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

Create adjacency matrices of vocalisation graphs from dataframes containing sequences of speech and silence intervals, transforming these matrices into Markov diagrams, and generating datasets for classification of these diagrams by flattening them and adding global properties (functionals) etc. Vocalisation diagrams date back to early work in psychiatry (Jaffe and Feldstein, 1970) and social psychology (Dabbs and Ruback, 1987) but have only recently been employed as a data representation method for machine learning tasks including meeting segmentation (Luz, 2012) <doi:10.1145/2328967.2328970> and classification (Luz, 2013) <doi:10.1145/2522848.2533788>.

Generation of domain variables, linearization of several non-linear population statistics (the ratio of two totals, weighted income percentile, relative median income ratio, at-risk-of-poverty rate, at-risk-of-poverty threshold, Gini coefficient, gender pay gap, the aggregate replacement ratio, the relative median income ratio, median income below at-risk-of-poverty gap, income quintile share ratio, relative median at-risk-of-poverty gap), computation of regression residuals in case of weight calibration, variance estimation of sample surveys by the ultimate cluster method (Hansen, Hurwitz and Madow, Sample Survey Methods And Theory, vol. I: Methods and Applications; vol. II: Theory. 1953, New York: John Wiley and Sons), variance estimation for longitudinal, cross-sectional measures and measures of change for single and multistage stage cluster sampling designs (Berger, Y. G., 2015, <doi:10.1111/rssa.12116>). Several other precision measures are derived - standard error, the coefficient of variation, the margin of error, confidence interval, design effect.

This package provides new classes for (rotated) BB1, BB6, BB7, BB8, and Tawn copulas, extends the existing Gumbel and Clayton families with rotations, and allows to set up a vine copula model using the copula API. Corresponding objects from the VineCopula API can easily be converted.

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.

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 tools for reporting and forecasting viral respiratory infections, using case surveillance data. Report generation tools for short-term forecasts, and validation metrics for an arbitrary number of customizable respiratory viruses. Estimation of the effective reproduction number is based on the EpiEstim framework described in work by Cori and colleagues. (2013) <doi:10.1093/aje/kwt133>.

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 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 implements variable screening techniques for ultra-high dimensional regression settings. Techniques for independent (iid) data, varying-coefficient models, and longitudinal data are implemented. The package currently contains three screen functions: screenIID(), screenLD() and screenVCM(), and six methods for simulating dataset: simulateDCSIS(), simulateLD, simulateMVSIS(), simulateMVSISNY(), simulateSIRS() and simulateVCM(). The package is based on the work of Li-Ping ZHU, Lexin LI, Runze LI, and Li-Xing ZHU (2011) <DOI:10.1198/jasa.2011.tm10563>, Runze LI, Wei ZHONG, & Liping ZHU (2012) <DOI:10.1080/01621459.2012.695654>, Jingyuan LIU, Runze LI, & Rongling WU (2014) <DOI:10.1080/01621459.2013.850086> Hengjian CUI, Runze LI, & Wei ZHONG (2015) <DOI:10.1080/01621459.2014.920256>, and Wanghuan CHU, Runze LI and Matthew REIMHERR (2016) <DOI:10.1214/16-AOAS912>.

Using frequency matrices, very low frequency variants (VLFs) are assessed for amino acid and nucleotide sequences. The VLFs are then compared to see if they occur in only one member of a species, singleton VLFs, or if they occur in multiple members of a species, shared VLFs. The amino acid and nucleotide VLFs are then compared to see if they are concordant with one another. Amino acid VLFs are also assessed to determine if they lead to a change in amino acid residue type, and potential changes to protein structures. Based on Stoeckle and Kerr (2012) <doi:10.1371/journal.pone.0043992> and Phillips et al. (2023) <doi:10.3897/BDJ.11.e96480>.

