This package provides functions for importing, validating, and analyzing Viva Glint survey data exports, with optional API-based import via the Microsoft Graph API. Includes tools for data reshaping, question-level analysis, multi-cycle comparisons, organizational hierarchy analysis, factor analysis, and correlation analysis. Harman (1960, ISBN: 0226316513); Husser (2017) <doi:10.1002/9781118901731.iecrm0048>.

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/>.

Provide a collection of miscellaneous R functions related to the Vasicek distribution with the intent to make the lives of risk modelers easier.

This package provides a set of functions for data transformations. Transformations are performed on character and numeric data. As the scope of the package is within Student Analytics, there are functions focused around the academic year.

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.

This package provides a collection of tools for downstream analysis of VirusHunterGatherer output. Processing of hittables and plotting of results, enabling better interpretation, is made easier with the provided functions.

Declare data validation rules and data quality indicators; confront data with them and analyze or visualize the results. The package supports rules that are per-field, in-record, cross-record or cross-dataset. Rules can be automatically analyzed for rule type and connectivity. Supports checks implied by an SDMX DSD file as well. See also Van der Loo and De Jonge (2018) <doi:10.1002/9781118897126>, Chapter 6 and the JSS paper (2021) <doi:10.18637/jss.v097.i10>.

This package provides statistical methods for analytical method comparison and validation studies. Implements Bland-Altman analysis for assessing agreement between measurement methods (Bland & Altman (1986) <doi:10.1016/S0140-6736(86)90837-8>), Passing-Bablok regression for non-parametric method comparison (Passing & Bablok (1983) <doi:10.1515/cclm.1983.21.11.709>), and Deming regression accounting for measurement error in both variables (Linnet (1993) <doi:10.1093/clinchem/39.3.424>). Also includes tools for setting quality goals based on biological variation (Fraser & Petersen (1993) <doi:10.1093/clinchem/39.7.1447>) and calculating Six Sigma metrics, precision experiments with variance component analysis, precision profiles for functional sensitivity estimation (Kroll & Emancipator (1993) <https://pubmed.ncbi.nlm.nih.gov/8448849/>). Commonly used in clinical laboratory method validation. Provides publication-ready plots and comprehensive statistical summaries.

Many bioacoustic data workflows rely on manual review (i.e., validation) of a subset of call files to provide information to statistical models that account for misclassification by automated algorithms. Because manual review can be prohibitively expensive, simulation can be a valuable tool to aid the design of studies that use validation. This package provides user-friendly functions to reduce the programming burden of simulation studies that compare validation sampling designs. Simulations assume the count-detection model, which is a realistic model for bioacoustic data, especially for bats. For more information, see Oram et al. (2025) <doi:10.1214/25-AOAS2096>.

This package provides a collection of tools for analyzing the field of vision. It provides a framework for development and use of innovative methods for visualization, statistical analysis, and clinical interpretation of visual-field loss and its change over time. It is intended to be a tool for collaborative research. The package is described in Marin-Franch and Swanson (2013) <doi:10.1167/13.4.10> and is part of the Open Perimetry Initiative (OPI) [Turpin, Artes, and McKendrick (2012) <doi:10.1167/12.11.22>].

This package provides a wrapper around a CSS library called vov.css', intended for use in shiny applications. Simply wrap a UI element in one of the animation functions to see it move.

Rule sets with validation rules may contain redundancies or contradictions. Functions for finding redundancies and problematic rules are provided, given a set a rules formulated with validate'.

Tool for easy and efficient discretization of continuous and categorical data. The package calculates the most optimal binning of a given explanatory variable with respect to a user-specified target variable. The purpose is to assign a unique Weight-of-Evidence value to each of the calculated binpoints in order to recode the original variable. The package allows users to impose certain restrictions on the functional form on the resulting binning while maximizing the overall information value in the original data. The package is well suited for logistic scoring models where input variables may be subject to restrictions such as linearity by e.g. regulatory authorities. An excellent source describing in detail the development of scorecards, and the role of Weight-of-Evidence coding in credit scoring is (Siddiqi 2006, ISBN: 978â 0-471â 75451â 0). The package utilizes the discrete nature of decision trees and Isotonic Regression to accommodate the trade-off between flexible functional forms and maximum information value.

