This package provides a collection of various R functions for the purpose of Luminescence dating data analysis. This includes, amongst others, data import, export, application of age models, curve deconvolution, sequence analysis and plotting of equivalent dose distributions.

Changes of landscape diversity and structure can be detected soon if relying on landscape class combinations and analysing patterns at multiple scales. LandComp provides such an opportunity, based on Juhász-Nagy's functions (Juhász-Nagy P, Podani J 1983 <doi:10.1007/BF00129432>). Functions can handle multilayered data. Requirements of the input: binary data contained by a regular square or hexagonal grid, and the grid should have projected coordinates.

Miscellaneous scripts, e.g. functionality to make and plot factor diagrams for the statistical design.

Generate and correlate synthetic Likert and rating-scale questionnaire responses with predefined means, standard deviations, Cronbach's Alpha, Factor Loading table, coefficients, and other summary statistics. It can be used to simulate Likert data, construct multi-item scales, generate correlation matrices, and create example survey datasets for teaching statistics, psychometrics, and methodological research. Worked examples and documentation are available in the package articles, accessible via the package website, <https://winzarh.github.io/LikertMakeR/>.

Perform two linear combination methods for biomarkers: (1) Empirical performance optimization for specificity (or sensitivity) at a controlled sensitivity (or specificity) level of Huang and Sanda (2022) <doi:10.1214/22-aos2210>, and (2) weighted maximum score estimator with empirical minimization of averaged false positive rate and false negative rate. Both adopt the algorithms of Huang and Sanda (2022) <doi:10.1214/22-aos2210>. MOSEK solver is used and needs to be installed; an academic license for MOSEK is free.

Compute and visualize using the visNetwork package all the bivariate correlations of a dataframe. Several and different types of correlation coefficients (Pearson's r, Spearman's rho, Kendall's tau, distance correlation, maximal information coefficient and equal-freq discretization-based maximal normalized mutual information) are used according to the variable couple type (quantitative vs categorical, quantitative vs quantitative, categorical vs categorical).

This package provides functions for simulating missing morphometric data randomly, with taxonomic bias and with anatomical bias. LOST also includes functions for estimating linear and geometric morphometric data.

Simplify the loading matrix in factor models using the l1 criterion as proposed in Freyaldenhoven (2025) <doi:10.21799/frbp.wp.2020.25>. Given a data matrix, find the rotation of the loading matrix with the smallest l1-norm and/or test for the presence of local factors with main function local_factors().

Kernel-based methods are powerful methods for integrating heterogeneous types of data. mixKernel aims at providing methods to combine kernel for unsupervised exploratory analysis. Different solutions are provided to compute a meta-kernel, in a consensus way or in a way that best preserves the original topology of the data. mixKernel also integrates kernel PCA to visualize similarities between samples in a non linear space and from the multiple source point of view <doi:10.1093/bioinformatics/btx682>. A method to select (as well as funtions to display) important variables is also provided <doi:10.1093/nargab/lqac014>.

This package implements methods for post-hoc analysis and visualisation of benchmark experiments, for mlr3 and beyond.

Conduct network meta-analyses through a graphical user interface using bnma', gemtc and netmeta with additional analysis provided by meta and metafor'. Frequentist, Bayesian, meta-regression and baseline risk meta-regression analyses can all be conducted using a consistent data structure and terminology. Many options are provided for downloading publication-ready outputs and analyses can be reproduced outside of the application by downloading a quarto file. The interface was generated using shinyscholar'. The initial version of the app was described by Owen et al. (2018) <doi:10.1002/jrsm.1373>, Bayesian ranking visualisations were described by Nevill et al. (2023) <doi:10.1016/j.jclinepi.2023.02.016> and metaregression was described by Morris et al. (2025) <doi:10.1016/j.jclinepi.2025.111839>.

