This package provides a set of functions to: (1) perform fuzzy clustering of vegetation data (De Caceres et al, 2010) <doi:10.1111/j.1654-1103.2010.01211.x>; (2) to assess ecological community similarity on the basis of structure and composition (De Caceres et al, 2013) <doi:10.1111/2041-210X.12116>.

This package provides tools to analyze vaccine coverage data and simulate potential disease outbreak scenarios. It allows users to calculate key epidemiological metrics such as the effective reproduction number (Re), outbreak probabilities, and expected infection counts based on county-level vaccination rates, disease characteristics, and vaccine effectiveness. The package includes historical kindergarten vaccination data for Florida counties and offers functions for generating summary tables, visualizations, and exporting the underlying plot data.

This package implements a set of routines to perform structured matrix factorization with minimum volume constraints. The NMF procedure decomposes a matrix X into a product C * D. Given conditions such that the matrix C is non-negative and has sufficiently spread columns, then volume minimization of a matrix D delivers a correct and unique, up to a scale and permutation, solution (C, D). This package provides both an implementation of volume-regularized NMF and "anchor-free" NMF, whereby the standard NMF problem is reformulated in the covariance domain. This algorithm was applied in Vladimir B. Seplyarskiy Ruslan A. Soldatov, et al. "Population sequencing data reveal a compendium of mutational processes in the human germ line". Science, 12 Aug 2021. <doi:10.1126/science.aba7408>. This package interacts with data available through the simulatedNMF package, which is available in a drat repository. To access this data package, see the instructions at <https://github.com/kharchenkolab/vrnmf>. The size of the simulatedNMF package is approximately 8 MB.

This package provides an R interface for interacting with the Semestry TermTime services. It allows users to retrieve scheduling data from the API. see <https://github.com/vusaverse/vvtermtime/blob/main/openapi_7.7.0.pdf> for details.

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 tools to generate virtual environmental drivers with a given temporal autocorrelation, and to simulate pollen curves at annual resolution over millennial time-scales based on these drivers and virtual taxa with different life traits and niche features. It also provides the means to simulate quasi-realistic pollen-data conditions by applying simulated accumulation rates and given depth intervals between consecutive samples.

Vasicek density, cumulative distribution, quantile functions and random deviate generation of Vasicek distribution. In addition, there are two functions for fitting the Generalized Additive Models for Location Scale and Shape introduced by Rigby and Stasinopoulos (2005, <doi:10.1111/j.1467-9876.2005.00510.x>). Some functions are written in C++ using Rcpp', developed by Eddelbuettel and Francois (2011, <doi:10.18637/jss.v040.i08>).

This package contains selected data from two publications, Campbell et al'. (2016) <DOI:10.1080/14486563.2015.1028486> and Pacioni et al'. (2017) <DOI:10.1071/PC17002>. The data is provided both as raw outputs from the population viability analysis software Vortex and packaged as R objects. The R package vortexR uses the raw data provided here to illustrate its functionality of parsing raw Vortex output into R objects.

This package provides platform for Vedic calendar system having several functionalities to facilitate conversion between Gregorian and Vedic calendar systems, and helpful in examining its impact in the time series analysis domain.

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>.

This package provides a framework to infer causality on a pair of time series of real numbers based on variable-lag Granger causality and transfer entropy. Typically, Granger causality and transfer entropy have an assumption of a fixed and constant time delay between the cause and effect. However, for a non-stationary time series, this assumption is not true. For example, considering two time series of velocity of person A and person B where B follows A. At some time, B stops tying his shoes, then running to catch up A. The fixed-lag assumption is not true in this case. We propose a framework that allows variable-lags between cause and effect in Granger causality and transfer entropy to allow them to deal with variable-lag non-stationary time series. Please see Chainarong Amornbunchornvej, Elena Zheleva, and Tanya Berger-Wolf (2021) <doi:10.1145/3441452> when referring to this package in publications.

Computes the Gaussian variational approximation of the Bayesian empirical likelihood posterior. This is an implementation of the function found in Yu, W., & Bondell, H. D. (2023) <doi:10.1080/01621459.2023.2169701>.

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.

This package provides a variety of tools to allow the quantification of videos of the lymphatic vasculature taken under an operating microscope. Lymphatic vessels that have been injected with a variety of blue dyes can be tracked throughout the video to determine their width over time. Code is optimised for efficient processing of multiple large video files. Functions to calculate physiologically relevant parameters and generate graphs from these values are also included.

US VAERS vaccine data for 01/01/2018 - 06/14/2018. If you want to explore the full VAERS data for 1990 - Present (data, symptoms, and vaccines), then check out the vaers package from the URL below. The URL and BugReports below correspond to the vaers package, of which vaersvax is a small subset (2018 only). vaers is not hosted on CRAN due to the large size of the data set. To install the Suggested vaers and vaersND packages, use the following R code: devtools::install_git("<https://gitlab.com/iembry/vaers.git>", build_vignettes = TRUE) and devtools::install_git("<https://gitlab.com/iembry/vaersND.git>", build_vignettes = TRUE)'. "The Vaccine Adverse Event Reporting System (VAERS) is a national early warning system to detect possible safety problems in U.S.-licensed vaccines. VAERS is co-managed by the Centers for Disease Control and Prevention (CDC) and the U.S. Food and Drug Administration (FDA)." For more information about the data, visit <https://vaers.hhs.gov/>. For information about vaccination/immunization hazards, visit <http://www.questionuniverse.com/rethink.html#vaccine>.

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>.

Make it easy to use vue in R with helper dependency functions and examples.

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>.

This package contains logic for cell-specific gene set scoring of single cell RNA sequencing data.

Conducts linear regression using variational Bayesian inference, particularly optimized for genome-wide association mapping and whole-genome prediction which use a number of DNA markers as the explanatory variables. Provides seven regression models which select the important variables (i.e., the variables related to response variables) among the given explanatory variables in different ways (i.e., model structures).

An interactive document on the topic of variance analysis using rmarkdown and shiny packages. Runtime examples are provided in the package function as well as at <https://predanalyticssessions1.shinyapps.io/chisquareVarianceTest/>.

Multi-precision library that allows to store and operate with arbitrarily big integers without loss of precision. It includes a large list of tools to work with them, like: - Arithmetic and logic operators - Modular-arithmetic operators - Computer Number Theory utilities - Probabilistic primality tests - Factorization algorithms - Random generators of diferent types of integers.

Feature selection using Sequential Forward Floating feature Selection and Jeffries-Matusita distance. It returns a suboptimal set of features to use for image classification. Reference: Dalponte, M., Oerka, H.O., Gobakken, T., Gianelle, D. & Naesset, E. (2013). Tree Species Classification in Boreal Forests With Hyperspectral Data. IEEE Transactions on Geoscience and Remote Sensing, 51, 2632-2645, <DOI:10.1109/TGRS.2012.2216272>.

This package provides functions for the mass-univariate voxelwise analysis of medical imaging data that follows the NIfTI <http://nifti.nimh.nih.gov> format.