This package provides a set of functions for generating HTML to embed hosted video in your R Markdown documents or Shiny applications.

This package provides tools for the analysis and visualization of animal and plant pedigrees. Analytical methods include equivalent complete generations, generation intervals, effective population size (via inbreeding, coancestry, and demographic approaches), founder and ancestor contributions, partial inbreeding, genetic diversity indices, and additive (A), dominance (D), and epistatic (AA) relationship matrices. Core algorithms â ancestry tracing, topological sorting, inbreeding coefficients, and matrix construction â are implemented in C++ ('Rcpp', RcppArmadillo') and data.table', scaling to pedigrees with over one million individuals. Pedigree graphs are rendered via igraph with support for compact full-sib family display; relationship matrices can be visualized as heatmaps. Supports complex mating systems, including selfing and pedigrees in which the same individual can appear as both sire and dam.

Designed to help the user to determine the sensitivity of an proposed causal effect to unconsidered common causes. Users can create visualizations of sensitivity, effect sizes, and determine which pattern of effects would support a causal claim for between group differences. Number needed to treat formula from Kraemer H.C. & Kupfer D.J. (2006) <doi:10.1016/j.biopsych.2005.09.014>.

This package provides a set of visual input controls for Shiny apps to facilitate filtering across multiple outputs.

Under a different representation of the multivariate normal (MVN) probability, we can use the Vecchia approximation to sample the integrand at a linear complexity with respect to n. Additionally, both the SOV algorithm from Genz (92) and the exponential-tilting method from Botev (2017) can be adapted to linear complexity. The reference for the method implemented in this package is Jian Cao and Matthias Katzfuss (2024) "Linear-Cost Vecchia Approximation of Multivariate Normal Probabilities" <doi:10.48550/arXiv.2311.09426>. Two major references for the development of our method are Alan Genz (1992) "Numerical Computation of Multivariate Normal Probabilities" <doi:10.1080/10618600.1992.10477010> and Z. I. Botev (2017) "The Normal Law Under Linear Restrictions: Simulation and Estimation via Minimax Tilting" <doi:10.48550/arXiv.1603.04166>.

Offers a comprehensive set of assertion tests to help users validate the integrity of their data. These tests can be used to check for specific conditions or properties within a dataset and help ensure that data is accurate and reliable. The package is designed to make it easy to add quality control checks to data analysis workflows and to aid in identifying and correcting any errors or inconsistencies in data.

This package provides a graphical user interface to integrate, visualize and explore results from linkage and quantitative trait loci analysis, together with genomic information for autopolyploid species. The app is meant for interactive use and allows users to optionally upload different sources of information, including gene annotation and alignment files, enabling the exploitation and search for candidate genes in a genome browser. In its current version, VIEWpoly supports inputs from MAPpoly', polymapR', diaQTL', QTLpoly', polyqtlR', GWASpoly', and HIDECAN packages.

Generate Venn plots, summary tables, and ellipse paths for polygon clipping. Provides direct access to subsets of interest and offers flexible customization of Venn diagrams. Summary tables are also available when Venn diagram visualization is not suitable.

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.

This package provides functions for downloading, reshaping, culling, cleaning, and analyzing fossil data from the Paleobiology Database <https://paleobiodb.org>.

Bayesian variable selection using shrinkage priors to identify significant variables in high-dimensional datasets. The package includes methods for determining the number of significant variables through innovative clustering techniques of posterior distributions, specifically utilizing the 2-Means and Sequential 2-Means (S2M) approaches. The package aims to simplify the variable selection process with minimal tuning required in statistical analysis.