This package provides tools for Bayesian parameter estimation of adsorption isotherm models using Markov Chain Monte Carlo (MCMC) methods. This package enables users to fit non-linear and linear adsorption isotherm modelsâ Freundlich, Langmuir, and Temkinâ within a probabilistic framework, capturing uncertainty and parameter correlations. It provides posterior summaries, 95% credible intervals, convergence diagnostics (Gelman-Rubin), and visualizations through trace and density plots. With this R package, researchers can rigorously analyze adsorption behavior in environmental and chemical systems using robust Bayesian inference. For more details, see Gilks et al. (1995) <doi:10.1201/b14835>, and Gamerman & Lopes (2006) <doi:10.1201/9781482296426>.

Filters animal satellite tracking data obtained from the Argos system(<https://www.argos-system.org/>), following the algorithm described in Freitas et al (2008) <doi:10.1111/j.1748-7692.2007.00180.x>. It is especially indicated for telemetry studies of marine animals, where Argos locations are predominantly of low-quality.

Extraction, preparation, visualisation and analysis of TERN AusPlots ecosystem monitoring data. Direct access to plot-based data on vegetation and soils across Australia, including physical sample barcode numbers. Simple function calls extract the data and merge them into species occurrence matrices for downstream analysis, or calculate things like basal area and fractional cover. TERN AusPlots is a national field plot-based ecosystem surveillance monitoring method and dataset for Australia. The data have been collected across a national network of plots and transects by the Terrestrial Ecosystem Research Network (TERN - <https://www.tern.org.au>), an Australian Government NCRIS-enabled project, and its Ecosystem Surveillance platform (<https://www.tern.org.au/tern-land-observatory/ecosystem-surveillance-and-environmental-monitoring/>).

Colour palettes and a ggplot2 theme to follow the UK Government Analysis Function best practice guidance for producing data visualisations, available at <https://analysisfunction.civilservice.gov.uk/policy-store/data-visualisation-charts/>. Includes continuous and discrete colour and fill scales, as well as a ggplot2 theme.

This package provides tools for quantitative analysis related to archaeological and historical problems for irregularly spaced time indexed observations, toward evaluating linear dependence and homogeneity over time. Methods include effect sizes for measuring homogeneity, simulation from a truncated Poisson distribution for random right-censoring of count data, and least-squares spectral analysis by lowest frequency iteration for model fitting. Collins-Elliott (2026) <https://volweb.utk.edu/~scolli46/sce_aqysuppl2026.pdf>.

This package provides automated machine learning workflows for survival analysis, binary classification, continuous outcomes, and ordinal outcomes. The package trains and combines model variants across user-supplied multi-cohort data, evaluates survival models by leave-one-out cross-validation using Harrell's concordance index, binary models by leave-one-out cross-validation using receiver operating characteristic area under the curve, continuous models by out-of-fold root mean squared error and R-squared, and ordinal models by out-of-fold quadratic weighted kappa. It renders reproducible reports in Hypertext Markup Language (HTML) with figures and diagnostics. The survival workflow supports penalized and tree-based Cox proportional hazards models, stepwise Cox models, partial least squares regression for Cox models, supervised principal components, gradient boosting machine Cox models, survival support vector machines (survival-SVM), random survival forests, and optional CoxBoost'. The binary workflow supports penalized logistic regression, logistic baselines, gradient boosting machines, random forests, principal component analysis (PCA) logistic regression, and Gaussian naive Bayes variants. Continuous and ordinal workflows reuse an 18-variant regression registry with penalized, linear, boosted, forest, PCA, and baseline families. The optional CoxBoost model is enabled when the suggested CoxBoost package is installed; it is used conditionally and is not a strong dependency. Optional model backends are checked at run time so missing backend packages skip only the affected model variants rather than blocking installation of the whole package. Methods build on Friedman et al. (2010) <doi:10.18637/jss.v033.i01>, Bair and Tibshirani (2004) <doi:10.1371/journal.pbio.0020108>, Ishwaran et al. (2008) <doi:10.1214/08-AOAS169>, Blanche et al. (2013) <doi:10.1002/sim.5958>, and Binder and Schumacher (2008) <doi:10.1186/1471-2105-9-14>.

Evaluates land suitability for different crops production. The package is based on the Food and Agriculture Organization (FAO) and the International Rice Research Institute (IRRI) methodology for land evaluation. Development of ALUES is inspired by similar tool for land evaluation, Land Use Suitability Evaluation Tool (LUSET). The package uses fuzzy logic approach to evaluate land suitability of a particular area based on inputs such as rainfall, temperature, topography, and soil properties. The membership functions used for fuzzy modeling are the following: Triangular, Trapezoidal and Gaussian. The methods for computing the overall suitability of a particular area are also included, and these are the Minimum, Maximum and Average. Finally, ALUES is a highly optimized library with core algorithms written in C++.

This package provides a variable selection method using B-Splines in multivariate nOnparametric Regression models Based on partial dErivatives Regularization (ABSORBER) implements a novel variable selection method in a nonlinear multivariate model using B-splines. For further details we refer the reader to the paper Savino, M. E. and Lévy-Leduc, C. (2024), <https://hal.science/hal-04434820>.

