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>).

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!

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

Edit XMP metadata <https://en.wikipedia.org/wiki/Extensible_Metadata_Platform> in a variety of media file formats as well as edit bookmarks (aka outline aka table of contents) and documentation info entries in pdf files. Can detect and use a variety of command-line tools to perform these operations such as exiftool <https://exiftool.org/>, ghostscript <https://www.ghostscript.com/>, and/or pdftk <https://gitlab.com/pdftk-java/pdftk>.

Computes Chatterjee's non-parametric correlation coefficient for time series data. It extends the original metric to time series analysis by providing the univariate Xi-Autocorrelation Function (Xi-ACF), directional Xi-Cross-Correlation Function (Xi-CCF), and multivariate network evaluation matrices. The package allows users to test for non-linear dependence using Iterative Amplitude Adjusted Fourier Transform (IAAFT) and Multivariate IAAFT (MIAAFT) surrogate data with strict Family-Wise Error Rate ('FWER') control via Max-statistic approaches. Methodologies are based on Chatterjee (2021) <doi:10.1080/01621459.2020.1758115>, surrogate data testing methods by Schreiber and Schmitz (1996) <doi:10.1103/PhysRevLett.77.635>, and local structural identification by Watanabe (2026) <doi:10.2139/ssrn.6829431>.

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

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>.

Translates a BNF (Backus-Naur Form) specification of a context-free language into an R grammar object which consists of the start symbol, the symbol table, the production table, and a short production table. The short production table is non-recursive. The grammar object contains the file name from which it was generated (without a path). In addition, it provides functions to determine the type of a symbol (isTerminal() and isNonterminal()) and functions to access the production table (rules() and derives()). For the BNF specification, see Backus, John et al. (1962) "Revised Report on the Algorithmic Language ALGOL 60". (ALGOL60 standards page <http://www.algol60.org/2standards.htm>, html-edition <https://www.masswerk.at/algol60/report.htm>) A preprocessor for macros which expand to standard BNF is included. The grammar compiler is an extension of the APL2 implementation in Geyer-Schulz, Andreas (1997, ISBN:978-3-7908-0830-X).

This package implements the interactive fixed effects ('IFE') panel estimator of Bai (2009) <doi:10.3982/ECTA6135> with analytical standard errors ('homoskedastic', HC1 robust, and cluster-robust by unit). Supports asymptotic bias correction for large panels (Bai 2009) and a dynamic extension for predetermined regressors (Moon and Weidner 2017 <doi:10.1017/S0266466615000328>). Includes information-criterion-based factor number selection (Bai and Ng 2002 <doi:10.1111/1468-0262.00273>). All computations use base R only with no external dependencies.

An implementation of the RuleFit algorithm as described in Friedman & Popescu (2008) <doi:10.1214/07-AOAS148>. eXtreme Gradient Boosting ('XGBoost') is used to build rules, and glmnet is used to fit a sparse linear model on the raw and rule features. The result is a model that learns similarly to a tree ensemble, while often offering improved interpretability and achieving improved scoring runtime in live applications. Several algorithms for reducing rule complexity are provided, most notably hyperrectangle de-overlapping. All algorithms scale to several million rows and support sparse representations to handle tens of thousands of dimensions.

An R interface to the OpenPyXL Python library to create native Excel charts and work with Microsoft Excel files.

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".

Miscellaneous functions used for x-engineering (feature engineering) or for supporting in other packages maintained by Shichen Xie'.

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.

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.

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.

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 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 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.

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.

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

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

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.

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.