We extend two general methods of moment estimators to panel vector autoregression models (PVAR) with p lags of endogenous variables, predetermined and strictly exogenous variables. This general PVAR model contains the first difference GMM estimator by Holtz-Eakin et al. (1988) <doi:10.2307/1913103>, Arellano and Bond (1991) <doi:10.2307/2297968> and the system GMM estimator by Blundell and Bond (1998) <doi:10.1016/S0304-4076(98)00009-8>. We also provide specification tests (Hansen overidentification test, lag selection criterion and stability test of the PVAR polynomial) and classical structural analysis for PVAR models such as orthogonal and generalized impulse response functions, bootstrapped confidence intervals for impulse response analysis and forecast error variance decompositions.

Nonparametric method for testing the equality of the spectral densities of two time series of possibly different lengths. The time series are preprocessed with the discrete cosine transform and the variance stabilising transform to obtain an approximate Gaussian regression setting for the log-spectral density function. The test statistic is based on the squared L2 norm of the difference between the estimated log-spectral densities. The test returns the result, the statistic value, and the p-value. It also provides the estimated empirical quantile and null distribution under the hypothesis of equal spectral densities. An example using EEG data is included. For details see Nadin, Krivobokova, Enikeeva (2026), <doi:10.48550/arXiv.2602.10774>.

Offers a helping hand to psychologists and other behavioral scientists who routinely deal with experimental data from factorial experiments. It includes several functions to format output from other R functions according to the style guidelines of the APA (American Psychological Association). This formatted output can be copied directly into manuscripts to facilitate data reporting. These features are backed up by a toolkit of several small helper functions, e.g., offering out-of-the-box outlier removal. The package lends its name to Georg "Schorsch" Schuessler, ingenious technician at the Department of Psychology III, University of Wuerzburg. For details on the implemented methods, see Roland Pfister and Markus Janczyk (2016) <doi: 10.20982/tqmp.12.2.p147>.

An offline verifier for verifiable credentials and delegation chains issued by the Attestix Python core. Verifies Ed25519 (RFC 8032) signatures over W3C Verifiable Credentials, decodes Ed25519 did:key identifiers, and verifies UCAN delegation chains (EdDSA JWT's) including capability attenuation, with no Python runtime required. Reproduces the Attestix JCS-style JSON canonical form (a practical subset of RFC 8785 that additionally applies NFC Unicode normalization) byte-for-byte so that signatures produced by the reference implementation verify here. Useful for compliance, research and biostatistics users who work in R and need to check AI-agent compliance credentials. See <https://attestix.io> for the project and <https://attestix.io/spec/bundle/v1> for the bundle wire format.

Implementing algorithms and fitting models when sites (possibly remote) share computation summaries rather than actual data over HTTP with a master R process (using opencpu', for example). A stratified Cox model and a singular value decomposition are provided. The former makes direct use of code from the R survival package. (That is, the underlying Cox model code is derived from that in the R survival package.) Sites may provide data via several means: CSV files, Redcap API, etc. An extensible design allows for new methods to be added in the future and includes facilities for local prototyping and testing. Web applications are provided (via shiny') for the implemented methods to help in designing and deploying the computations.

Bayesian estimation and analysis methods for Probit Unfolding Models (PUMs), a novel class of scaling models designed for binary preference data. These models allow for both monotonic and non-monotonic response functions. The package supports Bayesian inference for both static and dynamic PUMs using Markov chain Monte Carlo (MCMC) algorithms with minimal or no tuning. Key functionalities include posterior sampling, hyperparameter selection, data preprocessing, model fit evaluation, and visualization. The methods are particularly suited to analyzing voting data, such as from the U.S. Congress or Supreme Court, but can also be applied in other contexts where non-monotonic responses are expected. For methodological details, see Shi et al. (2025) <doi:10.48550/arXiv.2504.00423>.

