Based on STATA xtsum command, it is used to compute summary statistics for a panel data set. It generates overall, between-group, and within-group statistics for specified variables in a panel data set, as presented in S. Porter (2023) <https://stephenporter.org/files/xtsum_handout.pdf>, StataCorp (2023) <https://www.stata.com/manuals/xtxtsum.pdf>.

This package implements an iterative mean-variance panel regression estimator that allows both the mean and variance of the dependent variable to be functions of covariates. The method alternates between estimating a mean equation (using generalized linear models with Gaussian family) and a variance equation (using generalized linear models with Gamma family on squared within-group residuals) until convergence. Based on the methodology in Mooi-Reci and Liao (2025) <doi:10.1093/esr/jcae052>.

Allows to provide live interpretations and explanations of statistical functions in R. These interpretations and explanations are shown when the explained function is called by the user. They can interact with the values of the explained function's actual results to offer relevant, meaningful insights. The xplain interpretations and explanations are based on an easy-to-use XML format that allows to include R code to interact with the returns of the explained function.

The xtdml package implements partially linear panel regression (PLPR) models with high-dimensional confounding variables and an exogenous treatment variable within the double machine learning framework. The package is used to estimate the structural parameter (treatment effect) in static panel data models with fixed effects using the approaches established in Clarke and Polselli (2025) <doi:10.1093/ectj/utaf011>. xtdml is built on the object-oriented package DoubleML (Bach et al., 2024) <doi:10.18637/jss.v108.i03> using the mlr3 ecosystem.

The xlsxjars package collects all the external jars required for the xlxs package. This release corresponds to POI 3.13.

This package provides tools to build CDISC compliant data sets and check for CDISC compliance.

Extras and extensions for xaringan slides. Navigate your slides with tile view. Make your slides editable, live! Announce slide changes with subtle tones. Animate slide transitions with animate.css'. Add tabbed panels to slides with panelset'. Use the Tachyons CSS utility toolkit for rapid slide development. Scribble on your slides. Add a copy button to your code chunks with clipboard'. Add a logo or top or bottom banner to every slide. Broadcast slides to stay in sync with remote viewers. Include yourself in your slides with webcam'. Plus a whole lot more!

We consider the problem where we observe k vectors (possibly of different lengths), each representing an independent multinomial random vector. For a given function that takes in the concatenated vector of multinomial probabilities and outputs a real number, this is a Monte Carlo estimation procedure of an exact p-value and confidence interval. The resulting inference is valid even in small samples, when the parameter is on the boundary, and when the function is not differentiable at the parameter value, all situations where asymptotic methods and the bootstrap would fail. For more details see Sachs, Fay, and Gabriel (2025) <doi:10.48550/arXiv.2406.19141>.

Institutional performance assessment remains a key challenge to a multitude of stakeholders. Existing indicators such as h-type indicators, g-type indicators, and many others do not reflect expertise of institutions that defines their research portfolio. The package offers functionality to compute and visualise two novel indices: the x-index and the xd-index. The x-index evaluates an institution's scholarly expertise within a specific discipline or field, while the xd-index provides a broader assessment of overall scholarly expertise considering an institution's publication pattern and strengths across coarse thematic areas. These indices offer a nuanced understanding of institutional research capabilities, aiding stakeholders in research management and resource allocation decisions. Lathabai, H.H., Nandy, A., and Singh, V.K. (2021) <doi:10.1007/s11192-021-04188-3>. Nandy, A., Lathabai, H.H., and Singh, V.K. (2023) <doi:10.5281/zenodo.8305585>. This package provides the h-, g-, x-, xd-indices, and their variants for use with standard format of Web of Science (WoS) scrapped datasets.

Import an XML document with nested object structures and convert it into a relational data model. The result is a set of R dataframes with foreign key relationships. The data model and the data can be exported as SQL code of different SQL flavors.