Complex niche models show low performance in identifying the most important range-limiting environmental variables and in transferring habitat suitability to novel environmental conditions (Warren and Seifert, 2011 <DOI:10.1890/10-1171.1>; Warren et al., 2014 <DOI:10.1111/ddi.12160>). This package helps to identify the most important set of uncorrelated variables and to fine-tune Maxent's regularization multiplier. In combination, this allows to constrain complexity and increase performance of Maxent niche models (assessed by information criteria, such as AICc (Akaike, 1974 <DOI:10.1109/TAC.1974.1100705>), and by the area under the receiver operating characteristic (AUC) (Fielding and Bell, 1997 <DOI:10.1017/S0376892997000088>). Users of this package should be familiar with Maxent niche modelling.

Calculates two sets of post-hoc variable importance measures for multivariate random forests. The first set of variable importance measures are given by the sum of mean split improvements for splits defined by feature j measured on user-defined examples (i.e., training or testing samples). The second set of importance measures are calculated on a per-outcome variable basis as the sum of mean absolute difference of node values for each split defined by feature j measured on user-defined examples (i.e., training or testing samples). The user can optionally threshold both sets of importance measures to include only splits that are statistically significant as measured using an F-test.

Common mass spectrometry tools described in John Roboz (2013) <doi:10.1201/b15436>. It allows checking element isotopes, calculating (isotope labelled) exact monoisitopic mass, m/z values and mass accuracy, and inspecting possible contaminant mass peaks, examining possible adducts in electrospray ionization (ESI) and matrix-assisted laser desorption ionization (MALDI) ion sources.

This package provides tools that facilitate ordinary differential equation (ODE) modeling in R'. This package allows one to perform simulations for ODE models that are encoded in the GNU MCSim model specification language (Bois, 2009) <doi:10.1093/bioinformatics/btp162> using ODE solvers from the R package deSolve (Soetaert et al., 2010) <doi:10.18637/jss.v033.i09>.

Selects bandwidth for the kernel density estimator with minimum distance method as proposed by Devroye and Lugosi (1996). The minimum distance method directly selects the optimal kernel density estimator from countably infinite kernel density estimators and indirectly selects the optimal bandwidth. This package selects the optimal bandwidth from finite kernel density estimators.

This package provides tools for likelihood-based inference in one-dimensional stochastic differential equations with mixed effects using expectationâ maximization (EM) algorithms. The package supports Wiener and Ornsteinâ Uhlenbeck diffusion processes with user-specified drift functions, allowing flexible parametric forms including polynomial, exponential, and trigonometric structures. Estimation is performed via Markov chain Monte Carlo EM.

This package provides tools that researchers can use to analyze untargeted metabolomics data generated using tandem mass spectroscopy from microbial communities. The overall approach taken to analyze metabolomics data parallels that used to analyze microbial communities using 16S rRNA gene sequencing data. Thus, we have a number of methods a user is able to use to generate data. Firstly, users can import Mass Spectrometry 1(MS1) data and filter it. Users are then able to match Mass Spectrometry 2(MS2) data to the filtered (or unfiltered) MS1 data. With the matched data users are able to cluster it, annotate it, predict de novo chemical formulas and calculate alpha and beta diversity. For chemical formula predictions, this was the method used; "Towards de novo identification of metabolites by analyzing tandem mass spectra" (Sebastian Böcker, Florian Rasche (2008) <doi:10.1093/bioinformatics/btn270>). The similarity/dissimilarity calculations we used to cluster our data together was: "Spectral entropy outperforms MS/MS dot product similarity for small-molecule compound identification" (Li, Y., Kind, T., Folz, J. et al. (2021) <doi:10.1038/s41592-021-01331-z>) and "Sharing and community curation of mass spectrometry data with Global Natural Products Social Molecular Networking" (Wang, M., Carver, J., Phelan, V. et al. (2021) <doi:10.1038/nbt.3597>).

Implementation of custom tidymodels metrics for multi-class prediction models with a single negative class. Currently are implemented macro-average sensitivity and specificity as in Mortaz, Ebrahim (2020) "Imbalance accuracy metric for model selection in multi-class imbalance classification problemsâ <doi:10.1016/j.knosys.2020.106490> and a generalized weighted Youden index as in Li, D.L., Shen F., Yin Y., Peng J.X and Chen P.Y. (2013) â Weighted Youden index and its two-independent-sample comparison based on weighted sensitivity and specificityâ <doi:10.3760/cma.j.issn.0366-6999.20123102>.