It provides versatile tools for analysis of birth and death based Markovian Queueing Models and Single and Multiclass Product-Form Queueing Networks. It implements M/M/1, M/M/c, M/M/Infinite, M/M/1/K, M/M/c/K, M/M/c/c, M/M/1/K/K, M/M/c/K/K, M/M/c/K/m, M/M/Infinite/K/K, Multiple Channel Open Jackson Networks, Multiple Channel Closed Jackson Networks, Single Channel Multiple Class Open Networks, Single Channel Multiple Class Closed Networks and Single Channel Multiple Class Mixed Networks. Also it provides a B-Erlang, C-Erlang and Engset calculators. This work is dedicated to the memory of D. Sixto Rios Insua.

The purpose of this package is to manipulate SVG files that are templates of charts the user wants to produce. In vector graphics one copes with x-/y-coordinates of elements (e.g. lines, rectangles, text). Their scale is often dependent on the program that is used to produce the graphics. In applied statistics one usually has numeric values on a fixed scale (e.g. percentage values between 0 and 100) to show in a chart. Basically, svgtools transforms the statistical values into coordinates and widths/heights of the vector graphics. This is done by stackedBar() for bar charts, by linesSymbols() for charts with lines and/or symbols (dot markers) and scatterSymbols() for scatterplots.

SqueezeMeta is a versatile pipeline for the automated analysis of metagenomics/metatranscriptomics data (<https://github.com/jtamames/SqueezeMeta>). This package provides functions loading SqueezeMeta results into R, filtering them based on different criteria, and visualizing the results using basic plots. The SqueezeMeta project (and any subsets of it generated by the different filtering functions) is parsed into a single object, whose different components (e.g. tables with the taxonomic or functional composition across samples, contig/gene abundance profiles) can be easily analyzed using other R packages such as vegan or DESeq2'. The methods in this package are further described in Puente-Sánchez et al., (2020) <doi:10.1186/s12859-020-03703-2>.

Estimates the authors or speakers of texts. Methods developed in Huang, Perry, and Spirling (2020) <doi:10.1017/pan.2019.49>. The model is built on a Bayesian framework in which the distinctiveness of each speaker is defined by how different, on average, the speaker's terms are to everyone else in the corpus of texts. An optional cross-validation method is implemented to select the subset of terms that generate the most accurate speaker predictions. Once a set of terms is selected, the model can be estimated. Speaker distinctiveness and term influence can be recovered from parameters in the model using package functions. Once fitted, the model can be used to predict authorship of new texts.

Allows users to quickly load multiple patients electrocardiographic (ECG) data at once and conduct relevant time analysis of heart rate variability (HRV) without manual edits from a physician or data cleaning specialist. The package provides the unique ability to iteratively filter, plot, and store time analysis results in a data frame while writing plots to a predefined folder. This streamlines the workflow for HRV analysis across multiple datasets. Methods are based on Rodrà guez-Liñares et al. (2011) <doi:10.1016/j.cmpb.2010.05.012>. Examples of applications using this package include Kwon et al. (2022) <doi:10.1007/s10286-022-00865-2> and Lawrence et al. (2023) <doi:10.1016/j.autneu.2022.103056>.

Generates balancing weights for causal effect estimation in observational studies with binary, multi-category, or continuous point or longitudinal treatments by easing and extending the functionality of several R packages and providing in-house estimation methods. Available methods include those that rely on parametric modeling, optimization, and machine learning. Also allows for assessment of weights and checking of covariate balance by interfacing directly with the cobalt package. Methods for estimating weighted regression models that take into account uncertainty in the estimation of the weights via M-estimation or bootstrapping are available. See the vignette "Installing Supporting Packages" for instructions on how to install any optional package WeightIt uses, including those that may not be on CRAN.

This is a package for graphical and statistical analyses of environmental data, with a focus on analyzing chemical concentrations and physical parameters, usually in the context of mandated environmental monitoring. It provides major environmental statistical methods found in the literature and regulatory guidance documents, with extensive help that explains what these methods do, how to use them, and where to find them in the literature. It comes with numerous built-in data sets from regulatory guidance documents and environmental statistics literature. It includes scripts reproducing analyses presented in the book "EnvStats: An R Package for Environmental Statistics" (Millard, 2013, Springer, ISBN 978-1-4614-8455-4, https://link.springer.com/book/10.1007/978-1-4614-8456-1).

