Equivalence tests and related confidence intervals for the comparison of two treatments, simultaneously for one or many normally distributed, primary response variables (endpoints). The step-up procedure of Quan et al. (2001) is both applied for differences and extended to ratios of means. A related single-step procedure is also available.

We introduce a high-dimensional multi-study robust factor model, which learns latent features and accounts for the heterogeneity among source. It could be used for analyzing heterogeneous RNA sequencing data. More details can be referred to Jiang et al. (2025) <doi:10.48550/arXiv.2506.18478>.

Analyse disability course in multiple sclerosis (MS) from longitudinal data. The package provides a flexible framework for identifying disability events under user-specified criteria, allowing adaptation to different study designs and endpoints. Tools are included to facilitate transparent and reproducible reporting of the settings used in the analysis. For consensus-based recommendations on endpoint calculation and use of the package, see Montobbio et al. (2026) <doi:10.1177/13524585261478492>. For the original description of the computational framework and illustrative applications, see Montobbio et al. (2024) <doi:10.1177/13524585241243157>.

Helpers for common data analysis tasks including missing-value summaries and filters, simple reporting and plotting utilities, Excel import and export workflows, and reading geospatial formats (for example shapefiles in zip archives, file geodatabases, KMZ, and KML) via sf and related packages. Also includes small project utilities such as creating directories, gitignore scaffolding, combined package loading, and optional lintr setup.

Routines for assessing multivariate normality. Implements three Wald's type chi-squared tests; non-parametric Anderson-Darling and Cramer-von Mises tests; Doornik-Hansen test, Royston test and Henze-Zirkler test.

Create beautiful and customizable tables to summarize several statistical models side-by-side. Draw coefficient plots, multi-level cross-tabs, dataset summaries, balance tables (a.k.a. "Table 1s"), and correlation matrices. This package supports dozens of statistical models, and it can produce tables in HTML, LaTeX, Word, Markdown, PDF, PowerPoint, Excel, RTF, JPG, or PNG. Tables can easily be embedded in Rmarkdown or knitr dynamic documents. Details can be found in Arel-Bundock (2022) <doi:10.18637/jss.v103.i01>.

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.

Computes model-based effect sizes for fixed-effect coefficients in multilevel (hierarchical) models. The coefficient effect sizes are standardized mean differences from zero (d) and unique variance-explained measures (squared semi-partial correlations, sr2). The package also reports variance components and level-specific and total R-squared values. It supports 2-level and 3-level linear and binary logistic models fitted with lme4 (Bates et al., 2015) <doi:10.18637/jss.v067.i01>, and 2-level Gaussian and Bernoulli models fitted with brms (Bürkner, 2017) <doi:10.18637/jss.v080.i01>. Sanders, Konold, and Cheng (in press), "Model-based effect sizes for multilevel linear regression coefficients," Methodology: European Journal of Research Methods for the Behavioral and Social Sciences, describe the 2-level linear-model methods.

This package provides a supervised learning algorithm inputs a train set, and outputs a prediction function, which can be used on a test set. If each data point belongs to a subset (such as geographic region, year, etc), then how do we know if subsets are similar enough so that we can get accurate predictions on one subset, after training on Other subsets? And how do we know if training on All subsets would improve prediction accuracy, relative to training on the Same subset? SOAK, Same/Other/All K-fold cross-validation, <doi:10.1002/sam.70055> can be used to answer these questions, by fixing a test subset, training models on Same/Other/All subsets, and then comparing test error rates (Same versus Other and Same versus All). Also provides code for estimating how many train samples are required to get accurate predictions on a test set.

This package implements the Maki (2012) <doi:10.1016/j.econmod.2012.04.022> residual-based test for cointegration allowing for an unknown number of structural breaks. Breaks are located by a sequential procedure and the cointegrating residual is tested for a unit root with an augmented Dickey-Fuller (ADF) regression; the test statistic is the minimum ADF t-statistic over all candidate breaks. Four model specifications are supported (level shift, level shift with trend, regime shift, and regime shift with trend) and one to four regressors. The default engine reproduces the original GAUSS / tspdlib implementation, with an optional break rule following Maki (2012, Steps 2 and 4). The test runs for any feasible number of breaks; beyond the five tabulated by Maki, critical values can be simulated by his Monte-Carlo design. A two-panel diagnostic plot is provided via ggplot2'.

Diagnostics of list of codes based on concepts from the domains measurement and observation. This package works for data mapped to the Observational Medical Outcomes Partnership Common Data Model.

