Aids the programming of Clinical Data Standards Interchange Consortium (CDISC) compliant Ophthalmology Analysis Data Model (ADaM) datasets in R. 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>).

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).

Fit pharmacokinetic/pharmacodynamic (PK/PD) models to aggregate-level data (mean vector and covariance matrix per study) rather than individual-level data, for meta-analysis across studies. Integrates with the nlmixr2'/'rxode2 ecosystem via four estimation methods: a First-Order ('FO') analytical estimator, a Monte Carlo (MC) estimator, a Gauss-Hermite quadrature ('GH') 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>.

Automatically do statistical exploration. Create formulas using tidyselect syntax, and then determine cross-validated model accuracy and variable contributions using glm and xgboost'. Contains additional helper functions to create and modify formulas. Has a flagship function to quickly determine relationships between categorical and continuous variables in the data set.

Parsing R code is key to build tools such as linters and stylers. This package provides a binding to the Rust crate ast-grep so that one can parse and explore R code.

This package provides a collection of model checking methods for semiparametric accelerated failure time (AFT) models under the rank-based approach. For the (computational) efficiency, Gehan's weight is used. It provides functions to verify whether the observed data fit the specific model assumptions such as a functional form of each covariate, a link function, and an omnibus test. The p-value offered in this package is based on the Kolmogorov-type supremum test and the variance of the proposed test statistics is estimated through the re-sampling method. Furthermore, a graphical technique to compare the shape of the observed residual to a number of the approximated realizations is provided. See the following references; A general model-checking procedure for semiparametric accelerated failure time models, Statistics and Computing, 34 (3), 117 <doi:10.1007/s11222-024-10431-7>; Diagnostics for semiparametric accelerated failure time models with R package afttest', Journal of Statistical Software, to appear, <doi:10.48550/arXiv.2511.09823>.

Uncertainty quantification and inverse estimation by probabilistic generative models from the beginning of the data analysis. An example is a Fourier basis method for inverse estimation in scattering analysis of microscopy videos. It does not require specifying a certain range of Fourier bases and it substantially reduces computational cost via the generalized Schur algorithm. See the reference: Mengyang Gu, Yue He, Xubo Liu and Yimin Luo (2023), <doi:10.48550/arXiv.2309.02468>, and Tong Lin, Jinseok Lee, Matt Helgeson, Megan T Valentine, Yimin Luo, Mengyang Gu (2026), <doi:10.48550/arXiv.2605.29424>.

This package contains functions carrying out adaptive procedures using mixed scaling approach to establish bioequivalence for in-vitro permeation test (IVPT) data. Currently, the package provides procedures based on parallel replicate design and balanced data, according to the U.S. Food and Drug Administration's "Draft Guidance on Acyclovir" <https://www.accessdata.fda.gov/drugsatfda_docs/psg/Acyclovir_topical%20cream_RLD%2021478_RV12-16.pdf>. Potvin et al. (2008) <doi:10.1002/pst.294> provides the basis for our adaptive design (see Method B). For a comprehensive overview of the method, refer to Lim et al. (2023) <doi:10.1002/pst.2333>. This package reflects the views of the authors and should not be construed to represent the views or policies of the U.S. Food and Drug Administration.

Create beautiful and interactive visualizations in a single function call. The data.table package is utilized to perform the data wrangling necessary to prepare your data for the plot types you wish to build, along with allowing fast processing for big data. There are two broad classes of plots available: standard plots and machine learning evaluation plots. There are lots of parameters available in each plot type function for customizing the plots (such as faceting) and data wrangling (such as variable transformations and aggregation).

Make summary tables for descriptive statistics and select explanatory variables automatically in various regression models. Support linear models, generalized linear models and cox-proportional hazard models. Generate publication-ready tables summarizing result of regression analysis and plots. The tables and plots can be exported in "HTML", "pdf('LaTex')", "docx('MS Word')" and "pptx('MS Powerpoint')" documents.

