This package provides a robust Partial Least-Squares (PLS) method is implemented that is robust to outliers in the residuals as well as to leverage points. A specific weighting scheme is applied which avoids iterations, and leads to a highly efficient robust PLS estimator.

This package provides programmatic access to Glottography, an online repository of geospatial speaker-area polygons for the world's languages. The package allows users to list available datasets, download and install them, and load speaker-area polygons as standard spatial sf objects in R. Data are sourced from either the Glottography organization on GitHub <https://github.com/Glottography> or the Glottography community on Zenodo <https://zenodo.org/communities/glottography>. Based on Ranacher et al. (2026) <doi:10.5334/johd.459>.

This package provides portable access from R to biomedical image processing toolbox ANTs by Avants et al. (2009) <doi:10.54294/uvnhin> via seamless integration with the Python implementation ANTsPy'. Allows biomedical images to be processed in Python and analyzed in R', and vice versa via shared memory. See citation("rpyANTs") for more reference information.

Fast design of risk parity portfolios for financial investment. The goal of the risk parity portfolio formulation is to equalize or distribute the risk contributions of the different assets, which is missing if we simply consider the overall volatility of the portfolio as in the mean-variance Markowitz portfolio. In addition to the vanilla formulation, where the risk contributions are perfectly equalized subject to no shortselling and budget constraints, many other formulations are considered that allow for box constraints and shortselling, as well as the inclusion of additional objectives like the expected return and overall variance. See vignette for a detailed documentation and comparison, with several illustrative examples. The package is based on the papers: Y. Feng, and D. P. Palomar (2015). SCRIP: Successive Convex Optimization Methods for Risk Parity Portfolio Design. IEEE Trans. on Signal Processing, vol. 63, no. 19, pp. 5285-5300. <doi:10.1109/TSP.2015.2452219>. F. Spinu (2013), An Algorithm for Computing Risk Parity Weights. <doi:10.2139/ssrn.2297383>. T. Griveau-Billion, J. Richard, and T. Roncalli (2013). A fast algorithm for computing High-dimensional risk parity portfolios. <arXiv:1311.4057>.

This package provides a plug in for using WinEdt as an editor for R.

Compute an exact CI for the population mean under a random effects model. The routines implement the algorithm described in Michael, Thronton, Xie, and Tian (2017) <https://haben-michael.github.io/research/Exact_Inference_Meta.pdf>.

Uses Elsevier Scopus API <https://dev.elsevier.com/sc_apis.html> to download information about authors and their citations.

This package provides functions for the calibration of radiocarbon dates, as well as options to calculate different radiocarbon-related timescales (cal BP, cal BC/AD, C14 age, F14C, pMC, D14C) and estimating the effects of contamination or local reservoir offsets (Reimer and Reimer 2001 <doi:10.1017/S0033822200038339>). Supporting publication: Blaauw, M., Reimer, P.J., in press. An open-source toolkit for radiocarbon dating and calibration. Radiocarbon. The methods follow long-established recommendations such as Stuiver and Polach (1977) <doi:10.1017/S0033822200003672> and Reimer et al. (2004) <doi:10.1017/S0033822200033154>. This package uses the calibration curves from the data package rintcal'.

Collection of portable choice dialog widgets.

This package provides the Jester Dataset for package recommenderlab.

An expansion of R's stats random wishart matrix generation. This package allows the user to generate singular, Uhlig and Harald (1994) <doi:10.1214/aos/1176325375>, and pseudo wishart, Diaz-Garcia, et al.(1997) <doi:10.1006/jmva.1997.1689>, matrices. In addition the user can generate wishart matrices with fractional degrees of freedom, Adhikari (2008) <doi:10.1061/(ASCE)0733-9399(2008)134:12(1029)>, commonly used in volatility modeling. Users can also use this package to create random covariance matrices.

This package provides a collection of functions to simulate luminescence signals in quartz and Al2O3 based on published models.

Extend Rasch and Item Response Theory (IRT) analyses by providing tools for post-processing the output from five major IRT packages (i.e., eRm', psychotools', ltm', mirt', and TAM'). The current version provides the plotPIccc() function, which extracts from the return object of the originating package all information required to draw an extended Person-Item-Map (PIccc), showing any combination of * category characteristic curves (CCCs), * threshold characteristic curves (TCCs), * item characteristic curves (ICCs), * category information functions (CIFs), * item information functions (IIFs), * test information function (TIF), and the * standard error curve (S.E.). for uni- and multidimensional models (as far as supported by each package). It allows for selecting dimensions, items, and categories to plot and offers numerous options to adapt the output. The return object contains all calculated values for further processing.

