This package provides a Python interface structured according to the general form described in package XR and in the book "Extending R".

This tool enables in-database scoring of XGBoost models built in R, by translating trained model objects into SQL query. XGBoost <https://github.com/dmlc/xgboost> provides parallel tree boosting (also known as gradient boosting machine, or GBM) algorithms in a highly efficient, flexible and portable way. GBM algorithm is introduced by Friedman (2001) <doi:10.1214/aos/1013203451>, and more details on XGBoost can be found in Chen & Guestrin (2016) <doi:10.1145/2939672.2939785>.

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

This package provides tools for reading, parsing and visualizing simulation data stored in xvg'/'xpm file formats (commonly generated by GROMACS molecular dynamics software). Streamlines post-processing and analysis of molecular dynamics ('MD') simulation outputs, enabling efficient exploration of molecular stability and conformational changes. Supports import of trajectory metrics ('RMSD', energy, temperature) and creation of publication-ready visualizations through integration with ggplot2'.

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>).

Datasets and definitions of generic functions used in dependencies of the xergm package.

The X13-ARIMA-SEATS <https://www.census.gov/data/software/x13as.html> methodology and software is a widely used software and developed by the US Census Bureau. It can be accessed from R with this package and X13-ARIMA-SEATS binaries are provided by the R package x13binary'.

This collection of gene representation-independent mechanisms for evolutionary and genetic algorithms contains four groups of functions: First, functions for selecting a gene in a population of genes according to its fitness value and for adaptive scaling of the fitness values as well as for performance optimization and measurement offer several variants for implementing the survival of the fittest. Second, evaluation functions for deterministic functions avoid recomputation. Evaluation of stochastic functions incrementally improve the estimation of the mean and variance of fitness values at almost no additional cost. Evaluation functions for gene repair handle error-correcting decoders. Third, timing and counting functions for profiling the algorithm pipeline are provided to assess bottlenecks in the algorithms. Fourth, a small collection of problem environments for function optimization, combinatorial optimization, and grammar-based genetic programming and grammatical evolution is provided for tutorial examples. The methods in the package are described by the following references: Baker, James E. (1987, ISBN:978-08058-0158-8), De Jong, Kenneth A. (1975) <https://deepblue.lib.umich.edu/handle/2027.42/4507>, Geyer-Schulz, Andreas (1997, ISBN:978-3-7908-0830-X), Grefenstette, John J. (1987, ISBN:978-08058-0158-8), Grefenstette, John J. and Baker, James E. (1989, ISBN:1-55860-066-3), Holland, John (1975, ISBN:0-472-08460-7), Lau, H. T. (1986) <doi:10.1007/978-3-642-61649-5>, Price, Kenneth V., Storn, Rainer M. and Lampinen, Jouni A. (2005) <doi:10.1007/3-540-31306-0>, Reynolds, J. C. (1993) <doi:10.1007/BF01019459>, Schaffer, J. David (1989, ISBN:1-55860-066-3), Wenstop, Fred (1980) <doi:10.1016/0165-0114(80)90031-7>, Whitley, Darrell (1989, ISBN:1-55860-066-3), Wickham, Hadley (2019, ISBN:978-815384571).

Extrema-weighted feature extraction for varying length functional data. Functional data analysis method that performs dimensionality reduction based on predefined features and allows for quantile weighting. Method implemented as presented in van den Boom et al. (2018) <doi:10.1093/bioinformatics/bty120>.

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

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.

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>.

Implementation of a scalable, highly configurable, and e(x)tended architecture for (e)volutionary and (g)enetic (a)lgorithms. Multiple representations (binary, real-coded, permutation, and derivation-tree), a rich collection of genetic operators, as well as an extended processing pipeline are provided for genetic algorithms (Goldberg, D. E. (1989, ISBN:0-201-15767-5)), differential evolution (Price, Kenneth V., Storn, Rainer M. and Lampinen, Jouni A. (2005) <doi:10.1007/3-540-31306-0>), simulated annealing (Aarts, E., and Korst, J. (1989, ISBN:0-471-92146-7)), grammar-based genetic programming (Geyer-Schulz (1997, ISBN:978-3-7908-0830-X)), grammatical evolution (Ryan, C., O'Neill, M., and Collins, J. J. (2018) <doi:10.1007/978-3-319-78717-6>), and grammatical differential evolution (O'Neill, M. and Brabazon, A. (2006) in Arabinia, H. (2006, ISBN:978-193-241596-3). All algorithms reuse basic adaptive mechanisms for performance optimization. For xega's architecture, see Geyer-Schulz, A. (2025) <doi:10.5445/IR/1000187255>. Sequential or parallel execution (on multi-core machines, local clusters, and high-performance computing environments) is available for all algorithms. See <https://github.com/ageyerschulz/xega/tree/main/examples/executionModel>.

Given the date column as an ascending entry, future errors are included in the sum of squares of error that should be minimized based on the number of steps and weights you determine. Thus, it is prevented that the variables affect each other's coefficients unrealistically.

Extension to xpose to support nlmixr2'. Provides functions to import nlmixr2 fit data into an xpose data object, allowing the use of xpose for nlmixr2 model diagnostics.

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 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 collection of gene representation-independent functions implements the population layer of extended evolutionary and genetic algorithms and its support. 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>.

Reading and writing sheets of a single Excel file into and from a list of data frames. Eases I/O of tabular data in bioinformatics while keeping them in a human readable format.

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

Grammatical evolution (see O'Neil, M. and Ryan, C. (2003,ISBN:1-4020-7444-1)) uses decoders to convert linear (binary or integer genes) into programs. In addition, automatic determination of codon precision with a limited rule choice bias is provided. For a recent survey of grammatical evolution, see Ryan, C., O'Neill, M., and Collins, J. J. (2018) <doi:10.1007/978-3-319-78717-6>.

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

Create beautifully color-coordinated and customized themes for your xaringan slides, without writing any CSS. Complete your slide theme with ggplot2 themes that match the font and colors used in your slides. Customized styles can be created directly in your slides R Markdown source file or in a separate external script.