The Analytic Hierarchy Process is a versatile multi-criteria decision-making tool introduced by Saaty (1987) <doi:10.1016/0270-0255(87)90473-8> that allows decision-makers to weigh attributes and evaluate alternatives presented to them. This package provides a consistent methodology for researchers to reformat data and run analytic hierarchy process in R on data that are formatted using the survey data entry mode. It is optimized for performing the analytic hierarchy process with many decision-makers, and provides tools and options for researchers to aggregate individual preferences and test multiple options. It also allows researchers to quantify, visualize and correct for inconsistency in the decision-maker's comparisons.

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.

This package provides functions required to classify subjects within camera trap field data. The package can handle both images and videos. The authors recommend a two-step approach using Microsoft's MegaDector model and then a second model trained on the classes of interest.

This package provides the data sets used to build the ArchaeoPhases vignettes. The data sets were formerly distributed with ArchaeoPhases', however they exceed current CRAN policy for package size.

Data from Gardner and Janson art history textbooks about both the artists featured in these books as well as their works. See Helen Gardner ("Art through the ages; an introduction to its history and significance," 1926, <https://find.library.duke.edu/catalog/DUKE000104481>. Helen Gardner, revised by Horst de la Croix and Richard G. Tansey ("Gardnerâ s Art through the ages," 1980, ISBN: 0155037587). Fred S. Kleiner ("Gardnerâ s art through the ages: a global history," 2020, ISBN: 9781337630702). Horst de la Croix and Richard G. Tansey ("Gardner's art through the ages," 1986, ISBN: 0155037633). Helen Gardner ("Art through the ages; an introduction to its history and significance," 1936, <https://find.library.duke.edu/catalog/DUKE001199463>). Helen Gardner ("Art through the ages," 1948, <https://find.library.duke.edu/catalog/DUKE001199466>). Helen Gardner, revised under the editorship of Sumner M. Crosby ("Art through the ages," 1959, <https://find.library.duke.edu/catalog/DUKE001199469>). Helen Gardner, revised by Horst de la Croix and Richard G. Tansey ("Gardnerâ s Art through the ages," 1975, ISBN: 0155037560). Fred S. Kleiner ("Gardnerâ s Art through the ages: a global history," 2013, ISBN: 9780495915423. Fred S. Kleiner, Christin J. Mamiya, Richard G. Tansey ("Gardnerâ s art through the ages," 2001, ISBN: 0155083155). Fred S. Kleiner ("Gardnerâ s Art through the ages: a global history," 2016, ISBN: 9781285837840). Fred S. Kleiner, Christin J. Mamiya ("Gardnerâ s art through the ages," 2005, ISBN: 0534640958). Helen Gardner, revised by Horst de la Croix and Richard G. Tansey ("Gardnerâ s Art through the ages," 1970, ISBN: 0155037528). Helen Gardner, Richard G. Tansey, Fred S. Kleiner ("Gardnerâ s Art through the ages," 1996, ISBN: 0155011413). Helen Gardner, Horst de la Croix, Richard G. Tansey, Diane Kirkpatrick ("Gardnerâ s Art through the ages," 1991, ISBN: 0155037692). Helen Gardner, Fred S. Kleiner ("Gardnerâ s Art through the ages: a global history," 2009, ISBN: 9780495093077). Davies, Penelope J.E., Walter B. Denny, Frima Fox Hofrichter, Joseph F. Jacobs, Ann S. Roberts, David L. Simon ("Jansonâ s history of art: the western tradition," 2007, ISBN: 0131934554). Davies, Penelope J.E., Walter B. Denny, Frima Fox Hofrichter, Joseph F. Jacobs, Ann S. Roberts, David L. Simon ("Jansonâ s history of art: the western tradition," 2011, ISBN: 9780205685172). H. W. Janson, Anthony F. Janson ("History of Art," 2001, ISBN: 0810934469). H. W. Janson, revised and expanded by Anthony F. Janson ("History of art," 1986, ISBN: 013389388). H. W. Janson, Dora Jane Janson ("History of art: a survey of the major visual arts from the dawn of history to present day," 1977, ISBN: 0810910527). H. W. Janson, Dora Jane Janson ("History of art: a survey of the major visual arts from the dawn of history to present day," 1969, <https://find.library.duke.edu/catalog/DUKE000005734>). H. W. Janson, Dora Jane Janson ("History of art: a survey of the major visual arts from the dawn of history to present day," 1963, <https://find.library.duke.edu/catalog/DUKE001521852>). H. W. Janson, revised and expanded by Anthony F. Janson ("History of art," 1991, ISBN: 0810934019). H. W. Janson, revised and expanded by Anthony F. Janson ("History of art," 1995, ISBN: 0810934213).

