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".

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.

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.

The circadian period of a time series data is predicted and the statistical significance of the periodicity are calculated using the chi-square periodogram.

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

There is limited native support for external pointers in the R interface. This package provides some basic tools to verify, create and modify externalptr objects.

Helpers for transforming XML content into number of tables while preserving parent to child relationships.

This package provides support for transformations of numeric aggregates between statistical classifications (e.g. occupation or industry categorisations) using the Crossmaps framework. Implements classes for representing transformations between a source and target classification as graph structures, and methods for validating and applying crossmaps to transform data collected under the source classification into data indexed using the target classification codes. Documentation about the Crossmaps framework is provided in the included vignettes and in Huang (2024, <doi:10.48550/arXiv.2406.14163>).

This package provides a toolbox for meta-analysis. This package includes: 1,a robust multivariate meta-analysis of continuous or binary outcomes; 2, a bivariate Egger's test for detecting small study effects; 3, Galaxy Plot: A New Visualization Tool of Bivariate Meta-Analysis Studies; 4, a bivariate T&F method accounting for publication bias in bivariate meta-analysis, based on symmetry of the galaxy plot. Hong C. et al(2020) <doi:10.1093/aje/kwz286>, Chongliang L. et al(2020) <doi:10.1101/2020.07.27.20161562>.

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.

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.

Derivation tree operations are needed for implementing grammar-based genetic programming and grammatical evolution: Generating a random derivation trees of a context-free grammar of bounded depth, decoding a derivation tree, choosing a random node in a derivation tree, extracting a tree whose root is a specified node, and inserting a subtree into a derivation tree at a specified node. These operations are necessary for the initialization and for decoders of a random population of programs, as well as for implementing crossover and mutation operators. Depth-bounds are guaranteed by switching to a grammar without recursive production rules. For executing the examples, the package BNF is needed. The basic tree operations for generating, extracting, and inserting derivation trees as well as the conditions for guaranteeing complete derivation trees have been presented in Geyer-Schulz (1997, ISBN:978-3-7908-0830-X). The use of random integer vectors for the generation of derivation trees has been introduced in Ryan, C., Collins, J. J., and O'Neill, M. (1998) <doi:10.1007/BFb0055930> for grammatical evolution.

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

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>.

XML package for creating and reading and manipulating XML', with an object model based on Reference Classes'.

There are two new network metrics, RWC (random walk centrality) and CBET (counting betweenness). Also available are the normalized versions of those metrics. These measures of centrality and betweenness are particularly useful for the analysis of very dense weighted networks which include loops. Traditional measures do not work as well for those network characteristics. The main reference is DePaolis at al (2022) <doi:10.1007/s41109-022-00519-2>.

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.

Calculates a number of valuation adjustments including CVA, DVA, FBA, FCA, MVA and KVA. A two-way margin agreement has been implemented. For the KVA calculation four regulatory frameworks are supported: CEM, (simplified) SA-CCR, OEM and IMM. The probability of default is implied through the credit spreads curve. The package supports an exposure calculation based on SA-CCR which includes several trade types and a simulated path which is currently available only for Interest Rate Swaps. The latest regulatory capital charge methodologies have been implementing including BA-CVA & SA-CVA.

This package provides tools to analyze sex differences in omics data for complex diseases. It includes functions for differential expression analysis using the limma method <doi:10.1093/nar/gkv007>, interaction testing between sex and disease, pathway enrichment with clusterProfiler <doi:10.1089/omi.2011.0118>, and gene regulatory network (GRN) construction and analysis using igraph'. The package enables a reproducible workflow from raw data processing to biological interpretation.

This package implements the bootstrap slope heterogeneity test for panel data based on Blomquist and Westerlund (2015) <doi:10.1007/s00181-015-0978-z>. Tests the null hypothesis that slope coefficients are homogeneous across cross-sectional units. Provides both standard and adjusted Delta statistics with bootstrap p-values. Supports partialling out of control variables and cross-sectional averages for dealing with cross-sectional dependence.

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.

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.

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>.