Browse Items (10 total)

The smacof package offers a comprehensive implementation of multidimensional scaling (MDS) techniques in R. Since its first publication (De Leeuw and Mair 2009b) the
functionality of the package has been enhanced, and several additional methods,…

Due to tradition and ease of estimation, the vast majority of clinical and epidemiological papers with time-to-event data report hazard ratios from Cox proportional hazards
regression models. Although hazard ratios are well known, they can be…

The R package econet provides methods for estimating parameter-dependent network
centrality measures with linear-in-means models. Both nonlinear least squares and maximum likelihood estimators are implemented. The methods allow for both link and…

This article presents the NeuralSens package that can be used to perform sensitivity
analysis of neural networks using the partial derivatives method. The main function of the
package calculates the partial derivatives of the output with regard to…

stagedtrees is an R package which includes several algorithms for learning the structure of staged trees and chain event graphs from data. Score-based and clustering-based
algorithms are implemented, as well as various functionalities to plot the…

The R package sensobol provides several functions to conduct variance-based uncertainty and sensitivity analysis, from the estimation of sensitivity indices to the visual
representation of the results. It implements several state-of-the-art first…

A graphical model is an undirected network representing the conditional independence
properties between random variables. Graphical modeling has become part and parcel
of systems or network approaches to multivariate data, in particular when the…

In factor analysis and structural equation modeling non-normal data simulation is
traditionally performed by specifying univariate skewness and kurtosis together with the
target covariance matrix. However, this leaves little control over the…

We present the features and implementation of the R package nvmix for the class of
normal variance mixtures including Student t and normal distributions. The package provides functionalities for such distributions, notably the evaluation of the…

Gaussian processes are a class of flexible nonparametric Bayesian tools that are widely
used across the sciences, and in industry, to model complex data sources. Key to applying
Gaussian process models is the availability of well-developed open…