Several cubic spline interpolation methods of H. Akima for irregular and regular gridded data are available through this package, both for the bivariate case (irregular data: ACM 761, regular data: ACM 760) and univariate case (ACM 433 and ACM 697). Linear interpolation of irregular gridded data is also covered by reusing D. J. Renkas triangulation code which is part of Akimas Fortran code. A bilinear interpolator for regular grids was also added for comparison with the bicubic interpolator on regular grids. Please note that most of the functions are now also covered in package interp, which is a re-implementation from scratch under a free license.

Automated psychological scale development and structural validation using large language models (LLMs) and network psychometric methods. Implements the AI-GENIE framework (Automatic Item Generation and Validation via Network-Integrated Evaluation) to generate candidate items, compute embedding representations, and estimate dimensional structure using Exploratory Graph Analysis (EGA). Item quality is evaluated using Unique Variable Analysis to identify redundant items and Bootstrap EGA to assess item and dimension stability. Supports both fully automated item generation and analysis of user-provided item sets, facilitating efficient, theory-informed measurement development prior to empirical data collection.

Simulate population dynamics from realistically complex matrix population models in a plug-and-play fashion. Supports aspatial and spatially implicit models with one or more species and time-varying covariates, stochasticity, density dependence, additions or removals of individuals, interspecific interactions, and metapopulations.

This package provides access to the Astronomy Engine C library (<https://github.com/cosinekitty/astronomy>) by Don Cross. The library calculates positions of the Sun, Moon, and planets, and predicts astronomical events such as rise/set times, lunar phases, equinoxes, solstices, eclipses, and transits. It is based on the VSOP87 planetary model and is accurate to within approximately one arcminute. This package bundles the single-file C source so that other R packages can link against it via LinkingTo without shipping their own copy.

Enables gene regulatory network (GRN) analysis on single cell clusters, using the GRN analysis software ANANSE', Xu et al.(2021) <doi:10.1093/nar/gkab598>. Export data from Seurat objects, for GRN analysis by ANANSE implemented in snakemake'. Finally, incorporate results for visualization and interpretation.

This package performs approximate unconditional and permutation testing for 2x2 contingency tables. Motivated by testing for disease association with rare genetic variants in case-control studies. When variants are extremely rare, these tests give better control of Type I error than standard tests.

Create APA style text from analyses for use within R Markdown documents. Descriptive statistics, confidence intervals, and cell sizes are reported.

Generate code for use with the Optical Mark Recognition free software Auto Multiple Choice (AMC). More specifically, this package provides functions that use as input the question and answer texts, and output the LaTeX code for AMC.

This package provides tools to construct (or add to) cell-type signature matrices using flow sorted or single cell samples and deconvolve bulk gene expression data. Useful for assessing the quality of single cell RNAseq experiments, estimating the accuracy of signature matrices, and determining cell-type spillover. Please cite: Danziger SA et al. (2019) ADAPTS: Automated Deconvolution Augmentation of Profiles for Tissue Specific cells <doi:10.1371/journal.pone.0224693>.