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.

Download data from individual XKCD comics, written by Randall Munroe <https://xkcd.com/>.

An implementation of representation-dependent gene level operations for genetic algorithms with genes representing permutations: Initialization of genes, mutation, and crossover. The crossover operation provided is position-based crossover (Syswerda, G., Chap. 21 in Davis, L. (1991, ISBN:0-442-00173-8). For mutation, several variants are included: Order-based mutation (Syswerda, G., Chap. 21 in Davis, L. (1991, ISBN:0-442-00173-8), randomized Lin-Kernighan heuristics (Croes, G. A. (1958) <doi:10.1287/opre.6.6.791> and Lin, S. and Kernighan. B. W. (1973) <doi:10.1287/opre.21.2.498>), and randomized greedy operators. A random mix operator for mutation selects a mutation variant randomly.

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.

This package implements the Durbin-Hausman panel cointegration tests of Westerlund (2008) <doi:10.1002/jae.963>. The tests are robust to cross-sectional dependence through common factor extraction using principal components. Provides both group-mean (DHg) and panel (DHp) test statistics with automatic factor number selection via information criteria.

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.

The US Census Bureau provides a seasonal adjustment program now called X-13ARIMA-SEATS building on both earlier programs called X-11 and X-12 as well as the SEATS program by the Bank of Spain. The US Census Bureau offers both source and binary versions -- which this package integrates for use by other R packages.

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'.

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.

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 provides a few functions which provide a quick way of subsetting genomic admixture data and generating customizable stacked barplots.

This package implements the Cross-Sectionally Augmented Panel Quantile Autoregressive Distributed Lag (CS-PQARDL) model and the Quantile Common Correlated Effects Mean Group (QCCEMG) estimator for panel data with cross-sectional dependence. The package handles unobserved common factors through cross-sectional averages following Pesaran (2006) <doi:10.1111/j.1468-0262.2006.00692.x> and Chudik and Pesaran (2015) <doi:10.1016/j.jeconom.2015.03.007>. Quantile regression for dynamic panels follows Harding, Lamarche, and Pesaran (2018) <doi:10.1016/j.jeconom.2018.07.010>. The ARDL approach to cointegration testing is based on Pesaran, Shin, and Smith (2001) <doi:10.1002/jae.616>.

Fit a two-step kernel ridge regression model for predicting edges in networks, and carry out cross-validation using shortcuts for swift and accurate performance assessment (Stock et al, 2018 <doi:10.1093/bib/bby095> ).

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).

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 collection of data exploration tools was developed at Yale University for the graphical exploration of complex multivariate data; barcode and gpairs now have their own packages. The big.read.table() function provided here may be useful for large files when only a subset is needed (but please see the note in the help page for this function).

Fits yield curves using Nelson-Siegel (1987) <doi:10.1086/296409>, Svensson (1994) <doi:10.3386/w4871>, and cubic spline methods. Extracts forward rates, discount factors, and par rates from fitted curves. Computes duration and convexity risk measures. Computes Z-spread and key rate durations. Provides principal component decomposition following Litterman and Scheinkman (1991) <doi:10.3905/jfi.1991.692347>, carry and roll-down analysis, and slope measures. All methods are pure computation with no external dependencies beyond base R; works with yield data from any source.

This package provides a collection of string functions designed for writing compact and expressive R code. yasp (Yet Another String Package) is simple, fast, dependency-free, and written in pure R. The package provides: a coherent set of abbreviations for paste() from package base with a variety of defaults, such as p() for "paste" and pcc() for "paste and collapse with commas"; wrap(), bracket(), and others for wrapping a string in flanking characters; unwrap() for removing pairs of characters (at any position in a string); and sentence() for cleaning whitespace around punctuation and capitalization appropriate for prose sentences.

This package performs nearest neighbor-based imputation using one or more alternative approaches to processing multivariate data. These include methods based on canonical correlation: analysis, canonical correspondence analysis, and a multivariate adaptation of the random forest classification and regression techniques of Leo Breiman and Adele Cutler. Additional methods are also offered. The package includes functions for comparing the results from running alternative techniques, detecting imputation targets that are notably distant from reference observations, detecting and correcting for bias, bootstrapping and building ensemble imputations, and mapping results.

Fits archetypal analysis models, including Euclidean, probabilistic, kernel, and directional variants. Methods include classical archetypal analysis from Cutler and Breiman (1994) <doi:10.1080/00401706.1994.10485840>, PCHA and kernel variants from Mørup and Hansen (2012) <doi:10.1016/j.neucom.2011.06.033>, probabilistic archetypal analysis from Seth and Eugster (2016) <doi:10.1007/s10994-015-5498-8>, directional archetypal analysis from Olsen et al. (2022) <doi:10.3389/fnins.2022.911034>, AA++ initialization from Mair and Sjölund (2023) <doi:10.48550/arXiv.2301.13748>, coreset-style initialization from Mair and Brefeld (2019) <https://proceedings.neurips.cc/paper_files/paper/2019/file/7f278ad602c7f47aa76d1bfc90f20263-Paper.pdf>, and adapted AIC from Suleman (2017) <doi:10.1109/FUZZ-IEEE.2017.8015385>. Provides initialization helpers, model selection paths, plotting methods, broom methods, and a tidymodels recipe step.

The generalized construction methods for magic squares, inspired by the ancient Chinese mathematician Yang Hui's classical work "Xu Gu Zhai Qi Suan Fa". These methods can construct 4n-order magic squares and 2(2n+1)-order magic squares.

Another implementation of general regression neural network in R based on Specht (1991) <DOI:10.1109/72.97934>. It is applicable to the functional approximation or the classification.

Compute the standard expected years of life lost (YLL), as developed by the Global Burden of Disease Study (Murray, C.J., Lopez, A.D. and World Health Organization, 1996). The YLL is based on comparing the age of death to an external standard life expectancy curve. It also computes the average YLL, which highlights premature causes of death and brings attention to preventable deaths (Aragon et al., 2008).

This package provides functions to manage taxonomy when lineages are described with strings and ranks separated with special patterns like "|*__" or ";*__".