In epigenome-wide association studies, the measured signals for each sample are a mixture of methylation profiles from different cell types. The current approaches to the association detection only claim whether a cytosine-phosphate-guanine (CpG) site is associated with the phenotype or not, but they cannot determine the cell type in which the risk-CpG site is affected by the phenotype. We propose a solid statistical method, HIgh REsolution (HIRE), which not only substantially improves the power of association detection at the aggregated level as compared to the existing methods but also enables the detection of risk-CpG sites for individual cell types. The "HIREewas" R package is to implement HIRE model in R.

OMICsPCA is an analysis pipeline designed to integrate multi OMICs experiments done on various subjects (e.g. Cell lines, individuals), treatments (e.g. disease/control) or time points and to analyse such integrated data from various various angles and perspectives. In it's core OMICsPCA uses Principal Component Analysis (PCA) to integrate multiomics experiments from various sources and thus has ability to over data insufficiency issues by using the ingegrated data as representatives. OMICsPCA can be used in various application including analysis of overall distribution of OMICs assays across various samples /individuals /time points; grouping assays by user-defined conditions; identification of source of variation, similarity/dissimilarity between assays, variables or individuals.

Description: Provides comprehensive tools for analysing and characterizing mixed-level factorial designs arranged in blocks. Includes construction and validation of incidence structures, computation of C-matrices, evaluation of A-, D-, E-, and MV-efficiencies, checking of orthogonal factorial structure (OFS), diagnostics based on Hamming distance, discrepancy measures, B-criterion, Es^2 statistics, J2-distance and J2-efficiency, Phi-p optimality, and symmetry conditions for universal optimality. The methodological framework follows foundational work on factorial and mixed-level design assessment by Xu and Wu (2001) <doi:10.1214/aos/1013699993>, and Gupta (1983) <doi:10.1111/j.2517-6161.1983.tb01253.x>. These methods assist in selecting, comparing, and studying factorial block designs across a range of experimental situations.

Uses ggplot2 to visualise either (a) a single DNA/RNA sequence split across multiple lines, (b) multiple DNA/RNA sequences, each occupying a whole line, or (c) base modifications such as DNA methylation called by modified bases models in Dorado or Guppy. Functions starting with visualise_<>() are the main plotting functions, and functions starting with extract_and_sort_<>() are key helper functions for reading files and reformatting data. Source code is available at <https://github.com/ejade42/ggDNAvis>, a full non-expert user guide is available at <https://ejade42.github.io/ggDNAvis/>, and an interactive web-app version of the software is available at <https://ejade42.github.io/ggDNAvis/articles/interactive_app.html>.

This package provides tools for longitudinal meta-analysis where studies contribute effect sizes at multiple follow-up time points. Implements robust variance estimation (RVE) with Tipton small-sample corrections following Hedges, Tipton, and Johnson (2010) <doi:10.1002/jrsm.5> and Tipton (2015) <doi:10.1037/met0000011>, time-varying sensitivity analysis via the Impact Threshold for a Confounding Variable (ITCV) following Frank (2000) <doi:10.1177/0049124100029002003>, benchmark calibration of the ITCV threshold against observed study-level covariates, spline-based nonlinear time-trend modeling with a nonlinearity test, and leave-k-out fragility analysis across the follow-up trajectory. Designed for researchers synthesising evidence from studies with repeated outcome measurement in education, psychology, health, and the social sciences.

This package implements the navigated weighting (NAWT) proposed by Katsumata (2020) <arXiv:2005.10998>, which improves the inverse probability weighting by utilizing estimating equations suitable for a specific pre-specified parameter of interest (e.g., the average treatment effects or the average treatment effects on the treated) in propensity score estimation. It includes the covariate balancing propensity score proposed by Imai and Ratkovic (2014) <doi:10.1111/rssb.12027>, which uses covariate balancing conditions in propensity score estimation. The point estimate of the parameter of interest as well as coefficients for propensity score estimation and their uncertainty are produced using the M-estimation. The same functions can be used to estimate average outcomes in missing outcome cases.