Mobile Motor Activity Research Consortium for Health (mMARCH) is a collaborative network of studies of clinical and community samples that employ common clinical, biological, and digital mobile measures across involved studies. One of the main scientific goals of mMARCH sites is developing a better understanding of the inter-relationships between accelerometry-measured physical activity (PA), sleep (SL), and circadian rhythmicity (CR) and mental and physical health in children, adolescents, and adults. Currently, there is no consensus on a standard procedure for a data processing pipeline of raw accelerometry data, and few open-source tools to facilitate their development. The R package GGIR is the most prominent open-source software package that offers great functionality and tremendous user flexibility to process raw accelerometry data. However, even with GGIR', processing done in a harmonized and reproducible fashion requires a non-trivial amount of expertise combined with a careful implementation. In addition, novel accelerometry-derived features of PA/SL/CR capturing multiscale, time-series, functional, distributional and other complimentary aspects of accelerometry data being constantly proposed and become available via non-GGIR R implementations. To address these issues, mMARCH developed a streamlined harmonized and reproducible pipeline for loading and cleaning raw accelerometry data, extracting features available through GGIR as well as through non-GGIR R packages, implementing several data and feature quality checks, merging all features of PA/SL/CR together, and performing multiple analyses including Joint Individual Variation Explained (JIVE), an unsupervised machine learning dimension reduction technique that identifies latent factors capturing joint across and individual to each of three domains of PA/SL/CR. In detail, the pipeline generates all necessary R/Rmd/shell files for data processing after running GGIR for accelerometer data. In module 1, all csv files in the GGIR output directory were read, transformed and then merged. In module 2, the GGIR output files were checked and summarized in one excel sheet. In module 3, the merged data was cleaned according to the number of valid hours on each night and the number of valid days for each subject. In module 4, the cleaned activity data was imputed by the average Euclidean norm minus one (ENMO) over all the valid days for each subject. Finally, a comprehensive report of data processing was created using Rmarkdown, and the report includes few exploratory plots and multiple commonly used features extracted from minute level actigraphy data. Reference: Guo W, Leroux A, Shou S, Cui L, Kang S, Strippoli MP, Preisig M, Zipunnikov V, Merikangas K (2022) Processing of accelerometry data with GGIR in Motor Activity Research Consortium for Health (mMARCH) Journal for the Measurement of Physical Behaviour, 6(1): 37-44.

The minorization-maximization (MM) algorithm is a powerful tool for maximizing nonconcave target function. However, for most existing MM algorithms, the surrogate function in the minorization step is constructed in a case-specific manner and requires manual programming. To address this limitation, we develop the R package MMAD, which systematically integrates the assembly--decomposition technology in the MM framework. This new package provides a comprehensive computational toolkit for one-stop inference of complex target functions, including function construction, evaluation, minorization and optimization via MM algorithm. By representing the target function through a hierarchical composition of assembly functions, we design a hierarchical algorithmic structure that supports both bottom-up operations (construction, evaluation) and top-down operation (minorization).

Play and record games of minesweeper using a graphics device that supports event handling. Replay recorded games and save GIF animations of them. Based on classic minesweeper as detailed by Crow P. (1997) <https://minesweepergame.com/math/a-mathematical-introduction-to-the-game-of-minesweeper-1997.pdf>.

Send server-side tracking data from R. The Measurement Protocol version 2 <https://developers.google.com/analytics/devguides/collection/protocol/ga4> allows sending HTTP tracking events from R code.

Computes mutual information matrices from continuous, categorical and survival variables, as well as feature selection with minimum redundancy, maximum relevance (mRMR) and a new ensemble mRMR technique. Published in De Jay et al. (2013) <doi:10.1093/bioinformatics/btt383>.