Random generation, density function and parameter estimation for the Voigt distribution. The main objective of this package is to provide R users with efficient estimation of Voigt parameters using classic iid data in a Bayesian framework. The estimating function allows flexible prior specification, specification of fixed parameters and several options for Markov Chain Monte Carlo posterior simulation. A basic version of the algorithm is described in: Cannas M. and Piras, N. (2025) <doi:10.1007/978-3-031-96303-2_53>.

This package provides additional data sets, methods and documentation to complement the vcd package for Visualizing Categorical Data and the gnm package for Generalized Nonlinear Models. In particular, vcdExtra extends mosaic, assoc and sieve plots from vcd to handle glm() and gnm() models and adds a 3D version in mosaic3d'. Additionally, methods are provided for comparing and visualizing lists of glm and loglm objects. This package is now a support package for the book, "Discrete Data Analysis with R" by Michael Friendly and David Meyer.

This package provides tools for designing virus protein panels through sequence clustering and protein sequence analysis. The package includes functionality for filtering sequences, removing redundancy, identifying outliers, clustering sequences, and calculating entropy to evaluate clustering quality. A publication describing these methods is in preparation and will be added once available.

This package performs 18 omnibus tests yielding a total of 28 distinct methodological variations for testing the composite hypothesis of variance homogeneity.

Estimates and plots as a heat map the correlation coefficients obtained via the wavelet local multiple correlation WLMC (Fernández-Macho 2018) and the dominant variable/s, i.e., the variable/s that maximizes the multiple correlation through time and scale (Polanco-Martà nez et al. 2020, Polanco-Martà nez 2022). We improve the graphical outputs of WLMC proposing a didactic and useful way to visualize the dominant variable(s) for a set of time series. The WLMC was designed for financial time series, but other kinds of data (e.g., climatic, ecological, etc.) can be used. The functions contained in VisualDom are highly flexible since these contains several parameters to personalize the time series under analysis and the heat maps. In addition, we have also included two data sets (named rdata_climate and rdata_Lorenz') to exemplify the use of the functions contained in VisualDom'. Methods derived from Fernández-Macho (2018) <doi:10.1016/j.physa.2017.11.050>, Polanco-Martà nez et al. (2020) <doi:10.1038/s41598-020-77767-8> and Polanco-Martà nez (2023, in press).

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.

Extending the functionalities of the VGAM package with additional functions and datasets. At present, VGAMextra comprises new family functions (ffs) to estimate several time series models by maximum likelihood using Fisher scoring, unlike popular packages in CRAN relying on optim(), including ARMA-GARCH-like models, the Order-(p, d, q) ARIMAX model (non- seasonal), the Order-(p) VAR model, error correction models for cointegrated time series, and ARMA-structures with Student-t errors. For independent data, new ffs to estimate the inverse- Weibull, the inverse-gamma, the generalized beta of the second kind and the general multivariate normal distributions are available. In addition, VGAMextra incorporates new VGLM-links for the mean-function, and the quantile-function (as an alternative to ordinary quantile modelling) of several 1-parameter distributions, that are compatible with the class of VGLM/VGAM family functions. Currently, only fixed-effects models are implemented. All functions are subject to change; see the NEWS for further details on the latest changes.

Visualize and compute percentiles/probabilities of normal, t, f, chi square and binomial distributions.

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.

Fit and simulate latent position and cluster models for network data, using a fast Variational Bayes approximation developed in Salter-Townshend and Murphy (2013) <doi:10.1016/j.csda.2012.08.004>.

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>.

Utilizes multiple variable selection methods to estimate Average Treatment Effect.

Realization of published methods to analyze visual field (VF) progression. Introduction to the plotting methods (designed by author TE) for VF output visualization. A sample dataset for two eyes, each with 10 follow-ups is included. The VF analysis methods could be found in -- Musch et al. (1999) <doi:10.1016/S0161-6420(99)90147-1>, Nouri-Mahdavi et at. (2012) <doi:10.1167/iovs.11-9021>, Schell et at. (2014) <doi:10.1016/j.ophtha.2014.02.021>, Aptel et al. (2015) <doi:10.1111/aos.12788>.