Dealing with neutrosophic data in single valued form using score, accuracy and certainty functions to calculate ranks of Single Valued Neutrosophic Set (SVNS), also to calculate the Mann-Whitney test, and making a post-hoc test after rejecting the null hypothesis using the Neutrosophic Statistics Kruskal-Wallis test. For more information see Miari, Mahmoud; Anan, Mohamad Taher; Zeina, Mohamed Bisher(2022) <https://digitalrepository.unm.edu/nss_journal/vol51/iss1/60/>.

This package provides a shiny web application to map scores from clinical instruments (PANSS, SQLS, WHODAS 2.0, PHQ-8, EQ-5D-5L) to preference-based EQ-5D-5L health utility values using validated regression-based and beta-mixture mapping algorithms developed from Singapore population studies. Intended for use in health economic evaluations and cost-utility analyses. Methods are based on: Abdin et al. (2019) <doi:10.1007/s11136-018-2037-7>, Seow et al. (2023) <doi:10.1080/14737167.2023.2215430>, Abdin et al. (2021) <doi:10.1186/s12888-021-03463-0>, Abdin et al. (2024) <doi:10.1080/14737167.2024.2376100>.

Spontaneous adverse event reports have a high potential for detecting adverse drug reactions. However, due to their dimension, the analysis of such databases requires statistical methods. We propose to use a logistic regression whose sparsity is viewed as a model selection challenge. Since the model space is huge, a Metropolis-Hastings algorithm carries out the model selection by maximizing the BIC criterion.

Olm and Megolm encryption ratchet primitives for the Matrix messaging protocol <https://matrix.org/>, wrapping the vodozemac Rust crate. Provides device-key generation, one-time-key management, 1:1 Olm sessions, and Megolm group sessions. Pairs with the mx.api package, which handles Matrix HTTP transport.

Palettes Inspired by Works at the Metropolitan Museum of Art in New York. Currently contains over 50 color schemes and checks for colorblind-friendliness of palettes. Colorblind accessibility checked using the colorblindcheck package by Jakub Nowosad'<https://jakubnowosad.com/colorblindcheck/>.

This package implements the method of successive dichotomizations by Bradley and Massof (2018) <doi:10.1371/journal.pone.0206106>, which estimates item measures, person measures and ordered rating category thresholds given ordinal rating scale data.

Various tools for microeconomic analysis and microeconomic modelling, e.g. estimating quadratic, Cobb-Douglas and Translog functions, calculating partial derivatives and elasticities of these functions, and calculating Hessian matrices, checking curvature and preparing restrictions for imposing monotonicity of Translog functions.

This package provides tools for longitudinal meta-analysis where studies contribute effect sizes at multiple follow-up time points. Implements robust variance estimation (RVE) with Tipton small-sample corrections following Hedges, Tipton, and Johnson (2010) <doi:10.1002/jrsm.5> and Tipton (2015) <doi:10.1037/met0000011>, time-varying sensitivity analysis via the Impact Threshold for a Confounding Variable (ITCV) following Frank (2000) <doi:10.1177/0049124100029002003>, benchmark calibration of the ITCV threshold against observed study-level covariates, spline-based nonlinear time-trend modeling with a nonlinearity test, and leave-k-out fragility analysis across the follow-up trajectory. Designed for researchers synthesising evidence from studies with repeated outcome measurement in education, psychology, health, and the social sciences.

Implementations of MOSUM-based statistical procedures and algorithms for detecting multiple changes in the mean. This comprises the MOSUM procedure for estimating multiple mean changes from Eichinger and Kirch (2018) <doi:10.3150/16-BEJ887> and the multiscale algorithmic extension from Cho and Kirch (2022) <doi:10.1007/s10463-021-00811-5>, as well as the bootstrap procedure for generating confidence intervals about the locations of change points as proposed in Cho and Kirch (2022) <doi:10.1016/j.csda.2022.107552>. See also Meier, Kirch and Cho (2021) <doi:10.18637/jss.v097.i08> which accompanies the R package.

This package provides a lightweight framework for model selection and hyperparameter tuning in R. The package offers intuitive tools for grid search, cross-validation, and combined grid search with cross-validation that work seamlessly with virtually any modeling package. Designed for flexibility and ease of use, it standardizes tuning workflows while remaining fully compatible with a wide range of model interfaces and estimation functions.

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 a model designed to be a reliable testbed where various gene drive interventions for mosquito-borne diseases control. It is being developed to accommodate the use of various mosquito-specific gene drive systems within a population dynamics framework that allows migration of individuals between patches in landscape. Previous work developing the population dynamics can be found in Deredec et al. (2001) <doi:10.1073/pnas.1110717108> and Hancock & Godfray (2007) <doi:10.1186/1475-2875-6-98>, and extensions to accommodate CRISPR homing dynamics in Marshall et al. (2017) <doi:10.1038/s41598-017-02744-7>.

