An R interface to the Project VoteSmart'<https://justfacts.votesmart.org/> API.

Calculate point estimates of and valid confidence intervals for nonparametric, algorithm-agnostic variable importance measures in high and low dimensions, using flexible estimators of the underlying regression functions. For more information about the methods, please see Williamson et al. (Biometrics, 2020), Williamson et al. (JASA, 2021), and Williamson and Feng (ICML, 2020).

This package provides a shiny dashboard and plotting utilities to explore and report VALD ForceDecks testing data. Includes interactive modules for metric exploration, radar charts, longitudinal comparisons, quadrant plots, and athlete reports.

This package provides a robust and reproducible pipeline for extracting, cleaning, and analyzing athlete performance data generated by VALD ForceDecks systems. The package supports batch-oriented data processing for large datasets, standardized data transformation workflows, and visualization utilities for sports science research and performance monitoring. It is designed to facilitate reproducible analysis across multiple sports with comprehensive documentation and error handling.

This package provides pedagogical tools for visualization and numerical computation in vector calculus. Includes functions for parametric curves, scalar and vector fields, gradients, divergences, curls, line and surface integrals, and dynamic 2D/3D graphical analysis to support teaching and learning. The implemented methods follow standard treatments in vector calculus and multivariable analysis as presented in Marsden and Tromba (2011) <ISBN:9781429215084>, Stewart (2015) <ISBN:9781285741550>, Thomas, Weir and Hass (2018) <ISBN:9780134438986>, Larson and Edwards (2016) <ISBN:9781285255869>, Apostol (1969) <ISBN:9780471000051>, Spivak (1971) <ISBN:9780805390216>, Schey (2005) <ISBN:9780071369080>, Colley (2019) <ISBN:9780321982384>, Lizarazo Osorio (2020) <ISBN:9789585450103>, Sievert (2020) <ISBN:9780367180165>, and Borowko (2013) <ISBN:9781439870791>.

Uses large language models to create poems about R packages. Currently contains the roses() function to make "roses are red, ..." style poems and the prompt() function to only assemble the prompt without submitting it to the model.

The Variable Infiltration Capacity (VIC) model is a macroscale hydrologic model that solves full water and energy balances, originally developed by Xu Liang at the University of Washington (UW). The version of VIC source code used is of 5.0.1 on <https://github.com/UW-Hydro/VIC/>, see Hamman et al. (2018). Development and maintenance of the current official version of the VIC model at present is led by the UW Hydro (Computational Hydrology group) in the Department of Civil and Environmental Engineering at UW. VIC is a research model and in its various forms it has been applied to most of the major river basins around the world, as well as globally <http://vic.readthedocs.io/en/master/Documentation/References/>. References: "Liang, X., D. P. Lettenmaier, E. F. Wood, and S. J. Burges (1994), A simple hydrologically based model of land surface water and energy fluxes for general circulation models, J. Geophys. Res., 99(D7), 14415-14428, <doi:10.1029/94JD00483>"; "Hamman, J. J., Nijssen, B., Bohn, T. J., Gergel, D. R., and Mao, Y. (2018), The Variable Infiltration Capacity model version 5 (VIC-5): infrastructure improvements for new applications and reproducibility, Geosci. Model Dev., 11, 3481-3496, <doi:10.5194/gmd-11-3481-2018>".

Analyze the co-adaptation of codon usage between a virus and its host, calculate various codon usage bias measurements as: effective number of codons (ENc) Novembre (2002) <doi:10.1093/oxfordjournals.molbev.a004201>, codon adaptation index (CAI) Sharp and Li (1987) <doi:10.1093/nar/15.3.1281>, relative codon deoptimization index (RCDI) Puigbò et al (2010) <doi:10.1186/1756-0500-3-87>, similarity index (SiD) Zhou et al (2013) <doi:10.1371/journal.pone.0077239>, synonymous codon usage orderliness (SCUO) Wan et al (2004) <doi:10.1186/1471-2148-4-19> and, relative synonymous codon usage (RSCU) Sharp et al (1986) <doi:10.1093/nar/14.13.5125>. Also, it provides a statistical dinucleotide over- and underrepresentation with three different models. Implement several methods for visualization of codon usage as ENc.GC3plot() and PR2.plot().

Constructs a virtual population from fertility and mortality rates for any country, calendar year and birth cohort in the Human Mortality Database <https://www.mortality.org> and the Human Fertility Database <https://www.humanfertility.org>. Fertility histories are simulated for every individual and their offspring, producing a multi-generation virtual population.

Although model selection is ubiquitous in scientific discovery, the stability and uncertainty of the selected model is often hard to evaluate. How to characterize the random behavior of the model selection procedure is the key to understand and quantify the model selection uncertainty. This R package offers several graphical tools to visualize the distribution of the selected model. For example, Gplot(), Hplot(), VDSM_scatterplot() and VDSM_heatmap(). To the best of our knowledge, this is the first attempt to visualize such a distribution. About what distribution of selected model is and how it work please see Qin,Y.and Wang,L. (2021) "Visualization of Model Selection Uncertainty" <https://homepages.uc.edu/~qinyn/VDSM/VDSM.html>.

