Analyses of frequencies can be performed using an alternative test based on the G statistic. The test has similar type-I error rates and power as the chi-square test. However, it is based on a total statistic that can be decomposed in an additive fashion into interaction effects, main effects, simple effects, contrast effects, etc., mimicking precisely the logic of ANOVA. We call this set of tools ANOFA (Analysis of Frequency data) to highlight its similarities with ANOVA. This framework also renders plots of frequencies along with confidence intervals. Finally, effect sizes and planning statistical power are easily done under this framework. The ANOFA is a tool that assesses the significance of effects instead of the significance of parameters; as such, it is more intuitive to most researchers than alternative approaches based on generalized linear models. See Laurencelle and Cousineau (2023) <doi:10.20982/tqmp.19.2.p173>.

Constructs sparse adjacency matrices from spatial coordinates, feature measurements, class labels, and temporal indices. Supports nearest-neighbor graphs, heat-kernel weights, graph Laplacians, diffusion operators, and bilateral smoothers for graph-based data analysis, following spectral graph methods in von Luxburg (2007) <doi:10.1007/s11222-007-9033-z>, diffusion maps in Coifman and Lafon (2006) <doi:10.1016/j.acha.2006.04.006>, and bilateral filtering in Tomasi and Manduchi (1998) <doi:10.1109/ICCV.1998.710815>.

Perform the Adaptable Regularized Hotelling's T^2 test (ARHT) proposed by Li et al., (2016) <arXiv:1609.08725>. Both one-sample and two-sample mean test are available with various probabilistic alternative prior models. It contains a function to consistently estimate higher order moments of the population covariance spectral distribution using the spectral of the sample covariance matrix (Bai et al. (2010) <doi:10.1111/j.1467-842X.2010.00590.x>). In addition, it contains a function to sample from 3-variate chi-squared random vectors approximately with a given correlation matrix when the degrees of freedom are large.

Automated generation, running, and interpretation of moderated nonlinear factor analysis models for obtaining scores from observed variables, using the method described by Gottfredson and colleagues (2019) <doi:10.1016/j.addbeh.2018.10.031>. This package creates M-plus input files which may be run iteratively to test two different types of covariate effects on items: (1) latent variable impact (both mean and variance); and (2) differential item functioning. After sequentially testing for all effects, it also creates a final model by including all significant effects after adjusting for multiple comparisons. Finally, the package creates a scoring model which uses the final values of parameter estimates to generate latent variable scores. \n\n This package generates TEMPLATES for M-plus inputs, which can and should be inspected, altered, and run by the user. In addition to being presented without warranty of any kind, the package is provided under the assumption that everyone who uses it is reading, interpreting, understanding, and altering every M-plus input and output file. There is no one right way to implement moderated nonlinear factor analysis, and this package exists solely to save users time as they generate M-plus syntax according to their own judgment.

Interactive R tutorials written using learnr for Field (2016), "An Adventure in Statistics", <ISBN:9781446210451>. Topics include general workflow in R and Rstudio', the R environment and tidyverse', summarizing data, model fitting, central tendency, visualising data using ggplot2', inferential statistics and robust estimation, hypothesis testing, the general linear model, comparing means, repeated measures designs, factorial designs, multilevel models, growth models, and generalized linear models (logistic regression).

An interface to the table storage service in Azure': <https://azure.microsoft.com/en-us/services/storage/tables/>. Supplies functionality for reading and writing data stored in tables, both as part of a storage account and from a CosmosDB database with the table service API. Part of the AzureR family of packages.

Extremely efficient toolkit for solving the best subset selection problem <https://www.jmlr.org/papers/v23/21-1060.html>. This package is its R interface. The package implements and generalizes algorithms designed in <doi:10.1073/pnas.2014241117> that exploits a novel sequencing-and-splicing technique to guarantee exact support recovery and globally optimal solution in polynomial times for linear model. It also supports best subset selection for logistic regression, Poisson regression, Cox proportional hazard model, Gamma regression, multiple-response regression, multinomial logistic regression, ordinal regression, Ising model reconstruction <doi:10.1080/01621459.2025.2571245>, (sequential) principal component analysis, and robust principal component analysis. The other valuable features such as the best subset of group selection <doi:10.1287/ijoc.2022.1241> and sure independence screening <doi:10.1111/j.1467-9868.2008.00674.x> are also provided.

Fast generators and iterators for permutations, combinations, integer partitions and compositions. The arrangements are in lexicographical order and generated iteratively in a memory efficient manner. It has been demonstrated that arrangements outperforms most existing packages of similar kind. Benchmarks could be found at <https://randy3k.github.io/arrangements/articles/benchmark.html>.

The at-Risk (aR) approach is based on a two-step parametric estimation procedure that allows to forecast the full conditional distribution of an economic variable at a given horizon, as a function of a set of factors. These density forecasts are then be used to produce coherent forecasts for any downside risk measure, e.g., value-at-risk, expected shortfall, downside entropy. Initially introduced by Adrian et al. (2019) <doi:10.1257/aer.20161923> to reveal the vulnerability of economic growth to financial conditions, the aR approach is currently extensively used by international financial institutions to provide Value-at-Risk (VaR) type forecasts for GDP growth (Growth-at-Risk) or inflation (Inflation-at-Risk). This package provides methods for estimating these models. Datasets for the US and the Eurozone are available to allow testing of the Adrian et al. (2019) model. This package constitutes a useful toolbox (data and functions) for private practitioners, scholars as well as policymakers.

