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>.

This package implements the GADGET (Generalized Additive Decomposition of Global EffecTs) algorithm for interpretable machine learning. The package recursively partitions the feature space to minimize heterogeneity of feature effects (e.g., Accumulated Local Effects or Partial Dependence), producing a tree of regions where effects are more stable. It supports both ALE and PD strategies, works with mlr3 learners and provides visualization of the interaction tree and regional effect plots. The method is described in Herbinger, J., Wright, M. N., Nagler, T., Bischl, B., and Casalicchio, G. (2024), "Decomposing Global Feature Effects Based on Feature Interactions" <https://jmlr.org/papers/volume25/23-0699/23-0699.pdf>.

This package implements the interactive fixed effects ('IFE') panel estimator of Bai (2009) <doi:10.3982/ECTA6135> for balanced and unbalanced panels, with optional additive unit and/or time fixed effects. Provides analytical standard errors ('homoskedastic', HC1 heteroskedasticity-robust, cluster-robust by unit, and heteroskedasticity- and autocorrelation- consistent), together with asymptotic incidental-parameter bias correction for large panels, including a dynamic extension for predetermined (lagged-dependent) regressors following Moon and Weidner (2017) <doi:10.1017/S0266466615000328>. The number of factors is chosen by information criteria (Bai and Ng 2002 <doi:10.1111/1468-0262.00273>) or by singular value thresholding. Unbalanced panels are handled by an expectation-maximisation algorithm with nuclear-norm-regularised initialisation, with estimation, analytical inference, and bias correction following Su, Wang and Wang (2025) <doi:10.2139/ssrn.5177283> and building on the matrix-completion and missing-data factor analysis of Bai and Ng (2021) <doi:10.1080/01621459.2021.1967163>. All computations use base R only, with no external dependencies.

This package provides tools to analyze datasets previous to any statistical modeling. Has various functions designed to find inconsistencies and understanding the distribution of the data.

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.

This package provides tools to download and merge data files on sub-national conflict, violence and protests from <http://www.x-sub.org>.

Provide R functions to read/write/format Excel 2007 and Excel 97/2000/XP/2003 file formats.

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

Fits relative survival regression models with or without proportional excess hazards and with the additional possibility to correct for background mortality by one or more parameter(s). These models are relevant when the observed mortality in the studied group is not comparable to that of the general population or in population-based studies where the available life tables used for net survival estimation are insufficiently stratified. In the latter case, the proposed model by Touraine et al. (2020) <doi:10.1177/0962280218823234> can be used. The user can also fit a model that relaxes the proportional expected hazards assumption considered in the Touraine et al. excess hazard model. This extension was proposed by Mba et al. (2020) <doi:10.1186/s12874-020-01139-z> to allow non-proportional effects of the additional variable on the general population mortality. In non-population-based studies, researchers can identify non-comparability source of bias in terms of expected mortality of selected individuals. An excess hazard model correcting this selection bias is presented in Goungounga et al. (2019) <doi:10.1186/s12874-019-0747-3>. This class of model with a random effect at the cluster level on excess hazard is presented in Goungounga et al. (2023) <doi:10.1002/bimj.202100210>.

Representation-dependent gene-level operations for genetic and evolutionary algorithms with real-coded genes used in the R-package xega <https://CRAN.R-project.org/package=xega> are collected in this package. The common feature of the gene operations is that all of them are useful for derivation-free optimization algorithms. At the moment the package implements initialization, mutation, crossover, and replication operations for differential evolution as described in Price, Kenneth V., Storn, Rainer M. and Lampinen, Jouni A. (2005) <doi:10.1007/3-540-31306-0>. In addition, several (more recent) methods for determining the scale factor are provided. For xega''s architecture, see Geyer-Schulz, A. (2025) <doi:10.5445/IR/1000187255>.

Draw images easily, set up a plot with an image, specify where that image should be placed. Image plot by default reflects the index of the image data itself, or can be specified in simple extent terms xmin,xmax,ymin,ymax'. Numeric matrices, integer arrays, byte arrays, character arrays, and native rasters are (or will be) supported. A combination of image() and rasterImage() from the graphics package with their good features in one place.

Fits flexible maximum likelihood regression models supporting censored, interval, and hybrid continuous/dichotomous data. Provides explicit analytic and numerical gradient computation, random intercept models via Gauss-Hermite quadrature, and multiple distribution families.

Extremely fast hashing of R objects using xxHash'. R objects are hashed via the standard serialization mechanism in R. Raw byte vectors and strings can be handled directly for compatibility with hashes created on other systems. This implementation is a wrapper around the xxHash C library which is available from <https://github.com/Cyan4973/xxHash>.

Read and write XES Files to create event log objects used by the bupaR framework. XES (Extensible Event Stream) is the `IEEE` standard for storing and sharing event data (see <http://standards.ieee.org/findstds/standard/1849-2016.html> for more info).

This package implements panel cointegration tests allowing for structural breaks and cross-section dependence following the methodology of Banerjee and Carrion-i-Silvestre (2015) <doi:10.1002/jae.2348>. The package provides iterative factor-break estimation, individual ADF tests on defactored residuals, standardized panel test statistics, and the Bai and Ng (2004) <doi:10.1111/j.1468-0262.2004.00528.x> MQ test for identifying common stochastic trends. Supports five model specifications with varying deterministic components and break structures.

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.

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.

Converts an XLSForm (survey in Excel') into a well-structured Word document, including sections, skip logic, options, and question labels. Designed to support survey documentation, training materials, and data collection workflows. The package was developed based on field experience with XLSForm and humanitarian operations, aiming to streamline documentation and enhance training efficiency.

This package provides comprehensive functionality to read, write and format Excel data.

Computes the Actuarial Climate Index (ACI) and its components (temperature, precipitation, drought, wind, sea level) from gridded climate data ('NetCDF') and tide gauge records. Implements the methodology described in Garrido, Milhaud & Olympio (2025) <https://hal.science/hal-04491982v2> for a French/European actuarial climate index, building on the American Academy of Actuaries framework, to any country in the world.

High-level functions to render LaTeX fragments in plots, including as labels and data symbols in ggplot2 plots, plus low-level functions to author LaTeX fragments (to produce LaTeX documents), typeset LaTeX documents (to produce DVI files), read DVI files (to produce "DVI" objects), and render "DVI" objects.

The XKCD color survey asked participants to name colours. Randall Munroe published the top thousand(roughly) names and their sRGB hex values. This package lets you use them.

Helps systematize and ease the process of building unit tests with the testthat package by providing tools for generating expectations.

This package implements fixed effects estimators for time-invariant variables in panel data models. Provides three estimation methods: FEVD (Fixed Effects Vector Decomposition) from Plumper and Troeger (2007) <doi:10.1093/pan/mpm002>, and FEF (Fixed Effects Filtered) and FEF-IV (instrumental variables variant) from Pesaran and Zhou (2018) <doi:10.1080/07474938.2016.1222225>. All methods use the correct Pesaran-Zhou variance estimators that account for generated regressor uncertainty, avoiding the size distortions documented in the literature.