Browse Items (8341 total)

The R package MLGL, standing for multi-layer group-Lasso, implements a new procedure of variable selection in the context of redundancy between explanatory variables,
which holds true with high-dimensional data. A sparsity assumption is made that…

Network meta-analysis compares different interventions for the same condition, by
combining direct and indirect evidence derived from all eligible studies. Network metaanalysis has been increasingly used by applied scientists and it is a major…

The lasso and elastic net are popular regularized regression models for supervised
learning. Friedman, Hastie, and Tibshirani (2010) introduced a computationally efficient
algorithm for computing the elastic net regularization path for ordinary…

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…