This package provides a nonparametric method to estimate Toeplitz covariance matrices from a sample of n independently and identically distributed p-dimensional vectors with mean zero. The data is preprocessed with the discrete cosine matrix and a variance stabilization transformation to obtain an approximate Gaussian regression setting for the log-spectral density function. Estimates of the spectral density function and the inverse of the covariance matrix are provided as well. Functions for simulating data and a protein data example are included. For details see (Klockmann, Krivobokova; 2023), <arXiv:2303.10018>.

Collection of functions to evaluate presence-absence models. It comprises functions to adjust discrimination statistics for the representativeness effect through case-weighting, along with functions for visualizing the outcomes. Originally outlined in: Jiménez-Valverde (2022) The uniform AUC: dealing with the representativeness effect in presence-absence models. Methods Ecol. Evol, 13, 1224-1236.

Various semiparametric and nonparametric statistical tools for immune correlates analysis of vaccine clinical trial data. This includes calculation of summary statistics and estimation of risk, vaccine efficacy, controlled effects (controlled risk and controlled vaccine efficacy), and mediation effects (natural direct effect, natural indirect effect, proportion mediated). See Gilbert P, Fong Y, Kenny A, and Carone, M (2022) <doi:10.1093/biostatistics/kxac024> and Fay MP and Follmann DA (2023) <doi:10.48550/arXiv.2208.06465>.

This package provides a binding for the valection program which offers various ways to sample the outputs of competing algorithms or parameterizations, and fairly assess their performance against each other. The valection C library is required to use this package and can be downloaded from: <http://labs.oicr.on.ca/boutros-lab/software/valection>. Cooper CI, et al; Valection: Design Optimization for Validation and Verification Studies; Biorxiv 2018; <doi:10.1101/254839>.

The variable importance is calculated using knock off variables. Then output can be provided in numerical and graphical form. Meredith L Wallace (2023) <doi:10.1186/s12874-023-01965-x>.

This package provides fast spectral estimation of latent factors in random dot product graphs using the vsp estimator. Under mild assumptions, the vsp estimator is consistent for (degree-corrected) stochastic blockmodels, (degree-corrected) mixed-membership stochastic blockmodels, and degree-corrected overlapping stochastic blockmodels.

This package provides access to data collected by the Ecuadorian Truth Commission. Allows users to extract and analyze systematized information for human rights research in Ecuador. The package contains datasets documenting human rights violations from 1984-2008, including victim information, violation types, perpetrators, and geographic distribution.

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>.

Uses a Bayesian model to estimate the variability in a repeated measure outcome and use that as an outcome or a predictor in a second stage model.

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

Estimates hierarchical models using variational inference. At present, it can estimate logistic, linear, and negative binomial models. It can accommodate models with an arbitrary number of random effects and requires no integration to estimate. It also provides the ability to improve the quality of the approximation using marginal augmentation. Goplerud (2022) <doi:10.1214/21-BA1266> and Goplerud (2024) <doi:10.1017/S0003055423000035> provide details on the variational algorithms.

Visual contour and 2D point and contour plots for binary classification modeling under algorithms such as glm', rf', gbm', nnet and svm', presented over two dimensions generated by famd and mca methods. Package FactoMineR for multivariate reduction functions and package MBA for interpolation functions are used. The package can be used to visualize the discriminant power of input variables and algorithmic modeling, explore outliers, compare algorithm behaviour, etc. It has been created initially for teaching purposes, but it has also many practical uses under the XAI paradigm.

This package provides low-level access to GDAL functionality. GDAL is the Geospatial Data Abstraction Library a translator for raster and vector geospatial data formats that presents a single raster abstract data model and single vector abstract data model to the calling application for all supported formats <https://gdal.org/>. This package is focussed on providing exactly and only what GDAL does, to enable developing further tools.

This package provides a Shiny application for the interactive visualisation and analysis of networks that also provides a web interface for collecting social media data using vosonSML'.

This package provides tools to estimate the impact of vaccination campaigns at population level (number of events averted, number of avertable events, number needed to vaccinate). Inspired by the methodology proposed by Foppa et al. (2015) <doi:10.1016/j.vaccine.2015.02.042> and Machado et al. (2019) <doi:10.2807/1560-7917.ES.2019.24.45.1900268> for influenza vaccination impact.

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>].

An interface between R and the Valhalla API. Valhalla is a routing service based on OpenStreetMap data. See <https://valhalla.github.io/valhalla/> for more information. This package enables the computation of routes, trips, isochrones and travel distances matrices (travel time and kilometer distance).

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.

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>.

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.

Simplifies functions assess normality for bivariate and multivariate statistical techniques. Includes functions designed to replicate plots and tables that would result from similar calls in SPSS', including hst(), box(), qq(), tab(), cormat(), and residplot(). Also includes simplified formulae, such as mode(), scatter(), p.corr(), ow.anova(), and rm.anova().

Implementation of shiny app to visualize adverse events based on the Common Terminology Criteria for Adverse Events (CTCAE) using stacked correspondence analysis as described in Diniz et. al (2021)<doi:10.1186/s12874-021-01368-w>.

This package creates visualization plots for 2D projected data including ellipse plots, Voronoi diagram plots, and combined ellipse-Voronoi plots. Designed to visualize class separation in dimensionally reduced data from techniques like principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA) or others. For more details see Lotsch and Ultsch (2024) <doi:10.1016/j.imu.2024.101573>.

This package provides a general framework for constructing variable importance plots from various types of machine learning models in R. Aside from some standard model- specific variable importance measures, this package also provides model- agnostic approaches that can be applied to any supervised learning algorithm. These include 1) an efficient permutation-based variable importance measure, 2) variable importance based on Shapley values (Strumbelj and Kononenko, 2014) <doi:10.1007/s10115-013-0679-x>, and 3) the variance-based approach described in Greenwell et al. (2018) <doi:10.48550/arXiv.1805.04755>. A variance-based method for quantifying the relative strength of interaction effects is also included (see the previous reference for details).