Compute effect sizes and their sampling variances from factorial experimental designs. The package supports calculation of simple effects, overall effects, and interaction effects for use in factorial meta-analyses. See Gurevitch et al. (2000) <doi:10.1086/303337>, Morris et al. (2007) <doi:10.1890/06-0442>, Lajeunesse (2011) <doi:10.1890/11-0423.1> and Macartney et al. (2022) <doi:10.1016/j.neubiorev.2022.104554>.

Maximum likelihood estimation for generalized linear mixed models via Monte Carlo EM. For a description of the algorithm see Brian S. Caffo, Wolfgang Jank and Galin L. Jones (2005) <DOI:10.1111/j.1467-9868.2005.00499.x>.

Fit multi-level models with possibly correlated random effects using Markov Chain Monte Carlo simulation. Such models allow smoothing over space and time and are useful in, for example, small area estimation.

This package provides a graphical user interface (GUI) for performing Multidimensional Scaling applications and interactively analysing the results all within the GUI environment. The MDS-GUI provides means of performing Classical Scaling, Least Squares Scaling, Metric SMACOF, Non-Metric SMACOF, Kruskal's Analysis and Sammon Mapping with animated optimisation.

This package provides a compilation of more than 80 functions designed to quantitatively and visually evaluate prediction performance of regression (continuous variables) and classification (categorical variables) of point-forecast models (e.g. APSIM, DSSAT, DNDC, supervised Machine Learning). For regression, it includes functions to generate plots (scatter, tiles, density, & Bland-Altman plot), and to estimate error metrics (e.g. MBE, MAE, RMSE), error decomposition (e.g. lack of accuracy-precision), model efficiency (e.g. NSE, E1, KGE), indices of agreement (e.g. d, RAC), goodness of fit (e.g. r, R2), adjusted correlation coefficients (e.g. CCC, dcorr), symmetric regression coefficients (intercept, slope), and mean absolute scaled error (MASE) for time series predictions. For classification (binomial and multinomial), it offers functions to generate and plot confusion matrices, and to estimate performance metrics such as accuracy, precision, recall, specificity, F-score, Cohen's Kappa, G-mean, and many more. For more details visit the vignettes <https://adriancorrendo.github.io/metrica/>.

This package implements the algorithm of Remez (1962) for polynomial minimax approximation and of Cody et al. (1968) <doi:10.1007/BF02162506> for rational minimax approximation.

This package provides the Augmented Dickey-Fuller test and its variations to check the existence of bubbles (explosive behavior) for time series, based on the article by Peter C. B. Phillips, Shuping Shi and Jun Yu (2015a) <doi:10.1111/iere.12131>. Some functions may take a while depending on the size of the data used, or the number of Monte Carlo replications applied.

We introduce a generalized factor model designed to jointly analyze high-dimensional multi-modality data from multiple studies by extracting study-shared and specified factors. Our factor models account for heterogeneous noises and overdispersion among modality variables with augmented covariates. We propose an efficient and speedy variational estimation procedure for estimating model parameters, along with a novel criterion for selecting the optimal number of factors. More details can be referred to Liu et al. (2025) <doi:10.48550/arXiv.2507.09889>.

Density, distribution function, ... hazard function, cumulative hazard function, survival function for survival distributions with piece-wise constant hazards and multiple states and methods to plot and summarise those distributions. A derivation of the used algorithms can be found in my masters thesis <doi:10.25365/thesis.76098>.

Find common entities detected in both positive and negative ionization mode, delete this entity in the less sensible mode and combine both matrices.

Facilitate the description, transformation, exploration, and reproducibility of metabarcoding analyses. MiscMetabar is mainly built on top of the phyloseq', dada2 and targets R packages. It helps to build reproducible and robust bioinformatics pipelines in R. MiscMetabar makes ecological analysis of alpha and beta-diversity easier, more reproducible and more powerful by integrating a large number of tools. Important features are described in Taudière A. (2023) <doi:10.21105/joss.06038>.

This package provides a comprehensive, simulation-based toolkit for power and sample-size analysis for linear and generalized linear mixed-effects models (LMMs and GLMMs). Supports Gaussian, binomial, Poisson, and negative binomial families via lme4'; Wald and likelihood-ratio tests; multi-parameter sensitivity grids; power curves and minimum sample-size solvers; parallel evaluation with deterministic seeds; and full reproducibility (manifests, result bundling, and export to CSV/JSON). Delivers thorough diagnostics per run (failure rate, singular-fit rate, effective N) and publication-ready summary tables. References: Bates et al. (2015) "Fitting Linear Mixed-Effects Models Using lme4" <doi:10.18637/jss.v067.i01>; Green and MacLeod (2016) "SIMR: an R package for power analysis of generalized linear mixed models by simulation" <doi:10.1111/2041-210X.12504>.