This package implements Meta Fuzzy Functions (MFFs) for regression Tak and Ucan (2026) <doi:10.1016/j.asoc.2026.114592> by aggregating predictions from multiple base learners using membership weights learned in the prediction space of validation set. The package supports fuzzy and crisp meta-ensemble structures via Fuzzy C-Means (FCM) Tak (2018) <doi:10.1016/j.asoc.2018.08.009>, Possibilistic FCM (PFCM) Tak (2021) <doi:10.1016/j.ins.2021.01.024>, Gustafsonâ Kessel (GK) clustering, and k-means, and provides a workflow to (i) generate validation/test prediction matrices from common regression learners (linear and penalized regression via glmnet', random forests, gradient boosting with xgboost and lightgbm'), (ii) fit cluster-wise meta fuzzy functions and compute membership-based weights, (iii) tune clustering-related hyperparameters (number of clusters/functions, fuzziness exponent, possibilistic regularization) via grid search on validation loss, and (iv) predict on new/test prediction matrices and evaluate performance using standard regression metrics (MAE, RMSE, MAPE, SMAPE, MSE, MedAE). This enables flexible, interpretable ensemble regression where different base models contribute to different meta components according to learned memberships.

Estimate parameters of linear regression and logistic regression with missing covariates with missing data, perform model selection and prediction, using EM-type algorithms. Jiang W., Josse J., Lavielle M., TraumaBase Group (2020) <doi:10.1016/j.csda.2019.106907>.

This package provides tools to simulate, analyse, visualise, and benchmark grid-based number merge puzzles. The package implements generic grid mechanics, tile-spawning rules, merge rules, scoring functions, reproducible simulation utilities, and local Shiny and WebGL interfaces for interactive use. It is intended for teaching, algorithmic experimentation, and game-theoretic examples. The autoplay helpers use standard heuristic search and Monte Carlo simulation ideas described in Russell and Norvig (2021, ISBN:9780134610993) and Robert and Casella (2004, ISBN:9780387212395).

Enables us to use the functions of the package magick interactively.

Advanced methods for a valuable quantitative environmental risk assessment using Bayesian inference of survival and reproduction Data. Among others, it facilitates Bayesian inference of the general unified threshold model of survival (GUTS). See our companion paper Baudrot and Charles (2021) <doi:10.21105/joss.03200>, as well as complementary details in Baudrot et al. (2018) <doi:10.1021/acs.est.7b05464> and Delignette-Muller et al. (2017) <doi:10.1021/acs.est.6b05326>.

Causal moderated mediation analysis using the methods proposed by Qin and Wang (2023) <doi:10.3758/s13428-023-02095-4>. Causal moderated mediation analysis is crucial for investigating how, for whom, and where a treatment is effective by assessing the heterogeneity of mediation mechanism across individuals and contexts. This package enables researchers to estimate and test the conditional and moderated mediation effects, assess their sensitivity to unmeasured pre-treatment confounding, and visualize the results. The package is built based on the quasi-Bayesian Monte Carlo method, because it has relatively better performance at small sample sizes, and its running speed is the fastest. The package is applicable to a treatment of any scale, a binary or continuous mediator, a binary or continuous outcome, and one or more moderators of any scale.

This package provides tools to handle, manipulate and explore trajectory data, with an emphasis on data from tracked animals. The package is designed to support large studies with several million location records and keep track of units where possible. Data import directly from movebank <https://www.movebank.org/cms/movebank-main> and files is facilitated.

Allows to perform the multivariate version of the Diebold-Mariano test for equal predictive ability of multiple forecast comparison. Main reference: Mariano, R.S., Preve, D. (2012) <doi:10.1016/j.jeconom.2012.01.014>.

This package provides a simple function, mwsApp(), that runs a shiny app spanning multiple, connected windows. This uses all standard shiny conventions, and depends only on the shiny package.

This package provides methods for Geographically Weighted Regression with spatial autocorrelation (Geniaux and Martinetti 2017) <doi:10.1016/j.regsciurbeco.2017.04.001>. Implements Multiscale Geographically Weighted Regression with Top-Down Scale approaches (Geniaux 2026) <doi:10.1007/s10109-025-00481-4>.

Supports morphological analysis for Japanese by using MeCab <https://taku910.github.io/mecab/>, Sudachi <https://github.com/WorksApplications/Sudachi>, Chamame <https://chamame.ninjal.ac.jp/>, or Ginza <https://github.com/megagonlabs/ginza>. Can input a data.frame and obtain all results of MeCab and the row number of the original data.frame as a text id.

This package provides TOML representations of packages needed that must be installed to run a project. Includes functions to specify detailed installation functions, validate files, and to use a given file as the requirements for a project. Handles package installations, when necessary, via pak'.