This package provides a copula based clustering algorithm that finds clusters according to the complex multivariate dependence structure of the data generating process. The updated version of the algorithm is described in Di Lascio, F.M.L. and Giannerini, S. (2019). "Clustering dependent observations with copula functions". Statistical Papers, 60, p.35-51. <doi:10.1007/s00362-016-0822-3>.

This package provides a Shiny application and supporting functions for the design and analysis of consistency tests based on Kappa statistic with categorical responses. Wraps irr and kappaSize packages.

Combines multiple functions that automate and simplify methods commonly employed in cross-cultural psychology, providing a unified analysis approach for measurement invariance testing, effect sizes for differential item functioning, factor congruence and multi-group reliability. Methods follow Fischer and Karl (2019) <doi:10.3389/fpsyg.2019.01507> and Gunn, Grimm and Edwards (2020) <doi:10.1080/10705511.2019.1689507>.

Tree-based classification and soft-clustering method for preference rankings, with tools for external validation of fuzzy clustering, and Kemeny-equivalent augmented unfolding. It contains the recursive partitioning algorithm for preference rankings, non-parametric tree-based method for a matrix of preference rankings as a response variable. It contains also the distribution-free soft clustering method for preference rankings, namely the K-median cluster component analysis (CCA). The package depends on the ConsRank R package. Options for validate the tree-based method are both test-set procedure and V-fold cross validation. The package contains the routines to compute the adjusted concordance index (a fuzzy version of the adjusted rand index) and the normalized degree of concordance (the corresponding fuzzy version of the rand index). The package also contains routines to perform the Kemeny-equivalent augmented unfolding. The mds endine is the function sacofSym from the package smacof'. Essential references: D'Ambrosio, A., Vera, J.F., and Heiser, W.J. (2021) <doi:10.1080/00273171.2021.1899892>; D'Ambrosio, A., Amodio, S., Iorio, C., Pandolfo, G., and Siciliano, R. (2021) <doi:10.1007/s00357-020-09367-0>; D'Ambrosio, A., and Heiser, W.J. (2019) <doi:10.1007/s41237-018-0069-5>; D'Ambrosio, A., and Heiser W.J. (2016) <doi:10.1007/s11336-016-9505-1>; Hullermeier, E., Rifqi, M., Henzgen, S., and Senge, R. (2012) <doi:10.1109/TFUZZ.2011.2179303>; Marden, J.J. <ISBN:0412995212>.

We present corto (Correlation Tool), a simple package to infer gene regulatory networks and visualize master regulators from gene expression data using DPI (Data Processing Inequality) and bootstrapping to recover edges. An initial step is performed to calculate all significant edges between a list of source nodes (centroids) and target genes. Then all triplets containing two centroids and one target are tested in a DPI step which removes edges. A bootstrapping process then calculates the robustness of the network, eventually re-adding edges previously removed by DPI. The algorithm has been optimized to run outside a computing cluster, using a fast correlation implementation. The package finally provides functions to calculate network enrichment analysis from RNA-Seq and ATAC-Seq signatures as described in the article by Giorgi lab (2020) <doi:10.1093/bioinformatics/btaa223>.

Model building, surrogate model based optimization and Efficient Global Optimization in combinatorial or mixed search spaces.

Multidimensional scaling (MDS) methods that aim at pronouncing the clustered appearance of the configuration (Rusch, Mair & Hornik, 2021, <doi:10.1080/10618600.2020.1869027>). They achieve this by transforming proximities/distances with explicit power functions and penalizing the fitting criterion with a clusteredness index, the OPTICS Cordillera (Rusch, Hornik & Mair, 2018, <doi:10.1080/10618600.2017.1349664>). There are two variants: One for finding the configuration directly (COPS-C) with given explicit power transformations and implicit ratio, interval and non-metric optimal scaling transformations (Borg & Groenen, 2005, ISBN:978-0-387-28981-6), and one for using the augmented fitting criterion to find optimal hyperparameters for the explicit transformations (P-COPS). The package contains various functions, wrappers, methods and classes for fitting, plotting and displaying a large number of different MDS models (most of the functionality in smacofx) in the COPS framework. The package further contains a function for pattern search optimization, the ``Adaptive Luus-Jaakola Algorithm (Rusch, Mair & Hornik, 2021,<doi:10.1080/10618600.2020.1869027>) and a functions to calculate the phi-distances for count data or histograms.

