This package provides a continuous version of the receiver operating characteristics (ROC) curve to assess both classification and continuity performances of biomarkers, diagnostic tests, or risk prediction models.

This package performs clustering of quantitative variables, assuming that clusters lie in low-dimensional subspaces. Segmentation of variables, number of clusters and their dimensions are selected based on BIC. Candidate models are identified based on many runs of K-means algorithm with different random initializations of cluster centers.

By creating crowd-sourcing tasks that can be easily posted and results retrieved using Amazon's Mechanical Turk (MTurk) API, researchers can use this solution to validate the quality of topics obtained from unsupervised or semi-supervised learning methods, and the relevance of topic labels assigned. This helps ensure that the topic modeling results are accurate and useful for research purposes. See Ying and others (2022) <doi:10.1101/2023.05.02.538599>. For more information, please visit <https://github.com/Triads-Developer/Topic_Model_Validation>.

Automatically selects and visualises statistical hypothesis tests between two vectors, based on their class, distribution, sample size, and a user-defined confidence level (conf.level). Visual outputs - including box plots, bar charts, regression lines with confidence bands, mosaic plots, residual plots, and Q-Q plots - are annotated with relevant test statistics, assumption checks, and post-hoc analyses where applicable. The algorithmic workflow helps the user focus on the interpretation of test results rather than test selection. It is particularly suited for quick data analysis, e.g., in statistical consulting projects or educational settings. The test selection algorithm proceeds as follows: Input vectors of class numeric or integer are considered numerical; those of class factor are considered categorical. Assumptions of residual normality and homogeneity of variances are considered met if the corresponding test yields a p-value greater than the significance level alpha = 1 - conf.level. (1) When the response vector is numerical and the predictor vector is categorical, a test of central tendencies is selected. If the categorical predictor has exactly two levels, t.test() is applied when group sizes exceed 30 (Lumley et al. (2002) <doi:10.1146/annurev.publhealth.23.100901.140546>). For smaller samples, normality of residuals is tested using shapiro.test(); if met, t.test() is used; otherwise, wilcox.test(). If the predictor is categorical with more than two levels, an aov() is initially fitted. Residual normality is evaluated using both shapiro.test() and ad.test(); residuals are considered approximately normal if at least one test yields a p-value above alpha. If this assumption is met, bartlett.test() assesses variance homogeneity. If variances are homogeneous, aov() is used; otherwise oneway.test(). Both tests are followed by TukeyHSD(). If residual normality cannot be assumed, kruskal.test() is followed by pairwise.wilcox.test(). (2) When both the response and predictor vectors are numerical, a simple linear regression model is fitted using lm(). (3) When both vectors are categorical, Cochran's rule (Cochran (1954) <doi:10.2307/3001666>) is applied to test independence either by chisq.test() or fisher.test().

This package provides numerous functions to fill data. These can be applied either to missing or skewed data. The functions are designed within the scope of Student Analytics.

This package creates Vertex Similarity matrix of an undirected graph based on the method stated by E. A. Leicht, Petter Holme, AND M. E. J. Newman in their paper <DOI:10.1103/PhysRevE.73.026120>.

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

Applies affine and similarity transformations on vector spatial data (sp objects). Transformations can be defined from control points or directly from parameters. If redundant control points are provided Least Squares is applied allowing to obtain residuals and RMSE.

Allows registered VectorSurv <https://vectorsurv.org/> users access to data through the VectorSurv API <https://api.vectorsurv.org/>. Additionally provides functions for analysis and visualization.

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.

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

This package contains functions for visualization univariate data: ccdplot and qddplot.

Automates set operations (i.e., comparisons of overlap) between multiple vectors. It also contains a function for automating reporting in RMarkdown', by generating markdown output for easy analysis, as well as an RMarkdown template for use with RStudio'.

Inference methods for state-space models, relying on the Kalman Filter or on Viking (Variational Bayesian VarIance tracKING). See J. de Vilmarest (2022) <https://theses.hal.science/tel-03716104/>.

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 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.

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>.

The base class VirtualArray is defined, which acts as a wrapper around lists allowing users to fold arbitrary sequential data into n-dimensional, R-style virtual arrays. The derived XArray class is defined to be used for homogeneous lists that contain a single class of objects. The RasterArray and SfArray classes enable the use of stacked spatial data instead of lists.

Multi-caller variant analysis pipeline for targeted analysis sequencing (TAS) data. Features a modular, automated workflow that can start with raw reads and produces a user-friendly PDF summary and a spreadsheet containing consensus variant information.

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 a dedicated viral-explainer model tool designed to empower researchers in the field of HIV research, particularly in viral load and CD4 (Cluster of Differentiation 4) lymphocytes regression modeling. Drawing inspiration from the tidymodels framework for rigorous model building of Max Kuhn and Hadley Wickham (2020) <https://www.tidymodels.org>, and the DALEXtra tool for explainability by Przemyslaw Biecek (2020) <doi:10.48550/arXiv.2009.13248>. It aims to facilitate interpretable and reproducible research in biostatistics and computational biology for the benefit of understanding HIV dynamics.

Handling of vegetation data from different sources ( Turboveg 2.0 <https://www.synbiosys.alterra.nl/turboveg/>; the German national repository <https://www.vegetweb.de> and others. Taxonomic harmonization (given appropriate taxonomic lists, e.g. the Euro+Med list <https://eurosl.infinitenature.org>).

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.

Implementation of the variable banding procedure for modeling local dependence and estimating precision matrices that is introduced in Yu & Bien (2016) and is available at <https://arxiv.org/abs/1604.07451>.