Create native charts for Microsoft PowerPoint', Microsoft Excel and Microsoft Word documents. The resulting charts can then be edited and annotated in the host application. It provides functions to create charts and to modify their content and formatting. The chart's underlying data is automatically saved within the Word', Excel or PowerPoint file. It extends the officer package, which does not provide native Microsoft chart production.

Compute important quantities when we consider stochastic systems that are observed continuously. Such as, Cost model, Limiting distribution, Transition matrix, Transition distribution and Occupancy matrix. The methods are described, for example, Ross S. (2014), Introduction to Probability Models. Eleven Edition. Academic Press.

This package provides functions for analyzing the association between one single response categorical variable (SRCV) and one multiple response categorical variable (MRCV), or between two or three MRCVs. A modified Pearson chi-square statistic can be used to test for marginal independence for the one or two MRCV case, or a more general loglinear modeling approach can be used to examine various other structures of association for the two or three MRCV case. Bootstrap- and asymptotic-based standardized residuals and model-predicted odds ratios are available, in addition to other descriptive information. Statisical methods implemented are described in Bilder et al. (2000) <doi:10.1080/03610910008813665>, Bilder and Loughin (2004) <doi:10.1111/j.0006-341X.2004.00147.x>, Bilder and Loughin (2007) <doi:10.1080/03610920600974419>, and Koziol and Bilder (2014) <https://journal.r-project.org/articles/RJ-2014-014/>.

This package provides a flexible general-purpose toolbox for implementing Rescorla-Wagner models in multi-armed bandit tasks. As the successor and functional extension of the binaryRL package, multiRL modularizes the Markov Decision Process (MDP) into six core components. This framework enables users to construct custom models via intuitive if-else syntax and define latent learning rules for agents. For parameter estimation, it provides both likelihood-based inference (MLE and MAP) and simulation-based inference (ABC and RNN), with full support for parallel processing across subjects. The workflow is highly standardized, featuring four main functions that strictly follow the four-step protocol (and ten rules) proposed by Wilson & Collins (2019) <doi:10.7554/eLife.49547>. Beyond the three built-in models (TD, RSTD, and Utility), users can easily derive new variants by declaring which variables are treated as free parameters.

This package provides tools to generate HTML interfaces for adaptive and non-adaptive tests using the shiny package (Chalmers (2016) <doi:10.18637/jss.v071.i05>). Suitable for applying unidimensional and multidimensional computerized adaptive tests (CAT) using item response theory methodology and for creating simple questionnaires forms to collect response data directly in R. Additionally, optimal test designs (e.g., "shadow testing") are supported for tests that contain a large number of item selection constraints. Finally, package contains tools useful for performing Monte Carlo simulations for studying test item banks.

This GUI for the mi package walks the user through the steps of multiple imputation and the analysis of completed data.

The Iterative Proportional Fitting (IPF) algorithm operates on count data. This package offers implementations for several algorithms that extend this to nested structures: parent and child items for both of which constraints can be provided. The fitting algorithms include Iterative Proportional Updating <https://trid.trb.org/view/881554>, Hierarchical IPF <doi:10.3929/ethz-a-006620748>, Entropy Optimization <https://trid.trb.org/view/881144>, and Generalized Raking <doi:10.2307/2290793>. Additionally, a number of replication methods is also provided such as Truncate, replicate, sample <doi:10.1016/j.compenvurbsys.2013.03.004>.

Auto-downloads Parquet data from the MTGJSON CDN and exposes the full Magic: The Gathering dataset through R6-based query interfaces backed by DuckDB'.

Computation and visualization of matrix correlation coefficients. The main method is the Similarity of Matrices Index, while various related measures like r1, r2, r3, r4, Yanai's GCD, RV, RV2, adjusted RV, Rozeboom's linear correlation and Coxhead's coefficient are included for comparison and flexibility.

This package implements a computational framework to predict microbial community-based metabolic profiles with O2PLS model. It provides procedures of model training and prediction. Paired microbiome and metabolome data are needed for modeling, and the trained model can be applied to predict metabolites of analogous environments using new microbial feature abundances.

Compute the average of a sequence of random vectors in a moving expanding window using a fixed amount of memory.

Translate R expressions to MathML or MathJax'/'LaTeX so that they can be rendered in R markdown documents and shiny apps. This package depends on R package rolog', which requires an installation of the SWI'-'Prolog runtime either from swi-prolog.org or from R package rswipl'.