Flagger to detect acute kidney injury (AKI) in a patient dataset.

Trigger animation effects on scroll on any HTML element of shiny and rmarkdown', such as any text or plot, thanks to the AOS Animate On Scroll jQuery library.

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.

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

This package provides WHO 2007 References for School-age Children and Adolescents (5 to 19 years) (z-scores) with confidence intervals and standard errors around the prevalence estimates, taking into account complex sample designs. More information on the methods is available online: <https://www.who.int/tools/growth-reference-data-for-5to19-years>.

This package provides functions for Posterior estimates of Accelerated Failure Time(AFT) model with MCMC and Maximum likelihood estimates of AFT model without MCMC for univariate and multivariate analysis in high dimensional gene expression data are available in this afthd package. AFT model with Bayesian framework for multivariate in high dimensional data has been proposed by Prabhash et al.(2016) <doi:10.21307/stattrans-2016-046>.

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.

This package provides functions for I/O, visualisation and analysis of functional Magnetic Resonance Imaging (fMRI) datasets stored in the ANALYZE or NIFTI format. Note that the latest version of XQuartz seems to be necessary under MacOS.

This package provides a color palette generator inspired by American politics, with colors ranging from blue on the left to gray in the middle and red on the right. A variety of palettes allow for a range of applications from brief discrete scales (e.g., three colors for Democrats, Independents, and Republicans) to continuous interpolated arrays including dozens of shades graded from blue (left) to red (right). This package greatly benefitted from building on the source code (with permission) from Ram and Wickham (2015).

Obtain overlapping clustering models for object-by-variable data matrices using the Additive Profile Clustering (ADPROCLUS) method. Also contains the low dimensional ADPROCLUS method for simultaneous dimension reduction and overlapping clustering. For reference see Depril, Van Mechelen, Mirkin (2008) <doi:10.1016/j.csda.2008.04.014> and Depril, Van Mechelen, Wilderjans (2012) <doi:10.1007/s00357-012-9112-5>.

The efficient Markov chain Monte Carlo estimation of stochastic volatility models with and without leverage (asymmetric and symmetric stochastic volatility models). Further, it computes the logarithm of the likelihood given parameters using particle filters.

The goal of the package aldvmm is to fit adjusted limited dependent variable mixture models of health state utilities. Adjusted limited dependent variable mixture models are finite mixtures of normal distributions with an accumulation of density mass at the limits, and a gap between 100% quality of life and the next smaller utility value. The package aldvmm uses the likelihood and expected value functions proposed by Hernandez Alava and Wailoo (2015) <doi:10.1177/1536867X1501500307> using normal component distributions and a multinomial logit model of probabilities of component membership.

Estimate ideal efficiencies of aerosol sampling through sample lines. Functions were developed consistent with the approach described in Hogue, Mark; Thompson, Martha; Farfan, Eduardo; Hadlock, Dennis, (2014), "Hand Calculations for Transport of Radioactive Aerosols through Sampling Systems" Health Phys 106, 5, S78-S87, <doi:10.1097/HP.0000000000000092>.

This package provides a collection of psychometric methods to process item metadata and use target assessment and measurement blueprint constraints to assemble a test form. Currently two automatic test assembly (ata) approaches are enabled. For example, the weighted (positive) deviations method, wdm(), proposed by Swanson and Stocking (1993) <doi:10.1177/014662169301700205> was implemented in its full specification allowing for both item selection as well as test form refinement. The linear constraint programming approach, atalp(), uses the linear equation solver by Berkelaar et. al (2014) <http://lpsolve.sourceforge.net/5.5/> to enable a variety of approaches to select items.

This package provides a project template to support the data science workflow.

This package provides an htmlwidgets interface to apexcharts.js'. Apexcharts is a modern JavaScript charting library to build interactive charts and visualizations with simple API. Apexcharts examples and documentation are available here: <https://apexcharts.com/>.

This package provides a weekly summary of Hass Avocado sales for the contiguous US from January 2017 through December 20204. See the package website for more information, documentation, and examples. Data source: Haas Avocado Board <https://hassavocadoboard.com/category-data/>.

Anytime-valid inference for linear models, namely, sequential t-tests, sequential F-tests, and confidence sequences with time-uniform Type-I error and coverage guarantees. This allows hypotheses to be continuously tested without sacrificing false positive guarantees. It is based on the methods documented in Lindon et al. (2022) <doi:10.48550/arXiv.2210.08589>.

Interact with Google Ads Data Hub API <https://developers.google.com/ads-data-hub/reference/rest>. The functionality allows to fetch customer details, submit queries to ADH.

This package implements the Classification-based on Association Rules (CBA) algorithm for association rule classification. The package, also described in Hahsler et al. (2019) <doi:10.32614/RJ-2019-048>, contains several convenience methods that allow to automatically set CBA parameters (minimum confidence, minimum support) and it also natively handles numeric attributes by integrating a pre-discretization step. The rule generation phase is handled by the arules package. To further decrease the size of the CBA models produced by the arc package, postprocessing by the qCBA package is suggested.

An unofficial companion to the textbook "Applied Regression Analysis" by N.R. Draper and H. Smith (3rd Ed., 1998) including all the accompanying datasets.

This package provides functions for interacting directly with the ALTADATA API. With this R package, developers can build applications around the ALTADATA API without having to deal with accessing and managing requests and responses. ALTADATA is a curated data marketplace for more information go to <https://www.altadata.io>.

This package performs the two-sample Ansariâ Bradley test (Ansari & Bradley, 1960 <https://www.jstor.org/stable/2237814>) for univariate, distinct data in the presence of missing values, as described in Zeng et al. (2025) <doi:10.48550/arXiv.2509.20332>. This method does not make any assumptions about the missingness mechanisms and controls the Type I error regardless of the missing values by taking all possible missing values into account.

This package provides tools to simulate alphanumeric alleles, impute genetic missing data and reconstruct non-recombinant haplotypes from pedigree databases in a deterministic way. Allelic simulations can be implemented taking into account many factors (such as number of families, markers, alleles per marker, probability and proportion of missing genotypes, recombination rate, etc). Genotype imputation can be used with simulated datasets or real databases (previously loaded in .ped format). Haplotype reconstruction can be carried out even with missing data, since the program firstly imputes each family genotype (without a reference panel), to later reconstruct the corresponding haplotypes for each family member. All this considering that each individual (due to meiosis) should unequivocally have two alleles per marker (one inherited from each parent) and thus imputation and reconstruction results can be deterministically calculated.