This package performs hypothesis testing for general block designs with empirical likelihood. The core computational routines are implemented using the Eigen C++ library and RcppEigen interface, with OpenMP for parallel computation. Details of the methods are given in Kim, MacEachern, and Peruggia (2023) <doi:10.1080/10485252.2023.2206919>. This work was supported by the U.S. National Science Foundation under Grants No. SES-1921523 and DMS-2015552.

Replication methods to compute some basic statistic operations (means, standard deviations, frequency tables, percentiles, mean comparisons using weighted effect coding, generalized linear models, and linear multilevel models) in complex survey designs comprising multiple imputed or nested imputed variables and/or a clustered sampling structure which both deserve special procedures at least in estimating standard errors. See the package documentation for a more detailed description along with references.

Testing for parallel trends is crucial in the Difference-in-Differences framework. To this end, this package performs equivalence testing in the context of Difference-in-Differences estimation. It allows users to test if pre-treatment trends in the treated group are â equivalentâ to those in the control group. Here, â equivalenceâ means that rejection of the null hypothesis implies that a function of the pre-treatment placebo effects (maximum absolute, average or root mean squared value) does not exceed a pre-specified threshold below which trend differences are considered negligible. The package is based on the theory developed in Dette & Schumann (2024) <doi:10.1080/07350015.2024.2308121>.

This package provides a series of R functions that come in handy while working with metabarcoding data. The reasoning of doing this is to have the same functions we use all the time stored in a curated, reproducible way. In a way it is all about putting together the grammar of the tidyverse from Wickham et al.(2019) <doi:10.21105/joss.01686> with the functions we have used in community ecology compiled in packages like vegan from Dixon (2003) <doi:10.1111/j.1654-1103.2003.tb02228.x> and phyloseq McMurdie & Holmes (2013) <doi:10.1371/journal.pone.0061217>. The package includes functions to read sequences from FAST(A/Q) into a tibble ('fasta_reader and fastq_reader'), to process cutadapt Martin (2011) <doi:10.14806/ej.17.1.200> info-file output. When it comes to sequence counts across samples, the package works with the long format in mind (a three column tibble with Sample, Sequence and counts ), with functions to move from there to the wider format.

Extreme value theory, nonparametric kernel estimation, tail conditional probabilities, extreme conditional quantile, adaptive estimation, quantile regression, survival probabilities.

This package provides data sets and R Codes for E.R. Williams, C.E. Harwood and A.C. Matheson (2023). Experimental Design and Analysis for Tree Improvement, CSIRO Publishing.

This package provides an extensible, vendor-neutral framework for importing, validating, harmonizing, transforming, visualizing, and modelling eye-tracking, pupillometry, behavioural, and biometric process data. The package uses explicit timebase and coordinate-space registries, preserves native fields and provenance, and offers first-class adapters for Gazepoint Analysis and Gazepoint Biometrics exports alongside generic and vendor-specific importers. Downstream tools support trial and area of interest reconstruction, signal-quality auditing, feature derivation, scanpath analysis, response-time and item-response workflows, and optional psychometric modelling engines. An integrated Gazepoint workflow produces quality-control evidence, media-trial reconstruction, plots, analysis-ready process tables, item response theory (IRT)-ready response structures, and reproducible reports. Brain Imaging Data Structure (BIDS) interoperability for eye-tracking and validation-release infrastructure support disk-backed storage, independent multi-vendor evidence, grouped validation, simulation calibration, model-equivalence audits, and explicitly experimental advanced psychometric process models. Research-scale infrastructure adds deterministic resumable Monte Carlo execution, atomic validation checkpoints, explicit advanced-model promotion gates, independent multi-vendor evidence registries, stable object contracts, partitioned disk-backed storage, optional probabilistic engines, and a fully synthetic multimodal benchmark for reproducibility testing. The measurement-intelligence programme adds probabilistic and compositional area of interest (AOI) analysis, measurement-uncertainty propagation, calibration and device-transportability audits, process reliability, phase-amplitude pupil registration, informative-missingness sensitivity, temporal and spatial process models, item-bank decision optimization, fairness monitoring, conditional process reference distributions, and evidence-provenance graphs.

An R interface to United States Environmental Protection Agency (EPA) Environmental Compliance History Online ('ECHO') Application Program Interface (API). ECHO provides information about EPA permitted facilities, discharges, and other reporting info associated with permitted entities. Data are obtained from <https://echo.epa.gov/>.

Interactive labelling of scatter plots, volcano plots and Manhattan plots using a shiny and plotly interface. Users can hover over points to see where specific points are located and click points on/off to easily label them. Labels can be dragged around the plot to place them optimally. Plots can be exported directly to PDF for publication. For plots with large numbers of points, points can optionally be rasterized as a bitmap, while all other elements (axes, text, labels & lines) are preserved as vector objects. This can dramatically reduce file size for plots with millions of points such as Manhattan plots, and is ideal for publication.