In the spirit of Anscombe's quartet, this package includes datasets that demonstrate the importance of visualizing your data, the importance of not relying on statistical summary measures alone, and why additional assumptions about the data generating mechanism are needed when estimating causal effects. The package includes "Anscombe's Quartet" (Anscombe 1973) <doi:10.1080/00031305.1973.10478966>, D'Agostino McGowan & Barrett (2023) "Causal Quartet" <doi:10.48550/arXiv.2304.02683>, "Datasaurus Dozen" (Matejka & Fitzmaurice 2017), "Interaction Triptych" (Rohrer & Arslan 2021) <doi:10.1177/25152459211007368>, "Rashomon Quartet" (Biecek et al. 2023) <doi:10.48550/arXiv.2302.13356>, and Gelman "Variation and Heterogeneity Causal Quartets" (Gelman et al. 2023) <doi:10.48550/arXiv.2302.12878>.

This package provides a unified interface to access open data from the Brazilian National Treasury ('Tesouro Nacional') and related government APIs. Covers six data sources: SICONFI <https://apidatalake.tesouro.gov.br/docs/siconfi/> for fiscal reports ('RREO', RGF', DCA', MSC') and entity information; CUSTOS <https://apidatalake.tesouro.gov.br/docs/custos/> for federal government cost data; SADIPEM <https://apidatalake.tesouro.gov.br/docs/sadipem/> for public debt and credit operations; Transferencias Constitucionais <https://apiapex.tesouro.gov.br/aria/v1/transferencias_constitucionais/docs> for constitutional transfers to states and municipalities; SIORG <https://estruturaorganizacional.dados.gov.br> for federal organizational structure; and SIOPE ('FNDE'/'MEC') for education spending data. Features automatic pagination, in-memory caching, retry logic, and tidy output.

This package implements a faster and more expressive version of Bayesian Additive Regression Trees that, at a high level, approximates unknown functions as a weighted sum of binary regression tree ensembles. Supports fitting (generalized) linear varying coefficient models that posits a linear relationship between the inverse link and some covariates but allows that relationship to change as a function of other covariates. Additionally supports fitting heteroscedastic BART models, in which both the mean and log-variance are approximated with separate regression tree ensembles. A formula interface allows for different splitting variables to be used in each ensemble. For more details see Deshpande (2025) <doi:10.1080/10618600.2024.2431072> and Deshpande et al. (2026) <doi:10.1214/24-BA1470>.

This package provides tools to analyse animal movement and space-use patterns from telemetry data using methods derived from statistical physics. Methods span displacement-based approaches, distribution fitting, space-use metrics (including the influence of correlations on space-use), network-based community detection, and measures of entropy and predictability. The package enables characterisation of these patterns across spatial and temporal scales, including variation within and among individuals (inter- and intraspecific analyses). Outputs include interpretable metrics and visualisations to support ecological analysis and the investigation of fundamental movement processes. For applications of these methods in ecological studies see Rodrà guez et al. (2017) <doi:10.1038/s41598-017-00165-0> and Sequeira et al. (2018) <doi:10.1073/pnas.1716137115>.

This package provides methods for evaluating and comparing clustering results, with an emphasis on sample-level clustering where observations are treated as the units being clustered and perturbation is performed by removing samples. The package implements internal, stability, and external validation measures and supports the comparison and ranking of clustering procedures across tuning parameter choices. It is conceptually related to the clValid package, which assesses clustering stability by perturbing features; in contrast, this package focuses on perturbing the samples being clustered, making the validation framework suitable for applications such as single-cell clustering where samples or cells are the primary units of interest. Methods are based in part on Visser and Datta (2025) <doi:10.1002/sim.70331>.