Curve Fitting of monotonic(sigmoidal) & non-monotonic(J-shaped) dose-response data. Predicting mixture toxicity based on reference models such as concentration addition', independent action', and generalized concentration addition'.

Three generalizations of the synthetic control method (which has already an implementation in package Synth') are implemented: first, MSCMT allows for using multiple outcome variables, second, time series can be supplied as economic predictors, and third, a well-defined cross-validation approach can be used. Much effort has been taken to make the implementation as stable as possible (including edge cases) without losing computational efficiency. A detailed description of the main algorithms is given in Becker and Klöà ner (2018) <doi:10.1016/j.ecosta.2017.08.002>.

This package provides a complement to all editions of *Modern Data Science with R* (ISBN: 978-0367191498, publisher URL: <https://www.routledge.com/Modern-Data-Science-with-R/Baumer-Kaplan-Horton/p/book/9780367191498>). This package contains data and code to complete exercises and reproduce examples from the text. It also facilitates connections to the SQL database server used in the book. All editions of the book are supported by this package.

Many tools for making, manipulating, and modifying many different types of networks. All functions operate with matrices, edge lists, and igraph', network', and tidygraph objects, on directed, multiplex, multimodal, signed, and other networks. The package includes functions for importing and exporting, creating and generating networks, modifying networks and node and tie attributes, and describing networks with sensible defaults.

Automatically segments a 3D array that represents a volume of binary voxels into mutually exclusive morphological elements. This package extends existing work for segmenting 2D binary raster data. A paper documenting this approach has been published in the journal Landscape Ecology: Remmel, T.K. (2022) <doi:10.1007/s10980-021-01384-7>. The output is a cartridge (list object) that maintains the input array, the segmentation results in array format, and a summary table. Plotting functionality is provided to produce interactive visual outputs from the produced results cartridge, allowing custom plotting to be performed separately from the segmentation, which speeds-up processing. While the old functions persist, they are being phased out and will eventually be replaced with the new runmorph3d() and plotmorph3d() functions.

This package provides install functions of other languages such as java', python'.

Several multivariate techniques from a biplot perspective. It is the translation (with many improvements) into R of the previous package developed in Matlab'. The package contains some of the main developments of my team during the last 30 years together with some more standard techniques. Package includes: Classical Biplots, HJ-Biplot, Canonical Biplots, MANOVA Biplots, Correspondence Analysis, Canonical Correspondence Analysis, Canonical STATIS-ACT, Logistic Biplots for binary and ordinal data, Multidimensional Unfolding, External Biplots for Principal Coordinates Analysis or Multidimensional Scaling, among many others. References can be found in the help of each procedure.

This package provides a multiple-response chi-square framework for the analysis of contingency tables arising from multiple-response questionnaires, such as check-all-that-apply tasks, where response options are crossed with a known grouping factor. The framework accommodates within-block (e.g., within-subject) designs, as commonly encountered in sensory evaluation. It comprises a multiple-response chi-square test of homogeneity with an associated dimensionality test, a multiple-response Correspondence Analysis (CA), and per-cell multiple-response hypergeometric tests. These methods extend their classical counterparts by grounding inference in a null model that properly accounts for the multiple-response nature of the data, treating evaluations, rather than individual citations, as the experimental units, yielding more statistically valid conclusions than standard contingency table analyses. Details may be found in Mahieu, Schlich, Visalli, and Cardot (2021). <doi:10.1016/j.foodqual.2021.104256>.

Maximum likelihood estimation for series systems where the component cause of failure is masked. Implements analytical log-likelihood, score, and Hessian functions for exponential, homogeneous Weibull, and heterogeneous Weibull component lifetimes under masked cause conditions (C1, C2, C3). Supports exact, right-censored, left-censored, and interval-censored observations via composable observation functors. Provides random data generation, model fitting, and Fisher information for asymptotic inference. See Lin, Loh, and Bai (1993) <doi:10.1109/24.257799> and Craiu and Reiser (2006) <doi:10.1111/j.1541-0420.2005.00498.x>.

This package provides a minimal library specifically designed to make the estimation of Machine Learning (ML) techniques as easy and accessible as possible, particularly within the framework of the Knowledge Discovery in Databases (KDD) process in data mining. The package provides essential tools to structure and execute each stage of a predictive or classification modeling workflow, aligning closely with the fundamental steps of the KDD methodology, from data selection and preparation, through model building and tuning, to the interpretation and evaluation of results using Sensitivity Analysis. The MLwrap workflow is organized into four core steps; preprocessing(), build_model(), fine_tuning(), and sensitivity_analysis(). It also includes global and pairwise interaction analysis based on Friedmanâ s H-statistic to support a more detailed interpretation of complex feature relationships.These steps correspond, respectively, to data preparation and transformation, model construction, hyperparameter optimization, and sensitivity analysis. The user can access comprehensive model evaluation results including fit assessment metrics, plots, predictions, and performance diagnostics for ML models implemented through Neural Networks', Random Forest', XGBoost (Extreme Gradient Boosting), and Support Vector Machines (SVM) algorithms. By streamlining these phases, MLwrap aims to simplify the implementation of ML techniques, allowing analysts and data scientists to focus on extracting actionable insights and meaningful patterns from large datasets, in line with the objectives of the KDD process.

This package provides a unified and consistent S3 interface for training and predicting with a variety of machine learning models in R. The package wraps popular algorithms (e.g., from glmnet', lightgbm', ranger', e1071', and caret') under a common workflow based on simple wrap_*() and predict() functions, allowing users to switch between models without changing their code structure. It supports both classification and regression tasks and facilitates rapid experimentation, benchmarking, and comparison of models. By abstracting away package-specific APIs while preserving flexibility in parameter specification, the package streamlines machine learning workflows and promotes reproducibility.