Automates feature engineering using evolutionary algorithms inspired by genetic programming. Starting from raw input features, the package evolves candidate transformation recipes through selection, crossover, and mutation, evaluating fitness via cross-validation or train/validation splits with gradient-boosted tree models ('LightGBM or XGBoost'). Built-in transformers include arithmetic, logarithmic, and power operations, interaction terms, target encoding, quantile and log-based binning, principal component analysis, truncated singular value decomposition, Uniform Manifold Approximation and Projection (UMAP) dimensionality reduction, and minimum spanning tree (MST) graph-based clustering. The evolutionary search yields an optimised feature recipe that can be applied to new data for prediction. Methods are described in McInnes et al. (2018) <doi:10.21105/joss.00861>, Ke et al. (2017) <doi:10.48550/arXiv.1711.08789>, Chen and Guestrin (2016) <doi:10.1145/2939672.2939785>, Gagolewski (2021) <doi:10.1016/j.softx.2021.100722>, Gagolewski (2026) <doi:10.32614/CRAN.package.lumbermark>, and Gagolewski (2026) <doi:10.32614/CRAN.package.deadwood>.

This package performs likelihood-based extreme value inferences with adjustment for the presence of missing values based on Simpson and Northrop (2026) <doi:10.1002/env.70075>. A Generalised Extreme Value distribution is fitted to block maxima using maximum likelihood estimation, with the location and scale parameters reflecting the numbers of non-missing raw values in each block. A Bayesian version is also provided. For the purposes of comparison, there are options to make no adjustment for missing values or to discard any block maximum for which greater than a percentage of the underlying raw values are missing. Example datasets containing missing values are provided.

Computes the Road Tolerance Index (RTI) and the Human Footprint Tolerance Index (HFTI) for species occurrence data. It automates data cleaning and integrates spatial data (roads and human footprint) to produce reproducible tolerance metrics for biodiversity and conservation research. The HFTI calculation is based on the global human footprint dataset by Mu et al. (2022) <doi:10.1038/s41597-022-01284-8>. This package is part of a PhD thesis focused on amphibian ecology in Brazil.

This is a collection of assorted functions and examples collected from various projects. Currently we have functionalities for simplifying overlapping time intervals, Charlson comorbidity score constructors for Danish data, getting frequency for multiple variables, getting standardized output from logistic and log-linear regressions, sibling design linear regression functionalities a method for calculating the confidence intervals for functions of parameters from a GLM, Bayes equivalent for hypothesis testing with asymptotic Bayes factor, and several help functions for generalized random forest analysis using grf'.

Forecasting time series with different decomposition based ARIMA models. For method details see Yu L, Wang S, Lai KK (2008). <doi:10.1016/j.eneco.2008.05.003>.

Energy landscape analysis (ELA) is a systematic method for analyzing an energy landscape represented as a weighted network. This R package is especially designed to analyze ecological dynamics, i.e., transitions in community compositions. For details of the analysis framework, please visit Suzuki K et al. (2021) <doi:10.1002/ecm.1469>.

This package provides tools for the analysis of questionnaire and survey data in epidemiological and One Health research. The package supports data preparation, reliability assessment, exploratory factor analysis, Kaiser-Meyer-Olkin assessment, parallel analysis, visualization, reporting, and export of results using a consistent analysis workflow. The methods are based on established approaches to psychometric and multivariate analysis; see Kaiser (1974) <doi:10.1007/BF02291575>, Horn (1965) <doi:10.1007/BF02289447>, and Tabachnick and Fidell (2019, ISBN:9780134790541).

Reproduces classic Rasch psychometric analysis features using Bayesian item response theory models fitted with brms following Bürkner (2021) <doi:10.18637/jss.v100.i05> and Bürkner (2020) <doi:10.3390/jintelligence8010005>. Supports both dichotomous and polytomous Rasch models. Features include posterior predictive item fit, conditional infit, item-restscore associations, person fit, differential item functioning, local dependence assessment via Q3 residual correlations, dimensionality assessment with residual principal components analysis, person-item targeting plots, item category probability curves, and reliability using relative measurement uncertainty following Bignardi et al. (2025) <doi:10.31234/osf.io/h54k8_v1>.

Using variational techniques we address some epidemiological problems as the incidence curve decomposition by inverting the renewal equation as described in Alvarez et al. (2021) <doi:10.1073/pnas.2105112118> and Alvarez et al. (2022) <doi:10.3390/biology11040540> or the estimation of the functional relationship between epidemiological indicators. We also propose a learning method for the short time forecast of the trend incidence curve as described in Morel et al. (2022) <doi:10.1101/2022.11.05.22281904>.

