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.

Simple functions to convert given Arabic numerals to Kansuji numerical figures that represent numbers written in Chinese characters.

The archdata package provides several types of data that are typically used in archaeological research. It provides all of the data sets used in "Quantitative Methods in Archaeology Using R" by David L Carlson, one of the Cambridge Manuals in Archaeology.

Allows users to stem Arabic texts for text analysis.

Auto-GO is a framework that enables automated, high quality Gene Ontology enrichment analysis visualizations. It also features a handy wrapper for Differential Expression analysis around the DESeq2 package described in Love et al. (2014) <doi:10.1186/s13059-014-0550-8>. The whole framework is structured in different, independent functions, in order to let the user decide which steps of the analysis to perform and which plot to produce.

Facilitates access to the data from the Atlas do Estado Brasileiro (<https://www.ipea.gov.br/atlasestado/>), maintained by the Instituto de Pesquisa Econômica Aplicada (Ipea). It allows users to search for specific series, list series or themes, and download data when available.

This package implements a parsimonious evolutionary model to analyze and predict gene-functional annotations in phylogenetic trees as described in Vega Yon et al. (2021) <doi:10.1371/journal.pcbi.1007948>. Focusing on computational efficiency, aphylo makes it possible to estimate pooled phylogenetic models, including thousands (hundreds) of annotations (trees) in the same run. The package also provides the tools for visualization of annotated phylogenies, calculation of posterior probabilities (prediction) and goodness-of-fit assessment featured in Vega Yon et al. (2021).

Collect your data on digital marketing campaigns from Amazon S3 using the Windsor.ai API <https://windsor.ai/api-fields/>.

Anscombe's quartet are a set of four two-variable datasets that have several common summary statistics but which have very different joint distributions. This becomes apparent when the data are plotted, which illustrates the importance of using graphical displays in Statistics. This package enables the creation of datasets that have identical marginal sample means and sample variances, sample correlation, least squares regression coefficients and coefficient of determination. The user supplies an initial dataset, which is shifted, scaled and rotated in order to achieve target summary statistics. The general shape of the initial dataset is retained. The target statistics can be supplied directly or calculated based on a user-supplied dataset. The datasauRus package <https://cran.r-project.org/package=datasauRus> provides further examples of datasets that have markedly different scatter plots but share many sample summary statistics.

This package provides a tool for generating acronyms and initialisms from arbitrary text input.

This package provides tools supporting multi-criteria and group decision making, including variable number of criteria, by means of aggregation operators, spread measures, fuzzy logic connectives, fusion functions, and preordered sets. Possible applications include, but are not limited to, quality management, scientometrics, software engineering, etc.

This package provides a collection of tools that support data splitting, predictive modeling, and model evaluation. A typical function is to split a dataset into a training dataset and a test dataset. Then compare the data distribution of the two datasets. Another feature is to support the development of predictive models and to compare the performance of several predictive models, helping to select the best model.

This package provides functions for estimating the attributable burden of disease due to risk factors. The posterior simulation is performed using arm::sim as described in Gelman, Hill (2012) <doi:10.1017/CBO9780511790942> and the attributable burden method is based on Nielsen, Krause, Molbak <doi:10.1111/irv.12564>.

This package provides a comprehensive set of tools for descriptive statistics, graphical data exploration, outlier detection, homoscedasticity testing, and multiple comparison procedures. Includes manual implementations of Levene's test, Bartlett's test, and the Fligner-Killeen test, as well as post hoc comparison methods such as Tukey, Scheffé, Games-Howell, Brunner-Munzel, and others. This version introduces two new procedures: the Jonckheere-Terpstra trend test and the Jarque-Bera test with Glinskiy's (2024) correction. Designed for use in teaching, applied statistical analysis, and reproducible research. Additionally you can find a post hoc Test Planner, which helps you to make a decision on which procedure is most suitable.

