This package provides a scripting and command-line front-end is provided by r (aka littler) as a lightweight binary wrapper around the GNU R language and environment for statistical computing and graphics. While R can be used in batch mode, the r binary adds full support for both shebang-style scripting (i.e. using a hash-mark-exclamation-path expression as the first line in scripts) as well as command-line use in standard pipelines. In other words, r provides the R language without the environment.

Simplifies the application of various energy expenditure models. The package is intended as a hub that brings together methods from a variety of other, themed packages such as Sojourn and TwoRegression'. Several methods are supported locally as well, including the linear methods of Hildebrand et al. (2014) <doi:10.1249/MSS.0000000000000289> and the non-linear adaptation by Ellingson et al. (2017) <doi:10.1088/1361-6579/aa6d00>. The package can combine output from different methods and produce standardized output in a range of units.

Average population attributable fractions are calculated for a set of risk factors (either binary or ordinal valued) for both prospective and case- control designs. Confidence intervals are found by Monte Carlo simulation. The method can be applied to either prospective or case control designs, provided an estimate of disease prevalence is provided. In addition to an exact calculation of AF, an approximate calculation, based on randomly sampling permutations has been implemented to ensure the calculation is computationally tractable when the number of risk factors is large.

This package implements Bayesian hierarchical models with flexible Gaussian process priors, focusing on Extended Latent Gaussian Models and incorporating various Gaussian process priors for Bayesian smoothing. Computations leverage finite element approximations and adaptive quadrature for efficient inference. Methods are detailed in Zhang, Stringer, Brown, and Stafford (2023) <doi:10.1177/09622802221134172>; Zhang, Stringer, Brown, and Stafford (2024) <doi:10.1080/10618600.2023.2289532>; Zhang, Brown, and Stafford (2023) <doi:10.48550/arXiv.2305.09914>; and Stringer, Brown, and Stafford (2021) <doi:10.1111/biom.13329>.

This package provides tools for estimation and clustering of spherical data, seamlessly integrated with the flexmix package. Includes the necessary M-step implementations for both Poisson Kernel-Based Distribution (PKBD) and spherical Cauchy distribution. Additionally, the package provides random number generators for PKBD and spherical Cauchy distribution. Methods are based on Golzy M., Markatou M. (2020) <doi:10.1080/10618600.2020.1740713>, Kato S., McCullagh P. (2020) <doi:10.3150/20-bej1222> and Sablica L., Hornik K., Leydold J. (2023) <doi:10.1214/23-ejs2149>.

Converts engineering DWG drawings ('AutoCAD 2013+ format) to GeoJSON entirely in-process, with no CAD software, LibreDWG', or GDAL required. Wraps the pure-Rust dwg2geo conversion core <https://github.com/milkway/dwg2geo>: every feature carries resolved CAD style metadata (layer, colour, line weight, text), skipped and failed entities are reported with reasons, and the output is deterministic: the same bytes always produce byte-identical GeoJSON'. Coordinates are kept in the drawing's local system; the package never guesses a coordinate reference system.

Support in preparing a raw ESM dataset for statistical analysis. Preparation includes the handling of errors (mostly due to technological reasons) and the generating of new variables that are necessary and/or helpful in meeting the conditions when statistically analyzing ESM data. The functions in esmprep are meant to hierarchically lead from bottom, i.e. the raw (separated) ESM dataset(s), to top, i.e. a single ESM dataset ready for statistical analysis. This hierarchy evolved out of my personal experience in working with ESM data.

Calculates additive and dominance genetic relationship matrices and their inverses, in matrix and tabular-sparse formats. It includes functions for checking and processing pedigree, calculating inbreeding coefficients (Meuwissen & Luo, 1992 <doi:10.1186/1297-9686-24-4-305>), as well as functions to calculate the matrix of genetic group contributions (Q), and adding those contributions to the genetic merit of animals (Quaas (1988) <doi:10.3168/jds.S0022-0302(88)79691-5>). Calculation of Q is computationally extensive. There are computationally optimized functions to calculate Q.

