Meta-package for statistical and machine learning with a unified interface for model fitting, prediction, performance assessment, and presentation of results. Approaches for model fitting and prediction of numerical, categorical, or censored time-to-event outcomes include traditional regression models, regularization methods, tree-based methods, support vector machines, neural networks, ensembles, data preprocessing, filtering, and model tuning and selection. Performance metrics are provided for model assessment and can be estimated with independent test sets, split sampling, cross-validation, or bootstrap resampling. Resample estimation can be executed in parallel for faster processing and nested in cases of model tuning and selection. Modeling results can be summarized with descriptive statistics; calibration curves; variable importance; partial dependence plots; confusion matrices; and ROC, lift, and other performance curves.

Lightweight utilities for nucleic acid melting curve analysis are important in life sciences and diagnostics. This software can be used for the analysis and presentation of melting curve data from microbead-based assays (surface melting curve analysis) and reactions in solution (e.g., quantitative PCR (qPCR), real-time isothermal Amplification). Further information are described in detail in two publications in The R Journal [ <https://journal.r-project.org/archive/2013-2/roediger-bohm-schimke.pdf>; <https://journal.r-project.org/archive/2015-1/RJ-2015-1.pdf>].

An implementation of the iterative proportional fitting (IPFP), maximum likelihood, minimum chi-square and weighted least squares procedures for updating a N-dimensional array with respect to given target marginal distributions (which, in turn can be multidimensional). The package also provides an application of the IPFP to simulate multivariate Bernoulli distributions.

This package provides a comprehensive set of tools for working with order statistics, including functions for simulating order statistics, censored samples (Type I and Type II), and record values from various continuous distributions. Additionally, it offers functions to compute moments (mean, variance, skewness, kurtosis) of order statistics for several continuous distributions. These tools assist researchers and statisticians in understanding and analyzing the properties of order statistics and related data. The methods and algorithms implemented in this package are based on several published works, including Ahsanullah et al (2013, ISBN:9789491216831), Arnold and Balakrishnan (2012, ISBN:1461236444), Harter and Balakrishnan (1996, ISBN:9780849394522), Balakrishnan and Sandhu (1995) <doi:10.1080/00031305.1995.10476150>, Genç (2012) <doi:10.1007/s00362-010-0320-y>, Makouei et al (2021) <doi:10.1016/j.cam.2021.113386> and Nagaraja (2013) <doi:10.1016/j.spl.2013.06.028>.

Scrapes and tidies publicly available data from the Women's Tennis Association website (<https://www.wtatennis.com>). Provides helpers to retrieve player biographies, singles and doubles career overviews, match histories, live rankings and aggregate statistics. Dynamic pages are rendered through a headless Chrome session so JavaScript'-generated content is fully captured, and all outputs are returned as tidy data frames suitable for downstream analysis or visualisation.

This package provides a tidy workflow for landscape-scale analysis. multilandr offers tools to generate landscapes at multiple spatial scales and compute landscape metrics, primarily using the landscapemetrics package. It also features utility functions for plotting and analyzing multi-scale landscapes, exploring correlations between metrics, filtering landscapes based on specific conditions, generating landscape gradients for a given metric, and preparing datasets for further statistical analysis. Documentation about multilandr is provided in an introductory vignette included in this package and in the paper by Huais (2024) <doi:10.1007/s10980-024-01930-z>; see citation("multilandr") for details.

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.