Process OpenPose human body keypoints for computer vision, including data structuring and user-defined linear transformations for standardization. It optionally, includes metadata extraction from filenames in the UCLA NewsScape archive.

Implementation of Matched Wake Analysis (mwa) for studying causal relationships in spatiotemporal event data, introduced by Schutte and Donnay (2014) <doi:10.1016/j.polgeo.2014.03.001>.

This package provides a simple way to construct and maintain functions that keep state i.e. remember their argument lists. This can be useful when one needs to repeatedly invoke the same function with only a small number of argument changes at each invocation.

This package provides density, distribution, quantile, and random generation functions for the Modified Half-Normal (MHN) distribution, along with moments and mode; the Fox-Wright Psi normalizing constant is evaluated internally. The MHN distribution arises as a conditional posterior in Bayesian Markov chain Monte Carlo and generalizes the half-normal, truncated normal, and square-root gamma distributions. Implements efficient sampling via the Sun, Kong & Pal (2023) <doi:10.1080/03610926.2021.1934700> algorithms and the Gao & Wang (2025) <doi:10.1080/03610918.2025.2524551> relaxed transformed density rejection method.

This package provides functions for the robust estimation of parametric families of copulas using minimization of the Maximum Mean Discrepancy, following the article Alquier, Chérief-Abdellatif, Derumigny and Fermanian (2022) <doi:10.1080/01621459.2021.2024836>.

Constructs mixed-level and regular fractional factorial designs using coordinate-exchange optimization and automatic generator search. Design quality is evaluated with J2 and balance (H-hat) criteria, alias structures are computed via correlation-based chaining, and deterministic trend-free run orders can be produced following Coster (1993) <doi:10.1214/aos/1176349410>. Mixed-level design construction follows the NONBPA approach of Pantoja-Pacheco et al. (2021) <doi:10.3390/math9131455>. Regular fraction identification follows Guo, Simpson and Pignatiello (2007) <doi:10.1080/00224065.2007.11917691>. Alias structure computation follows Rios-Lira et al.(2021) <doi:10.3390/math9233053>.

The midasml package implements estimation and prediction methods for high-dimensional mixed-frequency (MIDAS) time-series and panel data regression models. The regularized MIDAS models are estimated using orthogonal (e.g. Legendre) polynomials and sparse-group LASSO (sg-LASSO) estimator. For more information on the midasml approach see Babii, Ghysels, and Striaukas (2021, JBES forthcoming) <doi:10.1080/07350015.2021.1899933>. The package is equipped with the fast implementation of the sg-LASSO estimator by means of proximal block coordinate descent. High-dimensional mixed frequency time-series data can also be easily manipulated with functions provided in the package.

Supports visual interpretation of hierarchical composite endpoints (HCEs). HCEs are complex constructs used as primary endpoints in clinical trials, combining outcomes of different types into ordinal endpoints, in which each patient contributes the most clinically important event (one and only one) to the analysis. See Karpefors M et al. (2022) <doi:10.1177/17407745221134949>.

This package provides functions and datasets used in the book: Fernandez-Casal, R., Costa, J. and Oviedo-de la Fuente, M. (2024) "Metodos predictivos de aprendizaje estadistico" <https://rubenfcasal.github.io/aprendizaje_estadistico/>.

Use standard genomics file format (BED) and a table of orthologs to illustrate synteny conservation at the genome-wide scale. Significantly conserved linkage groups are identified as described in Simakov et al. (2020) <doi:10.1038/s41559-020-1156-z> and displayed on an Oxford Grid (Edwards (1991) <doi:10.1111/j.1469-1809.1991.tb00394.x>) or a chord diagram as in Simakov et al. (2022) <doi:10.1126/sciadv.abi5884>. The package provides a function that uses a network-based greedy algorithm to find communities (Clauset et al. (2004) <doi:10.1103/PhysRevE.70.066111>) and so automatically order the chromosomes on the plot to improve interpretability.

This package provides various functions for parameter estimation of one-dimensional stable distributions and their mixtures. It implements a diverse set of estimation methods, including quantile-based approaches, regression methods based on the empirical characteristic function (empirical, kernel, and recursive), and maximum likelihood estimation. For mixture models, it provides stochastic expectationâ maximization (SEM) algorithms and Bayesian estimation methods using sampling and importance sampling to overcome the long burn-in period of Markov Chain Monte Carlo (MCMC) strategies. The package also includes tools and statistical tests for analyzing whether a dataset follows a stable distribution. Some of the implemented methods are described in Hajjaji, O., Manou-Abi, S. M., and Slaoui, Y. (2024) <doi:10.1080/02664763.2024.2434627>.

Microbial growth is often measured by growth curves i.e. a table of population sizes and times of measurements. This package allows to use such growth curve data to determine the duration of "microbial lag phase" i.e. the time needed for microbes to restart divisions. It implements the most commonly used methods to calculate the lag duration, these methods are discussed and described in Opalek et.al. 2022. Citation: Smug, B. J., Opalek, M., Necki, M., & Wloch-Salamon, D. (2024). Microbial lag calculator: A shiny-based application and an R package for calculating the duration of microbial lag phase. Methods in Ecology and Evolution, 15, 301â 307 <doi:10.1111/2041-210X.14269>.

Package for processing downloaded MODIS Surface reflectance Product HDF files. Specifically, MOD09 surface reflectance product files, and the associated MOD03 geolocation files (for MODIS-TERRA). The package will be most effective if the user installs MRTSwath (MODIS Reprojection Tool for swath products; <https://lpdaac.usgs.gov/tools/modis_reprojection_tool_swath>, and adds the directory with the MRTSwath executable to the default R PATH by editing ~/.Rprofile.