This package contains an engine for spatially-explicit eco-evolutionary mechanistic models with a modular implementation and several support functions. It allows exploring the consequences of ecological and macroevolutionary processes across realistic or theoretical spatio-temporal landscapes on biodiversity patterns as a general term. Reference: Oskar Hagen, Benjamin Flueck, Fabian Fopp, Juliano S. Cabral, Florian Hartig, Mikael Pontarp, Thiago F. Rangel, Loic Pellissier (2021) "gen3sis: A general engine for eco-evolutionary simulations of the processes that shape Earth's biodiversity" <doi:10.1371/journal.pbio.3001340>.

The algorithm of semi-supervised learning is based on finite Gaussian mixture models and includes a mechanism for handling missing data. It aims to fit a g-class Gaussian mixture model using maximum likelihood. The algorithm treats the labels of unclassified features as missing data, building on the framework introduced by Rubin (1976) <doi:10.2307/2335739> for missing data analysis. By taking into account the dependencies in the missing pattern, the algorithm provides more information for determining the optimal classifier, as specified by Bayes rule.

This package provides HE plot and other functions for visualizing hypothesis tests in multivariate linear models. HE plots represent sums-of-squares-and-products matrices for linear hypotheses and for error using ellipses (in two dimensions) and ellipsoids (in three dimensions). It also provides other tools for analysis and graphical display of the models such as robust methods and homogeneity of variance covariance matrices. The related candisc package provides visualizations in a reduced-rank canonical discriminant space when there are more than a few response variables.

This package implements information-theoretic measures to explore variable interactions, including KSG mutual information estimation for continuous variables from Kraskov et al. (2004) <doi:10.1103/PhysRevE.69.066138>, knockoff conditional mutual information described in Zhang & Chen (2025) <doi:10.1126/sciadv.adu6464>, synergistic-unique-redundant decomposition introduced by Martinez-Sanchez et al. (2024) <doi:10.1038/s41467-024-53373-4>, and information imbalance gain following Del Tatto et al. (2024) <doi:10.1073/pnas.2317256121>, allowing detection of complex and diverse relationships among variables.

This package provides a compilation of functions to create visually appealing and information-rich plots of meta-analytic data using ggplot2'. Provides functions to create forest plots, funnel plots, and many of their variants, including rainforest plots, thick forest plots, additional evidence contour funnel plots, and sunset funnel plots. In addition, functionalities for visual inference with funnel plots in the context of meta-analysis are provided. Further functionalities include plots for comparing fixed-effect and random-effects models and dedicated visualizations for three-level meta-analysis.

Quantifies ecological memory in long time-series using Random Forest models ('Benito', Gil-Romera', and Birks 2019 <doi:10.1111/ecog.04772>) fitted with ranger (Wright and Ziegler 2017 <doi:10.18637/jss.v077.i01>). Ecological memory is assessed by modeling a response variable as a function of lagged predictors, distinguishing endogenous memory (lagged response) from exogenous memory (lagged environmental drivers). Designed for palaeoecological datasets and simulated pollen curves from virtualPollen', but applicable to any long time-series with environmental drivers and a biotic response.

This package implements a method that builds the coefficients of a polynomial model that performs almost equivalently as a given neural network (densely connected). This is achieved using Taylor expansion at the activation functions. The obtained polynomial coefficients can be used to explain features (and their interactions) importance in the neural network, therefore working as a tool for interpretability or eXplainable Artificial Intelligence (XAI). See Morala et al. 2021 <doi:10.1016/j.neunet.2021.04.036>, and 2023 <doi:10.1109/TNNLS.2023.3330328>.

This package implements the Network meta-Analytic Predictive (NAP) prior framework to accommodate changes in the standard of care (SoC) during ongoing randomized controlled trials (RCTs). The method synthesizes pre- and post-change in-trial data by leveraging external evidence, particularly head-to-head trials comparing the original and new standards of care, to bridge the two evidence periods and enable principled borrowing. The package provides utilities to construct NAP-based priors and perform Bayesian inference for time-to-event endpoints using summarized trial evidence.

