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>.

Colour palettes and helper functions for visualising Mycobacterium tuberculosis genomic and epidemiological data with ggplot2 and ggtree'. The package provides predefined palettes, scale functions, tree/cladogram helpers, and convenient preview tools to ensure consistent branding in pathogen-omics visualisations. The palettes were developed as part of the mycolorsTB project <https://github.com/PathoGenOmics-Lab/mycolorsTB>.

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>.

The Society of Actuaries (SOA) provides an extensive online database called Mortality and Other Rate Tables ('MORT') at <https://mort.soa.org/>. This database contains mortality, lapse, and valuation tables that cover a variety of product types and nations. Users of the database can download any tables in Excel', CSV', or XML formats. This package provides convenience functions that read XML formats from the database and return R objects.

This package provides a convenient interface in OpenMx for building Estabrook's (2015) <doi:10.1037/a0034523> Measurement Model of Derivatives (MMOD).

It performs the followings Multivariate Process Capability Indices: Shahriari et al. (1995) Multivariate Capability Vector, Taam et al. (1993) Multivariate Capability Index (MCpm), Pan and Lee (2010) proposal (NMCpm) and the followings based on Principal Component Analysis (PCA):Wang and Chen (1998), Xekalaki and Perakis (2002) and Wang (2005). Two datasets are included.

Regularly spaced grids containing continuous data are transformed to contour polygons. A grid can be defined by a data.frame (x, y, value), an sf object or a raster from terra'.

This package provides a user-friendly way for the analysis of multinomial processing tree (MPT) models (e.g., Riefer, D. M., and Batchelder, W. H. [1988]. Multinomial modeling and the measurement of cognitive processes. Psychological Review, 95, 318-339) for single and multiple datasets. The main functions perform model fitting and model selection. Model selection can be done using AIC, BIC, or the Fisher Information Approximation (FIA) a measure based on the Minimum Description Length (MDL) framework. The model and restrictions can be specified in external files or within an R script in an intuitive syntax or using the context-free language for MPTs. The classical .EQN file format for model files is also supported. Besides MPTs, this package can fit a wide variety of other cognitive models such as SDT models (see fit.model). It also supports multicore fitting and FIA calculation (using the snowfall package), can generate or bootstrap data for simulations, and plot predicted versus observed data.

Framework to facilitate patient subtyping with similarity network fusion and meta clustering. The similarity network fusion (SNF) algorithm was introduced by Wang et al. (2014) in <doi:10.1038/nmeth.2810>. SNF is a data integration approach that can transform high-dimensional and diverse data types into a single similarity network suitable for clustering with minimal loss of information from each initial data source. The meta clustering approach was introduced by Caruana et al. (2006) in <doi:10.1109/ICDM.2006.103>. Meta clustering involves generating a wide range of cluster solutions by adjusting clustering hyperparameters, then clustering the solutions themselves into a manageable number of qualitatively similar solutions, and finally characterizing representative solutions to find ones that are best for the user's specific context. This package provides a framework to easily transform multi-modal data into a wide range of similarity network fusion-derived cluster solutions as well as to visualize, characterize, and validate those solutions. Core package functionality includes easy customization of distance metrics, clustering algorithms, and SNF hyperparameters to generate diverse clustering solutions; calculation and plotting of associations between features, between patients, and between cluster solutions; and standard cluster validation approaches including resampled measures of cluster stability, standard metrics of cluster quality, and label propagation to evaluate generalizability in unseen data. Associated vignettes guide the user through using the package to identify patient subtypes while adhering to best practices for unsupervised learning.

The user must supply a matrix filled with similarity values. The software will search for significant differences between similarity values at different hierarchical levels. The algorithm will return a Loess-smoothed plot of the similarity values along with the inflection point, if there are any. There is the option to search for an inflection point within a specified range. The package also has a function that will return the matrix components at a specified cutoff. References: Mullner. <ArXiv:1109.2378>; Cserhati, Carter. (2020, Journal of Creation 34(3):41-50), <https://dl0.creation.com/articles/p137/c13759/j34-3_64-73.pdf>.

This package provides tools for calculating I-Scores, a simple way to measure how successful minor political parties are at influencing the major parties in their environment. I-Scores are designed to be a more comprehensive measurement of minor party success than vote share and legislative seats won, the current standard measurements, which do not reflect the strategies that most minor parties employ. The procedure leverages the Manifesto Project's NLP model to identify the issue areas that sentences discuss, see Burst et al. (2024) <doi:10.25522/manifesto.manifestoberta.56topics.context.2024.1.1>, and the Wordfish algorithm to estimate the relative positions that platforms take on those issue areas, see Slapin and Proksch (2008) <doi:10.1111/j.1540-5907.2008.00338.x>.

This package provides a tidyverse'-friendly client for the National Statistics Office of Mongolia PXWeb API <https://data.1212.mn/> with helpers to discover tables, variables, and fetch statistical data. Also includes utilities to retrieve Mongolia administrative boundaries (ADM0-ADM2) as sf objects from open sources for mapping and spatial analysis.

Algorithms to approximate the Pareto-front of multi-criteria minimum spanning tree problems.

This package provides a user-friendly interface for the construction of Makefiles'.

Incorporates a Bayesian monotonic single-index mixed-effect model with a multivariate skew-t likelihood, specifically designed to handle survey weights adjustments. Features include a simulation program and an associated Gibbs sampler for model estimation. The single-index function is constrained to be monotonic increasing, utilizing a customized Gaussian process prior for precise estimation. The model assumes random effects follow a canonical skew-t distribution, while residuals are represented by a multivariate Student-t distribution. Offers robust Bayesian adjustments to integrate survey weight information effectively.

