R Codes and Datasets for Duchateau, L. and Janssen, P. and Rowlands, G. J. (1998). Linear Mixed Models. An Introduction with applications in Veterinary Research. International Livestock Research Institute.

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>.

Alternative splicing produces a variety of different protein products from a given gene. VALERIE enables visualisation of alternative splicing events from high-throughput single-cell RNA-sequencing experiments. VALERIE computes percent spliced-in (PSI) values for user-specified genomic coordinates corresponding to alternative splicing events. PSI is the proportion of sequencing reads supporting the included exon/intron as defined by Shiozawa (2018) <doi:10.1038/s41467-018-06063-x>. PSI are inferred from sequencing reads data based on specialised infrastructures for representing and computing annotated genomic ranges by Lawrence (2013) <doi:10.1371/journal.pcbi.1003118>. Computed PSI for each single cell are subsequently presented in the form of a heatmap implemented using the pheatmap package by Kolde (2010) <https://CRAN.R-project.org/package=pheatmap>. Board overview of the mean PSI difference and associated p-values across different user-defined groups of single cells are presented in the form of a line graph using the ggplot2 package by Wickham (2007) <https://CRAN.R-project.org/package=ggplot2>.

This package provides a way of visualizing collections of time series and, optionally their future values, forecasts for their future values and prediction intervals for the forecasts. A web-based GUI can be used to display the information in a collection of time series.

Computation of volatility impulse response function for multivariate time series model using algorithm by Jin, Lin and Tamvakis (2012) <doi:10.1016/j.eneco.2012.03.003>.

This package provides helper functions and wrappers to simplify authentication, data retrieval, and result processing from the VALD APIs'. Designed to streamline integration for analysts and researchers working with VALD's external APIs'. For further documentation on integrating with VALD APIs', see: <https://support.vald.com/hc/en-au/articles/23415335574553-How-to-integrate-with-VALD-APIs>. For a step-by-step guide to using this package, see: <https://support.vald.com/hc/en-au/articles/48730811824281-A-guide-to-using-the-valdr-R-package>.

Mainly data sets to accompany the VGAM package and the book "Vector Generalized Linear and Additive Models: With an Implementation in R" (Yee, 2015) <DOI:10.1007/978-1-4939-2818-7>. These are used to illustrate vector generalized linear and additive models (VGLMs/VGAMs), and associated models (Reduced-Rank VGLMs, Quadratic RR-VGLMs, Row-Column Interaction Models, and constrained and unconstrained ordination models in ecology). This package now contains some old VGAM family functions which have been replaced by newer ones (often because they are now special cases).

Interactive variogram diagnostics.

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.

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 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.

This package provides an htmlwidgets interface to VChart.js'. VChart', more than just a cross-platform charting library, but also an expressive data storyteller. VChart examples and documentation are available here: <https://www.visactor.io/vchart>.

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.

The d3.js framework with the plugins d3-voronoi-map, d3-voronoi-treemap and d3-weighted-voronoi are used to generate Voronoi treemaps in R and in a shiny application. The computation of the Voronoi treemaps are based on Nocaj and Brandes (2012) <doi:10.1111/j.1467-8659.2012.03078.x>.

This package provides tools to explore and visualize transitions between clusters in multivariate data. The package generates pseudo-samples by interpolating between cluster medoids, enabling the study of gradual changes in feature space. It also computes k-nearest neighbors (KNN)-based statistics to relate pseudo-samples to real data and summarize variable behavior using mean, median, or standard deviation. Finally, the package offers interactive visualizations of variable trajectories along cluster transitions, including both direct trajectory plots and bootstrap-based interactive plots with confidence intervals to assess variability and uncertainty across the transition path.

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>".

Calculate and plot Venn diagrams in 2D and 3D.

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 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 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 variable selection for linear models and generalized linear models using Bayesian information criterion (BIC) and model posterior probability (MPP). Given a set of candidate predictors, it evaluates candidate models and returns model-level summaries (BIC and MPP) and predictor-level posterior inclusion probabilities (PIP). For more details see Xu, S., Ferreira, M. A., & Tegge, A. N. (2025) <doi:10.48550/arXiv.2510.02628>.

This package provides users with a simple and convenient mechanism to manage and query a Virtuoso database using the DBI (Data-Base Interface) compatible ODBC (Open Database Connectivity) interface. Virtuoso is a high-performance "universal server," which can act as both a relational database, supporting standard Structured Query Language ('SQL') queries, while also supporting data following the Resource Description Framework ('RDF') model for Linked Data. RDF data can be queried using SPARQL ('SPARQL Protocol and RDF Query Language) queries, a graph-based query that supports semantic reasoning. This allows users to leverage the performance of local or remote Virtuoso servers using popular R packages such as DBI and dplyr', while also providing a high-performance solution for working with large RDF triplestores from R. The package also provides helper routines to install, launch, and manage a Virtuoso server locally on Mac', Windows and Linux platforms using the standard interactive installers from the R command-line. By automatically handling these setup steps, the package can make using Virtuoso considerably faster and easier for a most users to deploy in a local environment. Managing the bulk import of triples from common serializations with a single intuitive command is another key feature of this package. Bulk import performance can be tens to hundreds of times faster than the comparable imports using existing R tools, including rdflib and redland packages.

Collapsed Variational Inference for a Dirichlet Process (DP) mixture model with unknown covariance matrix structure and DP concentration parameter. It enables efficient clustering of high-dimensional data with significantly improved computational speed than traditional MCMC methods. The package incorporates 8 parameterisations and corresponding prior choices for the unknown covariance matrix, from which the user can choose and apply accordingly.

This package provides templates and functions to simplify the production and maintenance of curriculum vitae.