Using frequency matrices, very low frequency variants (VLFs) are assessed for amino acid and nucleotide sequences. The VLFs are then compared to see if they occur in only one member of a species, singleton VLFs, or if they occur in multiple members of a species, shared VLFs. The amino acid and nucleotide VLFs are then compared to see if they are concordant with one another. Amino acid VLFs are also assessed to determine if they lead to a change in amino acid residue type, and potential changes to protein structures. Based on Stoeckle and Kerr (2012) <doi:10.1371/journal.pone.0043992> and Phillips et al. (2023) <doi:10.3897/BDJ.11.e96480>.

Allow R users to interact with the Canvas Learning Management System (LMS) API (see <https://canvas.instructure.com/doc/api/all_resources.html> for details). It provides a set of functions to access and manipulate course data, assignments, grades, users, and other resources available through the Canvas API.

Extendable R6 file comparison classes, including a shiny app for combining the comparison functionality into a file comparison application. The package idea originates from pharma companies drug development processes, where statisticians and statistical programmers need to review and compare different versions of the same outputs and datasets. The package implementation itself is not tied to any specific industry and can be used in any context for easy file comparisons between different file version sets.

This package provides easy-to-use tools for data analysis and visualization for hyperspectral remote sensing (also known as imaging spectroscopy), with a particular focus on vegetation hyperspectral data analysis. It consists of a set of functions, ranging from the organization of hyperspectral data in the proper data structure for spectral feature selection, calculation of vegetation index, multivariate analysis, as well as to the visualization of spectra and results of analysis in the ggplot2 style.

Visualize Variance is an intuitive shiny applications tailored for agricultural research data analysis, including one-way and two-way analysis of variance, correlation, and other essential statistical tools. Users can easily upload their datasets, perform analyses, and download the results as a well-formatted document, streamlining the process of data analysis and reporting in agricultural research.The experimental design methods are based on classical work by Fisher (1925) and Scheffe (1959). The correlation visualization approaches follow methods developed by Wei & Simko (2021) and Friendly (2002) <doi:10.1198/000313002533>.

The qda() function from package MASS is extended to calculate a weighted linear (LDA) and quadratic discriminant analysis (QDA) by changing the group variances and group means based on cell-wise uncertainties. The uncertainties can be derived e.g. through relative errors for each individual measurement (cell), not only row-wise or column-wise uncertainties. The method can be applied compositional data (e.g. portions of substances, concentrations) and non-compositional data.

Valid Improved Sparsity A-Learning (VISA) provides a new method for selecting important variables involved in optimal treatment regime from a multiply robust perspective. The VISA estimator achieves its success by borrowing the strengths of both model averaging (ARM, Yuhong Yang, 2001) <doi:10.1198/016214501753168262> and variable selection (PAL, Chengchun Shi, Ailin Fan, Rui Song and Wenbin Lu, 2018) <doi:10.1214/17-AOS1570>. The package is an implementation of Zishu Zhan and Jingxiao Zhang. (2022+).

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.

Empirical models for runoff, erosion, and phosphorus loss across a vegetated filter strip, given slope, soils, climate, and vegetation (Gall et al., 2018) <doi:10.1007/s00477-017-1505-x>. It also includes functions for deriving climate parameters from measured daily weather data, and for simulating rainfall. Models implemented include MUSLE (Williams, 1975) and APLE (Vadas et al., 2009 <doi:10.2134/jeq2008.0337>).

Variational Autoencoded Multivariate Spatial Fay-Herriot models are designed to efficiently estimate population parameters in small area estimation. This package implements the variational generalized multivariate spatial Fay-Herriot model (VGMSFH) using NumPyro and PyTorch backends, as demonstrated by Wang, Parker, and Holan (2025) <doi:10.48550/arXiv.2503.14710>. The vmsae package provides utility functions to load weights of the pretrained variational autoencoders (VAEs) as well as tools to train custom VAEs tailored to users specific applications.