This package provides an end-to-end workflow for estimating average treatment effects in clustered (multilevel) observational data. Core functionality includes cluster-aware propensity score estimation using fixed effects and Mundlak-style specifications, inverse probability weighting, within-cluster nearest-neighbor matching, covariate balance diagnostics at both individual and cluster-mean levels, outcome regression with cluster-robust standard errors, propensity score overlap visualization, and tipping-point sensitivity analysis for omitted cluster-level confounding.

Simultaneously estimates sparse regression coefficients and response network structure in multivariate models with missing data. Unlike traditional approaches requiring imputation, handles missingness natively through unbiased estimating equations (MCAR/MAR compatible). Employs dual L1 regularization with automated selection via cross-validation or information criteria. Includes parallel computation, warm starts, adaptive grids, publication-ready visualizations, and prediction methods. Ideal for genomics, neuroimaging, and multi-trait studies with incomplete high-dimensional outcomes. See Zeng et al. (2025) <doi:10.48550/arXiv.2507.05990>.

Fits the Bayesian multinomial probit model via Markov chain Monte Carlo. The multinomial probit model is often used to analyze the discrete choices made by individuals recorded in survey data. Examples where the multinomial probit model may be useful include the analysis of product choice by consumers in market research and the analysis of candidate or party choice by voters in electoral studies. The MNP package can also fit the model with different choice sets for each individual, and complete or partial individual choice orderings of the available alternatives from the choice set. The estimation is based on the efficient marginal data augmentation algorithm that is developed by Imai and van Dyk (2005). "A Bayesian Analysis of the Multinomial Probit Model Using the Data Augmentation." Journal of Econometrics, Vol. 124, No. 2 (February), pp. 311-334. <doi:10.1016/j.jeconom.2004.02.002> Detailed examples are given in Imai and van Dyk (2005). "MNP: R Package for Fitting the Multinomial Probit Model." Journal of Statistical Software, Vol. 14, No. 3 (May), pp. 1-32. <doi:10.18637/jss.v014.i03>.

This package contains functions for multiple imputation which complements existing functionality in R. In particular, several imputation methods for the mice package (van Buuren & Groothuis-Oudshoorn, 2011, <doi:10.18637/jss.v045.i03>) are implemented. Main features of the miceadds package include plausible value imputation (Mislevy, 1991, <doi:10.1007/BF02294457>), multilevel imputation for variables at any level or with any number of hierarchical and non-hierarchical levels (Grund, Luedtke & Robitzsch, 2018, <doi:10.1177/1094428117703686>; van Buuren, 2018, Ch.7, <doi:10.1201/9780429492259>), imputation using partial least squares (PLS) for high dimensional predictors (Robitzsch, Pham & Yanagida, 2016), nested multiple imputation (Rubin, 2003, <doi:10.1111/1467-9574.00217>), substantive model compatible imputation (Bartlett et al., 2015, <doi:10.1177/0962280214521348>), and features for the generation of synthetic datasets (Reiter, 2005, <doi:10.1111/j.1467-985X.2004.00343.x>; Nowok, Raab, & Dibben, 2016, <doi:10.18637/jss.v074.i11>).

Mass measurement corrections and uncertainties using calibration data, as recommended by EURAMET's guideline No. 18 (2015) ISBN:978-3-942992-40-4 . The package provides classes, functions, and methods for storing information contained in calibration certificates and converting balance readings to both conventional mass and real mass. For the latter, the Magnitude of the Air Buoyancy Correction factor employs models (such as the CIMP-2007 formula revised by Picard, Davis, Gläser, and Fujii (2008) <doi:10.1088/0026-1394/45/2/004>) to estimate the local air density using measured environmental conditions.

Multi-step adaptive elastic-net (MSAENet) algorithm for feature selection in high-dimensional regressions proposed in Xiao and Xu (2015) <DOI:10.1080/00949655.2015.1016944>, with support for multi-step adaptive MCP-net (MSAMNet) and multi-step adaptive SCAD-net (MSASNet) methods.

This system allows one to model a multi-variate, multi-response problem with interaction effects. It combines the usual squared error loss for the multi-response problem with some penalty terms to encourage responses that correlate to form groups and also allow for modeling main and interaction effects that exit within the covariates. The optimization method employed is the Alternating Direction Method of Multipliers (ADMM). The implementation is based on the methodology presented on Quachie Asenso, T., & Zucknick, M. (2023) <doi:10.48550/arXiv.2303.11155>.

Monolix is a tool for running mixed effects model using saem'. This tool allows you to convert Monolix models to rxode2 (Wang, Hallow and James (2016) <doi:10.1002/psp4.12052>) using the form compatible with nlmixr2 (Fidler et al (2019) <doi:10.1002/psp4.12445>). If available, the rxode2 model will read in the Monolix data and compare the simulation for the population model individual model and residual model to immediately show how well the translation is performing. This saves the model development time for people who are creating an rxode2 model manually. Additionally, this package reads in all the information to allow simulation with uncertainty (that is the number of observations, the number of subjects, and the covariance matrix) with a rxode2 model. This is complementary to the babelmixr2 package that translates nlmixr2 models to Monolix and can convert the objects converted from monolix2rx to a full nlmixr2 fit. While not required, you can get/install the lixoftConnectors package in the Monolix installation, as described at the following url <https://monolixsuite.slp-software.com/r-functions/2024R1/installation-and-initialization>. When lixoftConnectors is available, Monolix can be used to load its model library instead manually setting up text files (which only works with old versions of Monolix').

Package for moving grid adjustment in plant breeding field trials.