An application for analysis of Adverse Events, as described in Chen, et al., (2023) <doi:10.3390/cancers15092521>. The required data for the application includes demographics, follow up, adverse event, drug administration and optional tumor measurement data. The app can produce swimmers plots of adverse events, Kaplan-Meier plots and Cox Proportional Hazards model results for the association of adverse event biomarkers and overall survival and progression free survival. The adverse event biomarkers include occurrence of grade 3, low grade (1-2), and treatment related adverse events. Plots and tables of results are downloadable.

Estimate the linear and nonlinear autoregressive distributed lag (ARDL & NARDL) models and the corresponding error correction models, and test for longrun and short-run asymmetric. The general-to-specific approach is also available in estimating the ARDL and NARDL models. The Pesaran, Shin & Smith (2001) (<doi:10.1002/jae.616>) bounds test for level relationships is also provided. The ardl.nardl package also performs short-run and longrun symmetric restrictions available at Shin et al. (2014) <doi:10.1007/978-1-4899-8008-3_9> and their corresponding tests.

Create data that displays generative art when mapped into a ggplot2 plot. Functionality includes specialized data frame creation for geometric shapes, tools that define artistic color palettes, tools for geometrically transforming data, and other miscellaneous tools that are helpful when using ggplot2 for generative art.

Graphical functionalities for the representation of multivariate data. It is a complete re-implementation of the functions available in the ade4 package.

This package provides functions for Accurate and Speedy linkage map construction, manipulation and diagnosis of Doubled Haploid, Backcross and Recombinant Inbred R/qtl objects. This includes extremely fast linkage map clustering and optimal marker ordering using MSTmap (see Wu et al.,2008).

This package provides a stacking solution for modeling imbalanced and severely skewed data. It automates the process of building homogeneous or heterogeneous stacked ensemble models by selecting "best" models according to different criteria. In doing so, it strategically searches for and selects diverse, high-performing base-learners to construct ensemble models optimized for skewed data. This package is particularly useful for addressing class imbalance in datasets, ensuring robust and effective model outcomes through advanced ensemble strategies which aim to stabilize the model, reduce its overfitting, and further improve its generalizability.

This package creates all leave-one-out models and produces predictions for test samples.

Covers several areas of data processing: batch-splitting, reading and writing of large data files, data tiling, one-hot encoding and decoding of data tiles, stratified proportional (random or probabilistic) data sampling, data normalization and thresholding, substring location and commonalities inside strings, and location and tabulation of amino acids, modifications or associated monoisotopic masses inside modified peptides. The extractor implements code from Matrix.utils', Varrichio C (2020), <https://cran.r-project.org/package=Matrix.utils>.

This package contains various functions for optimal scaling. One function performs optimal scaling by maximizing an aspect (i.e. a target function such as the sum of eigenvalues, sum of squared correlations, squared multiple correlations, etc.) of the corresponding correlation matrix. Another function performs implements the LINEALS approach for optimal scaling by minimization of an aspect based on pairwise correlations and correlation ratios. The resulting correlation matrix and category scores can be used for further multivariate methods such as structural equation models.

Computation of A (pedigree), G (genomic-base), and H (A corrected by G) relationship matrices for diploid and autopolyploid species. Several methods are implemented considering additive and non-additive models.

Compute a tree level hierarchy, judgment matrix, consistency index and ratio, priority vectors, hierarchic synthesis and rank. Based on the book entitled "Models, Methods, Concepts and Applications of the Analytic Hierarchy Process" by Saaty and Vargas (2012, ISBN 978-1-4614-3597-6).

This package provides a very fast and robust interface to ArcGIS Geocoding Services'. Provides capabilities for reverse geocoding, finding address candidates, character-by-character search autosuggestion, and batch geocoding. The public ArcGIS World Geocoder is accessible for free use via arcgisgeocode for all services except batch geocoding. arcgisgeocode also integrates with arcgisutils to provide access to custom locators or private ArcGIS World Geocoder hosted on ArcGIS Enterprise'. Learn more in the Geocode service API reference <https://developers.arcgis.com/rest/geocode/api-reference/overview-world-geocoding-service.htm>.

An iterative implementation of a recursive binary partitioning algorithm to measure pairwise dependence with a modular design that allows user specification of the splitting logic and stop criteria. Helper functions provide suggested versions of both and support visualization and the computation of summary statistics on final binnings. For a thorough discussion and demonstration of the algorithm, see Salahub and Oldford (2025) <doi:10.1002/sam.70042>.

Check if a given package name is available to use. It checks the name's validity. Checks if it is used on GitHub', CRAN and Bioconductor'. Checks for unintended meanings by querying Wiktionary and Wikipedia.

Automatically generate a changelog file (NEWS.md / CHANGELOG.md) from the git history using conventional commit messages (<https://www.conventionalcommits.org/en/v1.0.0/>).

The goal of amp.sim is to transform NONMEM models into R syntax so they can be used for simulations using the deSolve', nlmixr2 or mrgsolve package. Additionally, functionality is included to aid simulations performed directly in NONMEM and to automatically create shiny apps for simulation models.