Browse Items (8208 total)

This paper introduces the logitr R package for fast maximum likelihood estimation of
multinomial logit and mixed logit models with unobserved heterogeneity across individuals, which is modeled by allowing parameters to vary randomly over individuals…

The R package BiDAG implements Markov chain Monte Carlo (MCMC) methods for
structure learning and sampling of Bayesian networks. The package includes tools to
search for a maximum a posteriori (MAP) graph and to sample graphs from the…

The R package abn is designed to fit additive Bayesian network models to observational datasets and contains routines to score Bayesian networks based on Bayesian or
information theoretic formulations of generalized linear models. It is equipped…

Despite the large body of research on missing value distributions and imputation, there
is comparatively little literature with a focus on how to make it easy to handle, explore,
and impute missing values in data. This paper addresses this gap. The…

The ergm package supports the statistical analysis and simulation of network data. It
anchors the statnet suite of packages for network analysis in R introduced in a special issue
in Journal of Statistical Software in 2008. This article provides an…

Recurrent event analyses have found a wide range of applications in biomedicine, public
health, and engineering, among others, where study subjects may experience a sequence of
event of interest during follow-up. The R package reReg offers a…

We introduce a Python library, called jumpdiff, which includes all necessary functions to assess jump-diffusion processes. This library includes functions which compute
a set of non-parametric estimators of all contributions composing a…

spsurvey is an R package for design-based statistical inference, with a focus on spatial data. spsurvey provides the generalized random-tessellation stratified (GRTS) algorithm to select spatially balanced samples via the grts() function. The grts()…

In this paper we describe the implementation of semi-structured deep distributional regression, a flexible framework to learn conditional distributions based on the combination
of additive regression models and deep networks. Our implementation…

Sparse graphical models have revolutionized multivariate inference. With the advent
of high-dimensional multivariate data in many applied fields, these methods are able to
detect a much lower-dimensional structure, often represented via a sparse…