This package provides functions for the variance gamma distribution. Density, distribution and quantile functions. Functions for random number generation and fitting of the variance gamma to data. Also, functions for computing moments of the variance gamma distribution of any order about any location. In addition, there are functions for checking the validity of parameters and to interchange different sets of parameterizations for the variance gamma distribution.

This package provides a user-friendly R shiny app for performing various statistical tests on datasets. It allows users to upload data in numerous formats and perform statistical analyses. The app dynamically adapts its options based on the selected columns and supports both single and multiple column comparisons. The app's user interface is designed to streamline the process of selecting datasets, columns, and test options, making it easy for users to explore and interpret their data. The underlying functions for statistical tests are well-organized and can be used independently within other R scripts.

Generate suggestions for validation rules from a reference data set, which can be used as a starting point for domain specific rules to be checked with package validate'.

The Vega-Lite JavaScript framework provides a higher-level grammar for visual analysis, akin to ggplot or Tableau', that generates complete Vega specifications. Functions exist which enable building a valid spec from scratch or importing a previously created spec file. Functions also exist to export spec files and to generate code which will enable plots to be embedded in properly configured web pages. The default behavior is to generate an htmlwidget'.

An implementation of methods related to sparse clustering and variable importance in clustering. The package currently allows to perform sparse k-means clustering with a group penalty, so that it automatically selects groups of numerical features. It also allows to perform sparse clustering and variable selection on mixed data (categorical and numerical features), by preprocessing each categorical feature as a group of numerical features. Several methods for visualizing and exploring the results are also provided. M. Chavent, J. Lacaille, A. Mourer and M. Olteanu (2020)<https://www.esann.org/sites/default/files/proceedings/2020/ES2020-103.pdf>.

Visualize and compute percentiles/probabilities of normal, t, f, chi square and binomial distributions.

This package provides a collection of statistical tests for martingale difference hypothesis, including automatic portmanteau test (Escansiano and Lobato, 2009) <doi:10.1016/j.jeconom.2009.03.001> and automatic variance ratio test (Kim, 2009) <doi:10.1016/j.frl.2009.04.003>.

Functionality for creating phase portraits of functions in the complex number plane. Works with R base graphics, whose full functionality is available. Parallel processing is used for optimum performance.

This package provides tools for designing virus protein panels through sequence clustering and protein sequence analysis. The package includes functionality for filtering sequences, removing redundancy, identifying outliers, clustering sequences, and calculating entropy to evaluate clustering quality. A publication describing these methods is in preparation and will be added once available.

This package provides a wrapped LASSO approach by integrating an ensemble learning strategy to help select efficient, stable, and high confidential variables from omics-based data. Using a bagging strategy in combination of a parametric method or inflection point search method for cut-off threshold determination. This package can integrate and vote variables generated from multiple LASSO models to determine the optimal candidates. Luo H, Zhao Q, et al (2020) <doi:10.1126/scitranslmed.aax7533> for more details.

This package provides a framework for generating virtual species distributions, a procedure increasingly used in ecology to improve species distribution models. This package integrates the existing methodological approaches with the objective of generating virtual species distributions with increased ecological realism.

Application of Variational Mode Decomposition based different Machine Learning models for univariate time series forecasting. For method details see (i) K. Dragomiretskiy and D. Zosso (2014) <doi:10.1109/TSP.2013.2288675>; (ii) Pankaj Das (2020) <http://krishi.icar.gov.in/jspui/handle/123456789/44138>.

Facilitates modeling species ecological niches and geographic distributions based on occurrences and environments that have a vertical as well as horizontal component, and projecting models into three-dimensional geographic space. Working in three dimensions is useful in an aquatic context when the organisms one wishes to model can be found across a wide range of depths in the water column. The package also contains functions to automatically generate marine training model training regions using machine learning, and interpolate and smooth patchily sampled environmental rasters using thin plate splines. Davis Rabosky AR, Cox CL, Rabosky DL, Title PO, Holmes IA, Feldman A, McGuire JA (2016) <doi:10.1038/ncomms11484>. Nychka D, Furrer R, Paige J, Sain S (2021) <doi:10.5065/D6W957CT>. Pateiro-Lopez B, Rodriguez-Casal A (2022) <https://CRAN.R-project.org/package=alphahull>.

This package contains functions for a variational Bayesian method for sparse PCA proposed by Ning (2020) <arXiv:2102.00305>. There are two algorithms: the PX-CAVI algorithm (if assuming the loadings matrix is jointly row-sparse) and the batch PX-CAVI algorithm (if without this assumption). The outputs of the main function, VBsparsePCA(), include the mean and covariance of the loadings matrix, the score functions, the variable selection results, and the estimated variance of the random noise.