This package provides a hybrid modeling framework combining Support Vector Regression (SVR) with metaheuristic optimization algorithms, including the Archimedes Optimization Algorithm (AO) (Hashim et al. (2021) <doi:10.1007/s10489-020-01893-z>), Coot Bird Optimization (CBO) (Naruei & Keynia (2021) <doi:10.1016/j.eswa.2021.115352>), and their hybrid (AOCBO), as well as several others such as Harris Hawks Optimization (HHO) (Heidari et al. (2019) <doi:10.1016/j.future.2019.02.028>), Gray Wolf Optimizer (GWO) (Mirjalili et al. (2014) <doi:10.1016/j.advengsoft.2013.12.007>), Ant Lion Optimization (ALO) (Mirjalili (2015) <doi:10.1016/j.advengsoft.2015.01.010>), and Enhanced Harris Hawk Optimization with Coot Bird Optimization (EHHOCBO) (Cui et al. (2023) <doi:10.32604/cmes.2023.026019>). The package enables automatic tuning of SVR hyperparameters (cost, gamma, and epsilon) to enhance prediction performance. Suitable for regression tasks in domains such as renewable energy forecasting and hourly data prediction. For more details about implementation and parameter bounds see: Setiawan et al. (2021) <doi:10.1016/j.procs.2020.12.003> and Liu et al. (2018) <doi:10.1155/2018/6076475>.

Translate R code into MongoDB aggregation pipelines.

Multiple moderation analysis for two-instance repeated measures designs, with up to three simultaneous moderators (dichotomous and/or continuous) with additive or multiplicative relationship. Includes analyses of simple slopes and conditional effects at (automatically determined or manually set) values of the moderator(s), as well as an implementation of the Johnson-Neyman procedure for determining regions of significance in single moderator models. Based on Montoya, A. K. (2018) "Moderation analysis in two-instance repeated measures designs: Probing methods and multiple moderator models" <doi:10.3758/s13428-018-1088-6> .

Maximum likelihood estimates are obtained via an EM algorithm with either a first-order or a fully exponential Laplace approximation as documented by Broatch and Karl (2018) <doi:10.48550/arXiv.1710.05284>, Karl, Yang, and Lohr (2014) <doi:10.1016/j.csda.2013.11.019>, and by Karl (2012) <doi:10.1515/1559-0410.1471>. Karl and Zimmerman <doi:10.1016/j.jspi.2020.06.004> use this package to illustrate how the home field effect estimator from a mixed model can be biased under nonrandom scheduling.

This package provides methods for fitting mixture distributions to univariate data using expectation maximization, HWHM and other methods. Supports Gaussian, Cauchy, Student's t, skew-normal and von Mises mixtures. For more details see Merkys (2018) <https://www.lvb.lt/permalink/370LABT_NETWORK/1m6ui06/alma9910036312108451>.

This package provides a collection of functions for processing and analyzing metabolite data. The namesake function mrbin() converts 1D or 2D Nuclear Magnetic Resonance data into a matrix of values suitable for further data analysis and performs basic processing steps in a reproducible way. Negative values, a common issue in such data, can be replaced by positive values (<doi:10.1021/acs.jproteome.0c00684>). All used parameters are stored in a readable text file and can be restored from that file to enable exact reproduction of the data at a later time. The function fia() ranks features according to their impact on classifier models, especially artificial neural network models.

Collection of functions to perform fixed and random-effects multivariate and univariate meta-analysis and meta-regression.

This package implements the split-fit-evaluate-assess workflow from Hastie, Tibshirani, and Friedman (2009, ISBN:978-0-387-84857-0) "The Elements of Statistical Learning", Chapter 7. Provides three-way data splitting with automatic stratification, mandatory seeds for reproducibility, automatic data type handling, and 10 algorithms out of the box. Uses Rust backend for cross-language deterministic splitting. Designed for tabular supervised learning with minimal ceremony. Polyglot parity with the Python mlw package on PyPI'.

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 tools of Bayesian analysis framework using the method suggested by Berger (1985) <doi:10.1007/978-1-4757-4286-2> for multivariate normal (MVN) distribution and multivariate normal mixture (MixMVN) distribution: a) calculating Bayesian posteriori of (Mix)MVN distribution; b) generating random vectors of (Mix)MVN distribution; c) Markov chain Monte Carlo (MCMC) for (Mix)MVN distribution.

