Generates interactive plots for analysing and visualising three-class high dimensional data. It is particularly suited to visualising differences in continuous attributes such as gene/protein/biomarker expression levels between three groups. Differential gene/biomarker expression analysis between two classes is typically shown as a volcano plot. However, with three groups this type of visualisation is particularly difficult to interpret. This package generates 3D volcano plots and 3-way polar plots for easier interpretation of three-class data.

This package provides a robust and reproducible pipeline for extracting, cleaning, and analyzing athlete performance data generated by VALD ForceDecks systems. The package supports batch-oriented data processing for large datasets, standardized data transformation workflows, and visualization utilities for sports science research and performance monitoring. It is designed to facilitate reproducible analysis across multiple sports with comprehensive documentation and error handling.

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

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>).

The Vega-Lite JavaScript framework provides a higher-level grammar for visual analysis, akin to ggplot or Tableau', that generates complete Vega specifications. Functions exist which enable building a valid spec from scratch or importing a previously created spec file. Functions also exist to export spec files and to generate code which will enable plots to be embedded in properly configured web pages. The default behavior is to generate an htmlwidget'.

This package performs variable selection/feature reduction under a clustering or classification framework. In particular, it can be used in an automated fashion using mixture model-based methods ('teigen and mclust are currently supported). Can account for mixtures of non-Gaussian distributions via Manly transform (via ManlyMix'). See Andrews and McNicholas (2014) <doi:10.1007/s00357-013-9139-2> and Neal and McNicholas (2023) <doi:10.48550/arXiv.2305.16464>.

Time series decomposition for univariate time series using the "Verallgemeinerte Berliner Verfahren" (Generalized Berlin Method) as described in Kontinuierliche Messgröà en und Stichprobenstrategien in Raum und Zeit mit Anwendungen in den Natur-, Umwelt-, Wirtschafts- und Finanzwissenschaften', by Hebbel and Steuer, Springer Berlin Heidelberg, 2022 <doi:10.1007/978-3-662-65638-9>, or Decomposition of Time Series using the Generalised Berlin Method (VBV) by Hebbel and Steuer, in Jan Beran, Yuanhua Feng, Hartmut Hebbel (Eds.): Empirical Economic and Financial Research - Theory, Methods and Practice, Festschrift in Honour of Prof. Siegfried Heiler. Series: Advanced Studies in Theoretical and Applied Econometrics. Springer 2014, p. 9-40.

This package provides methods to calculate the expected value of information from a decision-analytic model. This includes the expected value of perfect information (EVPI), partial perfect information (EVPPI) and sample information (EVSI), and the expected net benefit of sampling (ENBS). A range of alternative computational methods are provided under the same user interface. See Heath et al. (2024) <doi:10.1201/9781003156109>, Jackson et al. (2022) <doi:10.1146/annurev-statistics-040120-010730>.

Analyze Peptide Array Data and characterize peptide sequence space. Allows for high level visualization of global signal, Quality control based on replicate correlation and/or relative Kd, calculation of peptide Length/Charge/Kd parameters, Hits selection based on RFU Signal, and amino acid composition/basic motif recognition with RFU signal weighting. Basic signal trends can be used to generate peptides that follow the observed compositional trends.

Estimates the type of variables in non-quality controlled data. The prediction is based on a random forest model, trained on over 5000 medical variables with accuracy of 99%. The accuracy can hardy depend on type and coding style of data.

R implementation of the vol2bird software for generating vertical profiles of birds and other biological signals in weather radar data. See Dokter et al. (2011) <doi:10.1098/rsif.2010.0116> for a paper describing the methodology.

This package provides a visualization for characterizing subgroups defined by a decision tree structure. The visualization simplifies the ability to interpret individual pathways to subgroups; each sub-plot describes the distribution of observations within individual terminal nodes and percentile ranges for the associated inner nodes.

Via Foundry API provides streamlined tools for interacting with and extracting data from structured responses, particularly for use cases involving hierarchical data from Foundry's API. It includes functions to fetch and parse process-level and file-level metadata, allowing users to efficiently query and manipulate nested data structures. Key features include the ability to list all unique process names, retrieve file metadata for specific or all processes, and dynamically load or download files based on their type. With built-in support for handling various file formats (e.g., tabular and non-tabular files) and seamless integration with API through authentication, this package is designed to enhance workflows involving large-scale data management and analysis. Robust error handling and flexible configuration ensure reliable performance across diverse data environments. Please consult the documentation for the API endpoint for your installation.

ProPublica <https://projects.propublica.org/represent/> makes United States Congress member votes available and has developed their own unique cartogram to visually represent this data. Tools are provided to retrieve voting data, prepare voting data for plotting with ggplot2', create vote cartograms and theme them.

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/>.

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.

Vector autoregressive (VAR) model is a fundamental and effective approach for multivariate time series analysis. Shrinkage estimation methods can be applied to high-dimensional VAR models with dimensionality greater than the number of observations, contrary to the standard ordinary least squares method. This package is an integrative package delivering nonparametric, parametric, and semiparametric methods in a unified and consistent manner, such as the multivariate ridge regression in Golub, Heath, and Wahba (1979) <doi:10.2307/1268518>, a James-Stein type nonparametric shrinkage method in Opgen-Rhein and Strimmer (2007) <doi:10.1186/1471-2105-8-S2-S3>, and Bayesian estimation methods using noninformative and informative priors in Lee, Choi, and S.-H. Kim (2016) <doi:10.1016/j.csda.2016.03.007> and Ni and Sun (2005) <doi:10.1198/073500104000000622>.

Variance function estimation for models proposed by W. Sadler in his variance function program ('VFP', www.aacb.asn.au/AACB/Resources/Variance-Function-Program). Here, the idea is to fit multiple variance functions to a data set and consequently assess which function reflects the relationship Var ~ Mean best. For in-vitro diagnostic ('IVD') assays modeling this relationship is of great importance when individual test-results are used for defining follow-up treatment of patients.

This package provides a set of wrapper functions for Visa Chart Components'. Visa Chart Components <https://github.com/visa/visa-chart-components> is an accessibility focused, framework agnostic set of data experience design systems components for the web.

Calculate point estimates of and valid confidence intervals for nonparametric, algorithm-agnostic variable importance measures in high and low dimensions, using flexible estimators of the underlying regression functions. For more information about the methods, please see Williamson et al. (Biometrics, 2020), Williamson et al. (JASA, 2021), and Williamson and Feng (ICML, 2020).

Interactive adverse event (AE) volcano plot for monitoring clinical trial safety. This tool allows users to view the overall distribution of AEs in a clinical trial using standard (e.g. MedDRA preferred term) or custom (e.g. Gender) categories using a volcano plot similar to proposal by Zink et al. (2013) <doi:10.1177/1740774513485311>. This tool provides a stand-along shiny application and flexible shiny modules allowing this tool to be used as a part of more robust safety monitoring framework like the Shiny app from the safetyGraphics R package.

Functions, Classes & Methods for estimation, prediction, and simulation (bootstrap) of Variable Length Markov Chain ('VLMC') Models.

This package implements methods for inference on potential waning of vaccine efficacy and for estimation of vaccine efficacy at a user-specified time after vaccination based on data from a randomized, double-blind, placebo-controlled vaccine trial in which participants may be unblinded and placebo subjects may be crossed over to the study vaccine. The methods also for variant stratification and allow adjustment for possible confounding via inverse probability weighting through specification of models for the trial entry process, unblinding mechanisms, and the probability an unblinded placebo participant accepts study vaccine.

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.