The Codemeta Project defines a JSON-LD format for describing software metadata, as detailed at <https://codemeta.github.io>. This package provides core utilities to generate this metadata with a minimum of dependencies.

This package provides tools for trial-based economic evaluation of healthcare interventions. Computes and visualizes incremental cost-effectiveness ratios, cost-effectiveness acceptability curves, cost-effectiveness planes, net monetary benefit tables, and one-way deterministic sensitivity analyses. Supports cost-utility analyses using observed summaries and non-parametric bootstrap uncertainty.

Significance test of Spearman's Rho or Kendall's Tau between short-range dependent random variables.

Apply and visualize conditional formatting to data frames in R. It renders a data frame with cells formatted according to criteria defined by rules, using a tidy evaluation syntax. The table is printed either opening a web browser or within the RStudio viewer if available. The conditional formatting rules allow to highlight cells matching a condition or add a gradient background to a given column. This package supports both HTML and LaTeX outputs in knitr reports, and exporting to an xlsx file.

Fits the Cox proportional hazards model using a single descending-order pass per Newton-Raphson iteration. Peak RAM is O(p^2) regardless of the number of rows, making it suitable for datasets that do not fit in memory. Produces identical coefficients to survival::coxph() with Efron tie correction.

This package provides methods for interpretable machine learning with an emphasis on local feature importance estimation. The package implements the CLIQUE framework for computing observation-level importance values. Additional tools are included for visualizing multi-class partial dependence relationships across predictors and response classes.

This package provides function declarations and inline function definitions that facilitate communication between R and the Armadillo C++ library for linear algebra and scientific computing. This implementation is detailed in Vargas Sepulveda and Schneider Malamud (2024) <doi:10.1016/j.softx.2025.102087>.

Interface to the Google Cloud Machine Learning Platform <https://cloud.google.com/vertex-ai>, which provides cloud tools for training machine learning models.

Core functions for simulating quantities of interest from generalised linear models (GLM). This package will form the backbone of a series of other packages that improve the interpretation of GLM estimates.

Datasets for the book entitled "Modelling Survival Data in Medical Research" by Collett (2023) <doi:10.1201/9781003282525>. The datasets provide extensive examples of time-to-event data.

This package provides a one-stop shop for intuitive and dependency-free color and palette creation and modification. Includes palettes and functionality from popular packages such as viridis', RColorBrewer', and base R grDevices', as well as ggplot2 plot bindings. Users can generate perceptually uniform and colorblind-friendly palettes, adjust palettes in HSL and RGB color spaces, map color gradients to value ranges, and create color-generating functions.

Estimate different types of cluster robust standard errors (CR0, CR1, CR2) with degrees of freedom adjustments. Standard errors are computed based on Liang and Zeger (1986) <doi:10.1093/biomet/73.1.13> and Bell and McCaffrey <https://www150.statcan.gc.ca/n1/en/pub/12-001-x/2002002/article/9058-eng.pdf?st=NxMjN1YZ>. Functions used in Huang and Li <doi:10.3758/s13428-021-01627-0>, Huang, Wiedermann', and Zhang <doi:10.1080/00273171.2022.2077290>, and Huang, Zhang', and Li (forthcoming: Journal of Research on Educational Effectiveness).

This package provides a wrapper around the new cleaner package, that allows data cleaning functions for classes logical', factor', numeric', character', currency and Date to make data cleaning fast and easy. Relying on very few dependencies, it provides smart guessing, but with user options to override anything if needed.

It is devoted to Cramer-von Mises goodness-of-fit tests. It implements three statistical methods based on Cramer-von Mises statistics to estimate and test a regression model.

Plots the coefficients from model objects. This very quickly shows the user the point estimates and confidence intervals for fitted models.

Model soil gas fluxes with the Flux-Gradient Method. It includes functions for data handling, a forward and an inverse model for flux modeling and methods for calibration and uncertainty estimation. For more details see Gartiser et al. (2025a) <doi:10.21105/joss.08094> and Gartiser et al. (2025b) <doi:10.1111/ejss.70126>.

Calculate the distance between single-arm observational studies using covariate information to remove heterogeneity in Network Meta-Analysis (NMA) of randomized clinical trials. Facilitate the inclusion of observational data in NMA, enhancing the comprehensiveness and robustness of comparative effectiveness research. Schmitz (2018) <doi:10.1186/s12874-018-0509-7>.