This package contains model-based treatment of missing data for regression models with missing values in covariates or the dependent variable using maximum likelihood or Bayesian estimation (Ibrahim et al., 2005; <doi:10.1198/016214504000001844>; Luedtke, Robitzsch, & West, 2020a, 2020b; <doi:10.1080/00273171.2019.1640104><doi:10.1037/met0000233>). The regression model can be nonlinear (e.g., interaction effects, quadratic effects or B-spline functions). Multilevel models with missing data in predictors are available for Bayesian estimation. Substantive-model compatible multiple imputation can be also conducted.

The utility of this package includes finite mixture modeling and model-based clustering through Manly mixture models by Zhu and Melnykov (2016) <DOI:10.1016/j.csda.2016.01.015>. It also provides capabilities for forward and backward model selection procedures.

This package provides tools to conduct Bayesian multivariate network meta-analysis providing - the single correlation coefficient model by Efthimiou et al. (2015) <doi:10.1093/biostatistics/kxu030>; - per-outcome treatment hierarchies using the surface under the cumulative ranking curve (SUCRA), the probability of best value, or median (or mean) ranks (Salanti et al., 2011) <doi:10.1016/j.jclinepi.2010.03.016>; - across-outcomes benefit-risk assessment using the ViÅ¡eKriterijumska Optimizacija I Kompromisno ReÅ¡enje (VIKOR) method (Opricovic & Tzeng, 2004) <doi:10.1016/S0377-2217(03)00020-1>; - convergence checks using trace plots, density plots, or the R-hat statistic; - forest plots of treatment estimates, scatter plots of per-outcome rankings, Hasse diagrams (Carlsen & Bruggemann, 2014) <doi:10.1002/cem.2569> to visualize the partial order of the treatments across all outcomes.

This package implements bivariate and Multivariate Quantile-on-Quantile Granger causality tests building on the Quantile-on-Quantile regression framework of Sim and Zhou (2015) <doi:10.1016/j.jbankfin.2015.01.013> and the quantile Granger causality test of Troster (2018) <doi:10.1080/07474938.2016.1172400>. The bivariate test estimates the local-linear slope in the quantile regression of y_t on lagged x_t with lagged y_t as control, using Gaussian kernel weights, and tests it against zero by paired bootstrap. The multivariate (conditional) test additionally conditions on a set of moderators Z and optional x times Z interaction terms, in the spirit of Sinha, Ghosh, Hussain, Nguyen and Das (2023) <doi:10.1016/j.eneco.2023.107021>. A Sup-Wald summary across the quantile grid is also provided. Heatmaps and 3D surfaces default to the MATLAB Parula colour map.

This package provides a collection of helper functions for analyzing Second Primary Cancer data, including functions to reshape data, to calculate patient states and analyze cancer incidence.

This package provides a comprehensive toolkit for conducting Multilevel Analysis of Individual Heterogeneity and Discriminatory Accuracy (MAIHDA). Methods are described in Merlo (2018) <doi:10.1016/j.socscimed.2017.12.026> and Evans et al. (2018) <doi:10.1016/j.socscimed.2017.11.011>. Automatically generates intersectional strata, fits analytical models, extracts statistics, and produces visualizations.

This package implements functional principal component analysis (FPCA) for univariate and multivariate sparse functional data. The package estimates eigenfunctions, eigenvalues, and error variance simultaneously via maximum likelihood estimation (MLE), using a spline basis representation of the eigenfunctions. Orthonormality of the estimated eigenfunctions is enforced through a modified Gram-Schmidt (MGS) orthogonalization procedure applied iteratively during estimation, avoiding direct optimization over the Stiefel manifold and improving numerical stability. The optimal number of basis functions and principal components is selected via an Akaike Information Criterion (AIC)-type criterion, supporting both a full grid-search strategy and a computationally efficient sequential selection approach. Principal component scores are estimated by conditional expectation, enabling reconstruction of individual trajectories over the entire domain from sparse observations. Pointwise confidence intervals for reconstructed trajectories are also provided. Methods are described in Mbaka, Cao and Carey (2026) <doi:10.48550/arXiv.2603.18833> and Mbaka and Carey (2026) <doi:10.48550/arXiv.2603.19799>.