging data in the form of CIFTI files <https://www.nitrc.org/projects/cifti/>.

In computationally demanding analysis projects, statisticians and data scientists asynchronously deploy long-running tasks to distributed systems, ranging from traditional clusters to cloud services. The crew.aws.batch package extends the mirai'-powered crew package with a worker launcher plugin for AWS Batch. Inspiration also comes from packages mirai by Gao (2023) <https://github.com/r-lib/mirai>, future by Bengtsson (2021) <doi:10.32614/RJ-2021-048>, rrq by FitzJohn and Ashton (2023) <https://github.com/mrc-ide/rrq>, clustermq by Schubert (2019) <doi:10.1093/bioinformatics/btz284>), and batchtools by Lang, Bischl, and Surmann (2017). <doi:10.21105/joss.00135>.

This package implements the framework introduced in Di Francesco and Mellace (2025) <doi:10.48550/arXiv.2502.11691>, shifting the focus to well-defined and interpretable estimands that quantify how treatment affects the probability distribution over outcome categories. It supports selection-on-observables, instrumental variables, regression discontinuity, and difference-in-differences designs.

This is a function for validating microarray clusters via reproducibility, based on the paper referenced below.

This package implements higher order likelihood-based inference for logistic and loglinear models.

This package provides tools for Delphi's COVIDcast Epidata API: data access, maps and time series plotting, and basic signal processing. The API includes a collection of numerous indicators relevant to the COVID-19 pandemic in the United States, including official reports, de-identified aggregated medical claims data, large-scale surveys of symptoms and public behavior, and mobility data, typically updated daily and at the county level. All data sources are documented at <https://cmu-delphi.github.io/delphi-epidata/api/covidcast.html>.

Computes genomic breeding values using external information on the markers. The package fits a linear mixed model with heteroscedastic random effects, where the random effect variance is fitted using a linear predictor and a log link. The method is described in Mouresan, Selle and Ronnegard (2019) <doi:10.1101/636746>.

This package provides a local haplotyping visualization toolbox to capture major patterns of co-inheritance between clusters of linked variants, whilst connecting findings to phenotypic and demographic traits across individuals. crosshap enables users to explore and understand genomic variation across a trait-associated region. For an example of successful local haplotype analysis, see Marsh et al. (2022) <doi:10.1007/s00122-022-04045-8>.

Fits constrained groupwise additive index models and provides functions for inference and interpretation of these models. The method is described in Masselot, Chebana, Campagna, Lavigne, Ouarda, Gosselin (2022) "Constrained groupwise additive index models" <doi:10.1093/biostatistics/kxac023>.

An implementation of the statistical methods commonly used for advanced composite materials in aerospace applications. This package focuses on calculating basis values (lower tolerance bounds) for material strength properties, as well as performing the associated diagnostic tests. This package provides functions for calculating basis values assuming several different distributions, as well as providing functions for non-parametric methods of computing basis values. Functions are also provided for testing the hypothesis that there is no difference between strength and modulus data from an alternate sample and that from a "qualification" or "baseline" sample. For a discussion of these statistical methods and their use, see the Composite Materials Handbook, Volume 1 (2012, ISBN: 978-0-7680-7811-4). Additional details about this package are available in the paper by Kloppenborg (2020, <doi:10.21105/joss.02265>).

This package provides functions for calculating and evaluating likelihood ratios from uni/multivariate continuous observations.

Provide a series of functions to conduct a meta analysis of factor analysis based on co-occurrence matrices. The tool can be used to solve the factor structure (i.e. inner structure of a construct, or scale) debate in several disciplines, such as psychology, psychiatry, management, education so on. References: Shafer (2005) <doi:10.1037/1040-3590.17.3.324>; Shafer (2006) <doi:10.1002/jclp.20213>; Loeber and Schmaling (1985) <doi:10.1007/BF00910652>.