Computing and visualizing comparative asymptotic timings of different algorithms and code versions. Also includes functionality for comparing empirical timings with expected references such as linear or quadratic, <https://en.wikipedia.org/wiki/Asymptotic_computational_complexity> Also includes functionality for measuring asymptotic memory and other quantities.

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>.

Simulation and estimation tools for various types of ambit processes, including trawl processes and weighted trawl processes.

This package implements the methodology introduced in Capezza, Lepore, and Paynabar (2025) <doi:10.1080/00401706.2025.2561744> for process monitoring with limited labeling resources. The package provides functions to (i) simulate data streams with true latent states and multivariate Gaussian observations as done in the paper, (ii) fit partially hidden Markov models (pHMMs) using a constrained Baum-Welch algorithm with partial labels, and (iii) perform stream-based active learning that balances exploration and exploitation to decide whether to request labels in real time. The methodology is particularly suited for statistical process monitoring in industrial applications where labeling is costly.

This package provides functions to fit Accurate Generalized Linear Model (AGLM) models, visualize them, and predict for new data. AGLM is defined as a regularized GLM which applies a sort of feature transformations using a discretization of numerical features and specific coding methodologies of dummy variables. For more information on AGLM, see Suguru Fujita, Toyoto Tanaka, Kenji Kondo and Hirokazu Iwasawa (2020) <https://www.institutdesactuaires.com/global/gene/link.php?doc_id=16273&fg=1>.

Deals with many computations related to the thermodynamics of atmospheric processes. It includes many functions designed to consider the density of air with varying degrees of water vapour in it, saturation pressures and mixing ratios, conversion of moisture indices, computation of atmospheric states of parcels subject to dry or pseudoadiabatic vertical evolutions and atmospheric instability indices that are routinely used for operational weather forecasts or meteorological diagnostics.

This package provides Azure Active Directory (AAD) authentication functionality for R users of Microsoft's Azure cloud <https://azure.microsoft.com/en-us>. Use this package to obtain OAuth 2.0 tokens for services including Azure Resource Manager, Azure Storage and others. It supports both AAD v1.0 and v2.0, as well as multiple authentication methods, including device code and resource owner grant. Tokens are cached in a user-specific directory obtained using the rappdirs package. The interface is based on the OAuth framework in the httr package, but customised and streamlined for Azure. Part of the AzureR family of packages.

Calculate the area of triangles and polygons using the shoelace formula. Area may be signed, taking into account path orientation, or unsigned, ignoring path orientation. The shoelace formula is described at <https://en.wikipedia.org/wiki/Shoelace_formula>.

This package creates all leave-one-out models and produces predictions for test samples.

For instructions, check <https://github.com/Hzhang-ouce/ARTofR>. This is a wrapper of bannerCommenter', for inserting neat comments, headers and dividers.

This package provides functions for Accurate and Speedy linkage map construction, manipulation and diagnosis of Doubled Haploid, Backcross and Recombinant Inbred R/qtl objects. This includes extremely fast linkage map clustering and optimal marker ordering using MSTmap (see Wu et al.,2008).

Analyses of Proportions can be performed on the Anscombe (arcsine-related) transformed data. The ANOPA package can analyze proportions obtained from up to four factors. The factors can be within-subject or between-subject or a mix of within- and between-subject. The main, omnibus analysis can be followed by additive decompositions into interaction effects, main effects, simple effects, contrast effects, etc., mimicking precisely the logic of ANOVA. For that reason, we call this set of tools ANOPA (Analysis of Proportion using Anscombe transform) to highlight its similarities with ANOVA. The ANOPA framework also allows plots of proportions easy to obtain along with confidence intervals. Finally, effect sizes and planning statistical power are easily done under this framework. Only particularity, the ANOPA computes F statistics which have an infinite degree of freedom on the denominator. See Laurencelle and Cousineau (2023) <doi:10.3389/fpsyg.2022.1045436>.

Coerce R object to asciidoc', txt2tags', restructuredText', org', textile or pandoc syntax. Package comes with a set of drivers for Sweave'.

Amiga Disk Files (ADF) are virtual representations of 3.5 inch floppy disks for the Commodore Amiga. Most disk drives from other systems (including modern drives) are not able to read these disks. The adfExplorer package enables you to establish R connections to files on such virtual DOS-formatted disks, which can be use to read from and write to those files.

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>).

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.

This package provides simple assertions with sensible defaults and customisable error messages. It offers convenient assertion call wrappers and a general assert function that can handle any condition. Default error messages are user friendly and easily customized with inline code evaluation and styling powered by the cli package.

Bayesian inference using the no-U-turn (NUTS) algorithm by Hoffman and Gelman (2014) <https://www.jmlr.org/papers/v15/hoffman14a.html>. Designed for AD Model Builder ('ADMB') models, or when R functions for log-density and log-density gradient are available, such as Template Model Builder models and other special cases. Functionality is similar to Stan', and the rstan and shinystan packages are used for diagnostics and inference.

Programming oncology specific Clinical Data Interchange Standards Consortium (CDISC) compliant 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>). The package is an extension package of the admiral package.

Simple and transparent parsing of genotype/dosage data from an input Variant Call Format (VCF) file, matching of genotype coordinates to the component Single Nucleotide Polymorphisms (SNPs) of an existing polygenic score (PGS), and application of SNP weights to dosages for the calculation of a polygenic score for each individual in accordance with the additive weighted sum of dosages model. Methods are designed in reference to best practices described by Collister, Liu, and Clifton (2022) <doi:10.3389/fgene.2022.818574>.

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

Access and manage the application programming interface (API) of the Armed Conflict Location & Event Data Project (ACLED) at <https://acleddata.com/>. The package makes it easy to retrieve a user-defined sample (or all of the available data) of ACLED, enabling a seamless integration of regular data updates into the research work flow. It requires a minimal number of dependencies. See the package's README file for a note on replicability when drawing on ACLED data. When using this package, you acknowledge that you have read ACLED's terms and conditions of use, and that you agree with their attribution requirements.

Convenience functions for aggregating a data frame or data table. Currently mean, sum and variance are supported. For Date variables, the recency and duration are supported. There is also support for dummy variables in predictive contexts. Code has been completely re-written in data.table for computational speed.

Argument parsing for R scripts, with support for long and short Unix-style options including option clustering, positional arguments including those of variable length, and multiple usage patterns which may take different subsets of options.