Penalized variable selection tools for the Cox proportional hazards model with interval censored and possibly left truncated data. It performs variable selection via penalized nonparametric maximum likelihood estimation with an adaptive lasso penalty. The optimal thresholding parameter can be searched by the package based on the profile Bayesian information criterion (BIC). The asymptotic validity of the methodology is established in Li et al. (2019 <doi:10.1177/0962280219856238>). The unpenalized nonparametric maximum likelihood estimation for interval censored and possibly left truncated data is also available.

This package provides a function that implements the acceptance-rejection method in an optimized manner to generate pseudo-random observations for discrete or continuous random variables. Proposed by von Neumann J. (1951), <https://mcnp.lanl.gov/pdf_files/>, the function is optimized to work in parallel on Unix-based operating systems and performs well on Windows systems. The acceptance-rejection method implemented optimizes the probability of generating observations from the desired random variable, by simply providing the probability function or probability density function, in the discrete and continuous cases, respectively. Implementation is based on references CASELLA, George at al. (2004) <https://www.jstor.org/stable/4356322>, NEAL, Radford M. (2003) <https://www.jstor.org/stable/3448413> and Bishop, Christopher M. (2006, ISBN: 978-0387310732).

Enables sampling from arbitrary distributions if the log density is known up to a constant; a common situation in the context of Bayesian inference. The implemented sampling algorithm was proposed by Vihola (2012) <DOI:10.1007/s11222-011-9269-5> and achieves often a high efficiency by tuning the proposal distributions to a user defined acceptance rate.

Automatic normalisation of a data frame to third normal form, with the intention of easing the process of data cleaning. (Usage to design your actual database for you is not advised.) Originally inspired by the AutoNormalize library for Python by Alteryx (<https://github.com/alteryx/autonormalize>), with various changes and improvements. Automatic discovery of functional or approximate dependencies, normalisation based on those, and plotting of the resulting "database" via Graphviz', with options to exclude some attributes at discovery time, or remove discovered dependencies at normalisation time.

This package implements adaptive gPCA, as described in: Fukuyama, J. (2017) <arXiv:1702.00501>. The package also includes functionality for applying the method to phyloseq objects so that the method can be easily applied to microbiome data and a shiny app for interactive visualization.

Checks function arguments, ideally for use in R packages. Uses a simple interface and produces clean, informative error messages using cli'.

This package provides a dynamic time warping (DTW) algorithm for stratigraphic alignment, translated into R from the original published MATLAB code by Hay et al. (2019) <doi:10.1130/G46019.1>. The DTW algorithm incorporates two geologically relevant parameters (g and edge) for augmenting the typical DTW cost matrix, allowing for a range of sedimentologic and chronologic conditions to be explored, as well as the generation of an alignment library (as opposed to a single alignment solution). The g parameter relates to the relative sediment accumulation rate between the two time series records, while the edge parameter relates to the amount of total shared time between the records. Note that this algorithm is used for all DTW alignments in the Align Shiny application, detailed in Hagen et al. (in review).

This package implements the double-sampling bounds estimator of Coppock, Gerber, Green, and Kern (2017) <doi:10.1017/pan.2016.6> for randomized experiments with nonignorable missing outcomes. Provides worst-case (Manski) bounds, double-sampling bounds with analytic variance and Imbens-Manski confidence intervals, Lee (2009) <doi:10.1111/j.1467-937X.2009.00536.x> trimming bounds with analytic and bootstrap standard errors, covariate adjustment via poststratification, and a sensitivity analysis for violations of the outcome stability assumption.

Streamline use of the All of Us Researcher Workbench (<https://www.researchallofus.org/data-tools/workbench/>)with tools to extract and manipulate data from the All of Us database. Increase interoperability with the Observational Health Data Science and Informatics ('OHDSI') tool stack by decreasing reliance of All of Us tools and allowing for cohort creation via Atlas'. Improve reproducible and transparent research using All of Us'.

Client package for the AWS Key Management Service <https://aws.amazon.com/kms/>, a cloud service for managing encryption keys.