Estimation of two- and three-way dynamic panel threshold regression models (Di Lascio and Perazzini (2024) <https://repec.unibz.it/bemps104.pdf>; Di Lascio and Perazzini (2022, ISBN:978-88-9193-231-0); Seo and Shin (2016) <doi:10.1016/j.jeconom.2016.03.005>) through the generalized method of moments based on the first difference transformation and the use of instrumental variables. The models can be used to find a change point detection in the time series. In addition, random number generation is also implemented.

Deduplicates datasets by retaining the most complete and informative records. Identifies duplicated entries based on a specified key column, calculates completeness scores for each row, and compares values within groups. When differences between duplicates exceed a user-defined threshold, records are split into unique IDs; otherwise, they are coalesced into a single, most complete entry. Returns a list containing the original duplicates, the split entries, and the final coalesced dataset. Useful for cleaning survey or administrative data where duplicated IDs may reflect minor data entry inconsistencies.

This package provides scaffolding tools to help researchers prepare and submit computational jobs to high-throughput computing (HTC) schedulers. Generates the files required to run containerized R analyses on HTCondor', including submit files and executable scripts, and wraps the system commands needed to stage files, submit jobs, monitor status, and retrieve results from a CHTC submit node. Provides htc_config() for managing connection details and SSH connection reuse guidance. Works naturally alongside containr for container image management and toolero for dataset splitting and project scaffolding.

This package implements Transmission Channel Analysis (TCA) for structural vector autoregressive (SVAR) models following the methodology of Wegner, Lieb, Smeekes and Wilms (2025) <doi:10.48550/arXiv.2405.18987>. TCA decomposes impulse response functions (IRFs) into contributions from distinct transmission channels using a systems form representation and directed acyclic graph (DAG) path analysis. Supports overlapping channels, exhaustive 3-way and 4-way decompositions via inclusion-exclusion principle. This is a parallel R implementation of the tca-matlab-toolbox (<https://github.com/enweg/tca-matlab-toolbox>).

This package provides tools to convert from specific formats to more general forms of spatial data. Using tables to store the actual entities present in spatial data provides flexibility, and the functions here deliberately minimize the level of interpretation applied, leaving that for specific applications. Includes support for simple features, round-trip for Spatial classes and long-form tables, analogous to ggplot2::fortify'. There is also a more normal form representation that decomposes simple features and their kin to tables of objects, parts, and unique coordinates.

Lexicons and tools to perform sentiment analysis on Italian texts. Lexicons included: Sentix 3.0, MAL, ElIta VAD and basic emotions (Plutchik's wheel of emotions). For more details about the lexicons, see Basile & Nissim (2013), "Sentiment Analysis on Italian Tweets", <https://aclanthology.org/W13-1614/>; Vassallo et al. (2019), "The Tenuousness of Lemmatization in Lexicon-based Sentiment Analysis", <https://aclanthology.org/2019.clicit-1.79/>; Di Palma (2024), "ELIta: A New Italian Language Resource for Emotion Analysis", <https://aclanthology.org/2024.clicit-1.36/>.

Determine sample sizes, draw samples, and conduct data analysis using data frames. It specifically enables you to determine simple random sample sizes, stratified sample sizes, and complex stratified sample sizes using a secondary variable such as population; draw simple random samples and stratified random samples from sampling data frames; determine which observations are missing from a random sample, missing by strata, duplicated within a dataset; and perform data analysis, including proportions, margins of error and upper and lower bounds for simple, stratified and cluster sample designs.

Calculates a Satorra-Bentler scaled chi-squared difference test between nested models that were estimated using maximum likelihood (ML) with robust standard errors, which cannot be calculated the traditional way. For details see Satorra & Bentler (2001) <doi:10.1007/bf02296192> and Satorra & Bentler (2010) <doi:10.1007/s11336-009-9135-y>. This package may be particularly helpful when used in conjunction with Mplus software, specifically when implementing the complex survey option. In such cases, the model estimator in Mplus defaults to ML with robust standard errors.