Browse Items (8208 total)

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…

The stochastic block model is a popular probabilistic model for random graphs. It is
commonly used to cluster network data by aggregating nodes that share similar connectivity patterns into blocks. When fitting a stochastic block model to a…

Bayesian synthetic likelihood (BSL; Price, Drovandi, Lee, and Nott 2018) is a popular
method for estimating the parameter posterior distribution for complex statistical models
and stochastic processes that possess a computationally intractable…

We describe a new algorithm and R package for peak detection in genomic data sets
using constrained changepoint models. These detect changes from background to peak
regions by imposing the constraint that the mean should alternately increase then…

Linear transformation models, including the proportional hazards model and proportional odds model, under right censoring were discussed by Chen, Jin, and Ying (2002).
The asymptotic variance of the estimator they proposed has a closed form and can…

Markov random fields on two-dimensional lattices are behind many image analysis
methodologies. mrf2d provides tools for statistical inference on a class of discrete stationary Markov random field models with pairwise interaction, which includes many…