Implementation of gene-level rare variant association tests targeting allelic series: genes where increasingly deleterious mutations have increasingly large phenotypic effects. The COding-variant Allelic Series Test (COAST) operates on the benign missense variants (BMVs), deleterious missense variants (DMVs), and protein truncating variants (PTVs) within a gene. COAST uses a set of adjustable weights that tailor the test towards rejecting the null hypothesis for genes where the average magnitude of effect increases monotonically from BMVs to DMVs to PTVs. See McCaw ZR, Oâ Dushlaine C, Somineni H, Bereket M, Klein C, Karaletsos T, Casale FP, Koller D, Soare TW. (2023) "An allelic series rare variant association test for candidate gene discovery" <doi:10.1016/j.ajhg.2023.07.001>.

Facilitates writing computationally reproducible student theses in PDF format that conform to the American Psychological Association (APA) manuscript guidelines (6th Edition). The package currently provides two R Markdown templates for homework and theses at the Psychology Department of the University of Cologne. The package builds on the package papaja but is tailored to the requirements of student theses and omits features for simplicity.

Quantile regression with fixed effects solves longitudinal data, considering the individual intercepts as fixed effects. The parametric set of this type of problem used to be huge. Thus penalized methods such as Lasso are currently applied. Adaptive Lasso presents oracle proprieties, which include Gaussianity and correct model selection. Bayesian information criteria (BIC) estimates the optimal tuning parameter lambda. Plot tools are also available.

Grafts the extinct bird species from the Avotrex database (Sayol et al., in review) on to the BirdTree phylogenies <https://birdtree.org>, using a set of different commands.

Fit pharmacokinetic/pharmacodynamic (PK/PD) models to aggregate-level data (mean vector and covariance matrix per study) rather than individual-level data. Integrates with the nlmixr2'/'rxode2 ecosystem via three estimation methods: a First-Order ('FO') analytical estimator, a Monte Carlo (MC) estimator, and an Iterative Reweighting Monte Carlo ('IRMC') estimator. Methods are based on Välitalo (2021) <doi:10.1007/s10928-021-09760-1>; software described in van de Beek et al. (2025) <doi:10.1007/s10928-025-10011-w>.

The AIPW package implements the augmented inverse probability weighting, a doubly robust estimator, for average causal effect estimation with user-defined stacked machine learning algorithms. To cite the AIPW package, please use: "Yongqi Zhong, Edward H. Kennedy, Lisa M. Bodnar, Ashley I. Naimi (2021). AIPW: An R Package for Augmented Inverse Probability Weighted Estimation of Average Causal Effects. American Journal of Epidemiology. <doi:10.1093/aje/kwab207>". Visit: <https://yqzhong7.github.io/AIPW/> for more information.

Programming vaccine specific Clinical Data Interchange Standards Consortium (CDISC) compliant Analysis Data Model (ADaM) datasets in R'. Flat model is followed as per Center for Biologics Evaluation and Research (CBER) guidelines for creating vaccine specific domains. ADaM datasets are a mandatory part of any New Drug or Biologics License Application submitted to the United States Food and Drug Administration (FDA). Analysis derivations are implemented in accordance with the "Analysis Data Model Implementation Guide" (CDISC Analysis Data Model Team (2021), <https://www.cdisc.org/standards/foundational/adam/adamig-v1-3-release-package>). The package is an extension package of the admiral package.

This package contains data and functions that can be used to make actuarial life tables. Each function adds a column to the inputted dataset for each intermediate calculation between mortality rate and life expectancy. Users can run any of our functions to complete the life table until that step, or run lifetable() to output a full life table that can be customized to remove optional columns. Methods for creating lifetables are as described in Zedstatistics (2021) <https://www.youtube.com/watch?v=Dfe59glNXAQ>.

Construct time series for Germany's municipalities (Gemeinden) and districts (Kreise) using a annual crosswalk constructed by the Federal Office for Building and Regional Planning (BBSR).

Collect your data on digital marketing campaigns from Adform Ads using the Windsor.ai API <https://windsor.ai/api-fields/>.

An evaluation framework for algorithm portfolios using Item Response Theory (IRT). We use continuous and polytomous IRT models to evaluate algorithms and introduce algorithm characteristics such as stability, effectiveness and anomalousness (Kandanaarachchi, Smith-Miles 2020) <doi:10.13140/RG.2.2.11363.09760>.

A-priori power simulations and power-calculations for within, between and mixed ANOVAs based on target (partial) eta-squared values. Supports complex designs with more than two factors and their interactions with a single function call.

Empirical likelihood-based approximate Bayesian Computation. Approximates the required posterior using empirical likelihood and estimated differential entropy. This is achieved without requiring any specification of the likelihood or estimating equations that connects the observations with the underlying parameters. The procedure is known to be posterior consistent. More details can be found in Chaudhuri, Ghosh, and Kim (2024) <doi:10.1002/SAM.11711>.

This package provides a developer-facing interface to the Arrow Database Connectivity ('ADBC') SQLite driver for the purposes of building high-level database interfaces for users. ADBC <https://arrow.apache.org/adbc/> is an API standard for database access libraries that uses Arrow for result sets and query parameters.

Easy data analysis and quality checks which are commonly used in data science. It combines the tabular and graphical visualization for easier usability. This package also creates an R Notebook with detailed data exploration with one function call. The notebook can be made interactive.