Some EM-type algorithms to estimate parameters for the well-known Heckman selection model are provided in the package. Such algorithms are as follow: ECM(Expectation/Conditional Maximization), ECM(NR)(the Newton-Raphson method is adapted to the ECM) and ECME(Expectation/Conditional Maximization Either). Since the algorithms are based on the EM algorithm, they also have EMâ s main advantages, namely, stability and ease of implementation. Further details and explanations of the algorithms can be found in Zhao et al. (2020) <doi: 10.1016/j.csda.2020.106930>.

The main aim is to further facilitate the creation of exercises based on the package exams by Grün, B., and Zeileis, A. (2009) <doi:10.18637/jss.v029.i10>. Creating effective student exercises involves challenges such as creating appropriate data sets and ensuring access to intermediate values for accurate explanation of solutions. The functionality includes the generation of univariate and bivariate data including simple time series, functions for theoretical distributions and their approximation, statistical and mathematical calculations for tasks in basic statistics courses as well as general tasks such as string manipulation, LaTeX/HTML formatting and the editing of XML task files for Moodle'.

The EQ-5D is a widely-used standarized instrument for measuring Health Related Quality Of Life (HRQOL), developed by the EuroQol group <https://euroqol.org/>. It assesses five dimensions; mobility, self-care, usual activities, pain/discomfort, and anxiety/depression, using either a three-level (EQ-5D-3L) or five-level (EQ-5D-5L) scale. Scores from these dimensions are commonly converted into a single utility index using country-specific value sets, which are critical in clinical and economic evaluations of healthcare and in population health surveys. The eq5dsuite package enables users to calculate utility index values for the EQ-5D instruments, including crosswalk utilities using the original crosswalk developed by van Hout et al. (2012) <doi:10.1016/j.jval.2012.02.008> (mapping EQ-5D-5L responses to EQ-5D-3L index values), or the recently developed reverse crosswalk by van Hout et al. (2021) <doi:10.1016/j.jval.2021.03.009> (mapping EQ-5D-3L responses to EQ-5D-5L index values). Users are allowed to add and/or remove user-defined value sets. Additionally, the package provides tools to analyze EQ-5D data according to the recommended guidelines outlined in "Methods for Analyzing and Reporting EQ-5D data" by Devlin et al. (2020) <doi:10.1007/978-3-030-47622-9>.

Calculates conditional exact tests (Fisher's exact test, Blaker's exact test, or exact McNemar's test) and unconditional exact tests (including score-based tests on differences in proportions, ratios of proportions, and odds ratios, and Boshcloo's test) with appropriate matching confidence intervals, and provides power and sample size calculations. Gives melded confidence intervals for the binomial case (Fay, et al, 2015, <DOI:10.1111/biom.12231>). Gives boundary-optimized rejection region test (Gabriel, et al, 2018, <DOI:10.1002/sim.7579>), an unconditional exact test for the situation where the controls are all expected to fail. Gives confidence intervals compatible with exact McNemar's or sign tests (Fay and Lumbard, 2021, <DOI:10.1002/sim.8829>). For review of these kinds of exact tests see Fay and Hunsberger (2021, <DOI:10.1214/21-SS131>).

This package implements local influence diagnostics for the Extreme-Value Birnbaum-Saunders (EVBS) regression model: joint maximum likelihood estimation, conformal normal curvature diagnostics under three perturbation schemes (case-weight, response variable, and explanatory variable), randomized quantile residuals with simulation envelope, Monte Carlo simulation utilities, and publication-quality density and diagnostic plots. Version 1.1.0 adds the density, distribution and quantile functions, the finite upper endpoint, return levels and expected shortfall, block bootstrap standard errors for serially dependent series, local influence diagnostics for the generalized extreme-value regression model, and a GAMLSS family allowing the tail-shape parameter to depend on covariates. Version 1.2.0 adds a prospective control chart for endpoint identifiability. The methods are described in Ospina, Lima, Barros, and Macedo (2026, submitted) and are applied to monthly maximum wind gust data from Itajai, Brazil.

This package provides tools for generating hierarchical exploded-view maps from dense administrative boundary data. The package applies rigid-body translations to polygon geometries using a centroid-driven vector field, preserving the internal geometry of each feature while separating units within and across regions. Parameters can be derived analytically from dataset geometry using closed-form models for regional separation and local expansion. The package also includes grouped layouts, optional bounded collision refinement, and an interactive focus-map widget for selected-area inspection in htmlwidgets and Shiny'. It implements the methodology described in George Arthur (2026) <https://github.com/PrigasG/explodemap> "A Hierarchical Vector-Based Framework for Multi-Scale Exploded-View Cartography".