Partitions the phenotypic variance of a plastic trait, studied through its reaction norm. The variance partition distinguishes between the variance arising from the average shape of the reaction norms (V_Plas) and the (additive) genetic variance . The latter is itself separated into an environment-blind component (V_G/V_A) and the component arising from plasticity (V_GxE/V_AxE). The package also provides a way to further partition V_Plas into aspects (slope/curvature) of the shape of the average reaction norm (pi-decomposition) and partition V_Add (gamma-decomposition) and V_AxE (iota-decomposition) into the impact of genetic variation in the reaction norm parameters. Reference: de Villemereuil & Chevin (2025) <doi:10.32942/X2NC8B>.

Reads in sample description and slide description files and annotates the expression values taken from GenePix results files (text file format used by many microarray scanner and software providers). After normalization data can be visualized as boxplot, heatmap or dotplot.

The rkafkajars package collects all the external jars required for the rkafka package.

This package implements methods described by the paper Robins and Tsiatis (1991) <DOI:10.1080/03610929108830654>. These use g-estimation to estimate the causal effect of a treatment in a two-armed randomised control trial where non-compliance exists and is measured, under an assumption of an accelerated failure time model and no unmeasured confounders.

An implementation of an algorithm family for continuous optimization called memetic algorithms with local search chains (MA-LS-Chains), as proposed in Molina et al. (2010) <doi:10.1162/evco.2010.18.1.18102> and Molina et al. (2011) <doi:10.1007/s00500-010-0647-2>. Rmalschains is further discussed in Bergmeir et al. (2016) <doi:10.18637/jss.v075.i04>. Memetic algorithms are hybridizations of genetic algorithms with local search methods. They are especially suited for continuous optimization.

This package provides a method for performing joint registration and functional principal component analysis for curves (functional data) that are generated from exponential family distributions. This mainly implements the algorithms described in Wrobel et al. (2019) <doi:10.1111/biom.12963> and further adapts them to potentially incomplete curves where (some) curves are not observed from the beginning and/or until the end of the common domain. Curve registration can be used to better understand patterns in functional data by separating curves into phase and amplitude variability. This software handles both binary and continuous functional data, and is especially applicable in accelerometry and wearable technology.

Spatial Dispersion Index (SDI) is a generalized measurement index, or rather a family of indices to evaluate spatial dispersion of movements/flows in a network in a problem neutral way as described in: Gencer (2023) <doi:10.1007/s12061-023-09545-8>. This package computes and optionally visualizes this index with minimal hassle.

R utilities for gff files, either general feature format (GFF3) or gene transfer format (GTF) formatted files. This package includes functions for producing summary stats, check for consistency and sorting errors, conversion from GTF to GFF3 format, file sorting, visualization and plotting of feature hierarchy, and exporting user defined feature subsets to SAF format. This tool was developed by the BioinfoGP core facility at CNB-CSIC.

Calculates risk differences (or prevalence differences for cross-sectional data) and Number Needed to Treat (NNT) using generalized linear models with automatic link function selection. Provides robust model fitting with fallback methods, support for stratification and adjustment variables, inverse probability of treatment weighting (IPTW) for causal inference with NNT calculations, and publication-ready output formatting. Handles model convergence issues gracefully and provides confidence intervals using multiple approaches. Methods are based on approaches described in Mark W. Donoghoe and Ian C. Marschner (2018) "logbin: An R Package for Relative Risk Regression Using the Log-Binomial Model" <doi:10.18637/jss.v086.i09> for robust GLM fitting, Peter C. Austin (2011) "An Introduction to Propensity Score Methods for Reducing the Effects of Confounding in Observational Studies" <doi:10.1080/00273171.2011.568786> for IPTW methods, and standard epidemiological methods for risk difference estimation as described in Kenneth J. Rothman, Sander Greenland and Timothy L. Lash (2008, ISBN:9780781755641) "Modern Epidemiology".

We propose a general ensemble classification framework, RaSE algorithm, for the sparse classification problem. In RaSE algorithm, for each weak learner, some random subspaces are generated and the optimal one is chosen to train the model on the basis of some criterion. To be adapted to the problem, a novel criterion, ratio information criterion (RIC) is put up with based on Kullback-Leibler divergence. Besides minimizing RIC, multiple criteria can be applied, for instance, minimizing extended Bayesian information criterion (eBIC), minimizing training error, minimizing the validation error, minimizing the cross-validation error, minimizing leave-one-out error. There are various choices of base classifier, for instance, linear discriminant analysis, quadratic discriminant analysis, k-nearest neighbour, logistic regression, decision trees, random forest, support vector machines. RaSE algorithm can also be applied to do feature ranking, providing us the importance of each feature based on the selected percentage in multiple subspaces. RaSE framework can be extended to the general prediction framework, including both classification and regression. We can use the selected percentages of variables for variable screening. The latest version added the variable screening function for both regression and classification problems.

Recursive display of names and paths of all the items nested within sublists of a list object.