Position adjustments for ggplot2 to implement "visualize as you randomize" principles, which can be especially useful when plotting experimental data.

This is a package for creating and running Agent Based Models (ABM). It provides a set of base classes with core functionality to allow bootstrapped models. For more intensive modeling, the supplied classes can be extended to fit researcher needs.

Computes the random forest variable importance (VIMP) for the conditional inference random forest (cforest) of the party package. Includes a function (varImp) that computes the VIMP for arbitrary measures from the measures package. For calculating the VIMP regarding the measures accuracy and AUC two extra functions exist (varImpACC and varImpAUC).

The algorithm implemented in this package was designed to quickly estimates the distribution of the log-rank especially for heavy unbalanced groups. VALORATE estimates the null distribution and the p-value of the log-rank test based on a recent formulation. For a given number of alterations that define the size of survival groups, the estimation involves a weighted sum of distributions that are conditional on a co-occurrence term where mutations and events are both present. The estimation of conditional distributions is quite fast allowing the analysis of large datasets in few minutes <https://bioinformatics.mx/index.php/bioinfo-tools/>.

Estimates hierarchical models using variational inference. At present, it can estimate logistic, linear, and negative binomial models. It can accommodate models with an arbitrary number of random effects and requires no integration to estimate. It also provides the ability to improve the quality of the approximation using marginal augmentation. Goplerud (2022) <doi:10.1214/21-BA1266> and Goplerud (2024) <doi:10.1017/S0003055423000035> provide details on the variational algorithms.

This package provides fitting routines for four versions of the Vitality family of mortality models.

This package contains variable, diversity, and joining sequences and accompanying functions that enable both the extraction of and comparison between immune V-D-J genomic segments from a variety of species. Sources include IMGT from MP Lefranc (2009) <doi:10.1093/nar/gkn838> and Vgenerepertoire from publication DN Olivieri (2014) <doi:10.1007/s00251-014-0784-3>.

Estimation, lag selection, diagnostic testing, forecasting, causality analysis, forecast error variance decomposition and impulse response functions of VAR models and estimation of SVAR and SVEC models.

Estimates the predicted 10-year cardiovascular (CVD) risk score (in probability) for women military service members and veterans by inputting patient profiles. The proposed women CVD risk score improves the accuracy of the existing American College of Cardiology/American Heart Association CVD risk assessment tool in predicting longâ term CVD risk for VA women, particularly in young and racial/ethnic minority women. See the reference: Jeonâ Slaughter, H., Chen, X., Tsai, S., Ramanan, B., & Ebrahimi, R. (2021) <doi:10.1161/JAHA.120.019217>.

This package provides a suite of easy to use functions for collecting social media data and generating networks for analysis. Supports Mastodon, YouTube, Reddit and Web 1.0 data sources.

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.

This package provides a programmatic interface in R for the US Department of Transportation (DOT) National Highway Transportation Safety Administration (NHTSA) vehicle identification number (VIN) API, located at <https://vpic.nhtsa.dot.gov/api/>. The API can decode up to 50 vehicle identification numbers in one call, and provides manufacturer information about the vehicles, including make, model, model year, and gross vehicle weight rating (GVWR).

This package provides a shiny app for accurate estimation of vaccine induced immunogenicity with bivariate linear modeling. Method is detailed in: Lhomme, Hejblum, Lacabaratz, Wiedemann, Lelievre, Levy, Thiebaut & Richert (2020). Journal of Immunological Methods, 477:112711. <doi:10.1016/j.jim.2019.112711>.

To ease the visualization of outputs from Diversity Motif Analyser ('DiMA'; <https://github.com/BVU-BILSAB/DiMA>). vDiveR allows visualization of the diversity motifs (index and its variants â major, minor and unique) for elucidation of the underlying inherent dynamics. Please refer <https://vdiver-manual.readthedocs.io/en/latest/> for more information.