This package provides a complete toolkit to process the Munich ChronoType Questionnaire (MCTQ) for its three versions (standard, micro, and shift). MCTQ is a quantitative and validated tool to assess chronotypes using peoples sleep behavior, originally presented by Till Roenneberg, Anna Wirz-Justice, and Martha Merrow (2003, <doi:10.1177/0748730402239679>).

Function ModEstM() is the only one of this package, it estimates the modes of an empirical univariate distribution. It relies on the stats::density() function, even for input control. Due to very good performance of the density estimation, computation time is not an issue. The multiple modes are handled using dplyr::group_by(). For conditions and rates of convergences, see Eddy (1980) <doi:10.1214/aos/1176345080>.

Cooperative learning combines the usual squared error loss of predictions with an agreement penalty to encourage the predictions from different data views to agree. By varying the weight of the agreement penalty, we get a continuum of solutions that include the well-known early and late fusion approaches. Cooperative learning chooses the degree of agreement (or fusion) in an adaptive manner, using a validation set or cross-validation to estimate test set prediction error. In the setting of cooperative regularized linear regression, the method combines the lasso penalty with the agreement penalty (Ding, D., Li, S., Narasimhan, B., Tibshirani, R. (2021) <doi:10.1073/pnas.2202113119>).

Three main functions about analyzing massive data (missing observations are allowed) considered from multiple layers of categories are demonstrated. Flexible and diverse applications of the function parameters make the data analyses powerful.

Sampling and evaluation methods to apply Monetary Unit Sampling (or in older literature Dollar Unit Sampling) during an audit of financial statements.

An easy-to-use workflow that provides tools to create, update and fill literature matrices commonly used in research, specifically epidemiology and health sciences research. The project is born out of need as an easyâ toâ use tool for my research methods classes.

Implementation of a framework for cluster analysis with selection of the final number of clusters and an optional variable selection procedure. The package is designed to integrate the results of multiple imputed datasets while accounting for the uncertainty that the imputations introduce in the final results. In addition, the package can also be used for a cluster analysis of the complete cases of a single dataset. The package also includes specific methods to summarize and plot the results. The methods are described in Basagana et al. (2013) <doi:10.1093/aje/kws289>.

Generates Raven like matrices according to different rules and the response list associated to the matrix. The package can generate matrices composed of 4 or 9 cells, along with a response list of 11 elements (the correct response + 10 incorrect responses). The matrices can be generated according to both logical rules (i.e., the relationships between the elements in the matrix are manipulated to create the matrix) and visual-spatial rules (i.e., the visual or spatial characteristics of the elements are manipulated to generate the matrix). The graphical elements of this package are based on the DescTools package. This package has been developed within the PRIN2020 Project (Prot. 20209WKCLL) titled "Computerized, Adaptive and Personalized Assessment of Executive Functions and Fluid Intelligence" and founded by the Italian Ministry of Education and Research.

This package provides a guidance system for analysis with missing data. It incorporates expert, up-to-date methodology to help researchers choose the most appropriate analysis approach when some data are missing. You provide the available data and the assumed causal structure, including the likely causes of missing data. midoc will advise which analysis approaches can be used, and how best to perform them. midoc follows the framework for the treatment and reporting of missing data in observational studies (TARMOS). Lee et al (2021). <doi:10.1016/j.jclinepi.2021.01.008>.

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.

The current version (0.3.0) streamlines the underlying code, adds a feasibility check to maicWt and maxessWt', extends exmWt.2ipd with options target.ipd and method for one-sided MAIC weighting between two IPDs, and adds wtTrtDiff for the weighted treatment-effect difference with a Wald confidence interval based on Section 5 of Glimm & Yau (2026). The second version (0.2.0) contains implementation for exact matching which is an alternative to propensity score matching (see Glimm & Yau (2026) <doi:10.1080/19466315.2025.2507378>). The initial version (0.1.2) contains a collection of easy-to-implement tools for checking whether a MAIC can be conducted, as well as an alternative way of calculating weights (see Glimm & Yau (2022) <doi:10.1002/pst.2210>.).

This package provides a Shiny application to create thematic maps interactively, based on the mapsf package. Features include: a user-friendly interface to create and customize thematic maps without coding, support for various map types (choropleth, proportional symbols, etc.) and customization options (colors, legends, etc.), R code generation to ensure reproducibility and export options to save maps in different formats (PNG, SVG).

Quickly and conveniently create interactive visualisations of spatial data with or without background maps. Attributes of displayed features are fully queryable via pop-up windows. Additional functionality includes methods to visualise true- and false-color raster images and bounding boxes.