This collection of gene representation-independent functions implements the population layer of extended evolutionary and genetic algorithms and its support for the R-package xega <https://CRAN.R-project.org/package=xega>. The population layer consists of functions for initializing, logging, observing, evaluating a population of genes, as well as of computing the next population. For parallel evaluation of a population of genes 4 execution models - named Sequential, MultiCore, FutureApply, and Cluster - are provided. They are implemented by configuring the lapply() function. The execution model FutureApply can be externally configured as recommended by Bengtsson (2021) <doi:10.32614/RJ-2021-048>. Configurable acceptance rules and cooling schedules (see Kirkpatrick, S., Gelatt, C. D. J, and Vecchi, M. P. (1983) <doi:10.1126/science.220.4598.671>, and Aarts, E., and Korst, J. (1989, ISBN:0-471-92146-7) offer simulated annealing or greedy randomized approximate search procedure elements. Adaptive crossover and mutation rates depending on population statistics generalize the approach of Stanhope, S. A. and Daida, J. M. (1996, ISBN:0-18-201-031-7). For xega''s architecture, see Geyer-Schulz, A. (2025) <doi:10.5445/IR/1000187255>.

Fits hierarchical regularized regression models to incorporate potentially informative external data, Weaver and Lewinger (2019) <doi:10.21105/joss.01761>. Utilizes coordinate descent to efficiently fit regularized regression models both with and without external information with the most common penalties used in practice (i.e. ridge, lasso, elastic net). Support for standard R matrices, sparse matrices and big.matrix objects.

Computes robust association measures that do not presuppose linearity. The xi correlation (xicor) is based on cross correlation between ranked increments. The reference for the methods implemented here is Chatterjee, Sourav (2020) <arXiv:1909.10140> This package includes the Galton peas example.

This package provides a simple XML tree parser/generator. It includes functions to read XML files into R objects, get information out of and into nodes, and write R objects back to XML code. It's not as powerful as the XML package and doesn't aim to be, but for simple XML handling it could be useful. It was originally developed for the R GUI and IDE RKWard <https://rkward.kde.org>, to make plugin development easier.

This package implements a probabilistic approach to time series forecasting combining XGBoost regression with conformal inference methods. The package provides functionality for generating predictive distributions, evaluating uncertainty, and optimizing hyperparameters using Bayesian, coarse-to-fine, or random search strategies.

This package provides a suite of psychometric analysis tools for research and operation, including: (1) computation of probability, information, and likelihood for the 3PL, GPCM, and GRM; (2) parameter estimation using joint or marginal likelihood estimation method; (3) simulation of computerized adaptive testing using built-in or customized algorithms; (4) assembly and simulation of multistage testing. The full documentation and tutorials are at <https://github.com/xluo11/xxIRT>.

This package contains functions to identify tree-ring borders based on X-ray micro-density profiles and a Graphical User Interface (GUI) to visualize density profiles and correct tree-ring borders. Campelo F, Mayer K, Grabner M. (2019) <doi:10.1016/j.dendro.2018.11.002>.

This is a set of statistical quality control functions, that allows plotting control charts and its iterations, process capability for variable and attribute control, highlighting the xrs_gr() function, like a first iteration for variable chart, meanwhile the we_rules() function detects non random patterns in sample.

This package provides tools for interactive data exploration built using shiny'. Includes apps for descriptive statistics, visualizing probability distributions, inferential statistics, linear regression, logistic regression and RFM analysis.

Parse entire folders of non-rectangular xlsx files into a single rectangular and tidy data.frame based on a custom template file defining the column names of the output.

Adding some at-present missing functionality, or functions unlikely to be added to the base xpose package. This includes some diagnostic plots that have been missing in translation from xpose4', but also some useful features that truly extend the capabilities of what can be done with xpose'. These extensions include the concept of a set of xpose objects, and diagnostics for likelihood-based models.

XMRs combine X-Bar control charts and Moving Range control charts. These functions also will recalculate the reference lines when significant change has occurred.

An implementation of the representation-dependent gene level operations of grammar-based genetic programming with genes which are derivation trees of a context-free grammar: Initialization of a gene with a complete random derivation tree, decoding of a derivation tree. Crossover is implemented by exchanging subtrees. Depth-bounds for the minimal and the maximal depth of the roots of the subtrees exchanged by crossover can be set. Mutation is implemented by replacing a subtree by a random subtree. The depth of the random subtree and the insertion node are configurable. For details, see Geyer-Schulz (1997, ISBN:978-3-7908-0830-X).

Support for interfaces from R to other languages, built around a class for evaluators and a combination of functions, classes and methods for communication. Will be used through a specific language interface package. Described in the book "Extending R".