Browse Items (8254 total)

In a world with data that change rapidly and abruptly, it is important to detect those
changes accurately. In this paper we describe an R package implementing a generalized
version of an algorithm recently proposed by Hocking, Rigaill, Fearnhead,…

The study of non-stationary behavior in the extremes is important to analyze data
in environmental sciences, climate, finance, or sports. As an alternative to the classical
extreme value theory, this analysis can be based on the study of…

Analysis of dose-response data is an important step in many scientific disciplines,
including but not limited to pharmacology, toxicology, and epidemiology. The R package
drda is designed to facilitate the analysis of dose-response data by…

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…