% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/pulsar-package.R
\docType{package}
\name{pulsar-package}
\alias{pulsar-package}
\title{The pulsar package}
\description{
Graphical model selection with the pulsar package
}
\details{
This package provides methods to select a sparse, undirected graphical model by choosing a penalty parameter (lambda or \eqn{\lambda}) among a list of ordered values of lambda. We use an implementation of the Stability Approach to Regularization Selection (StARS, see references) inspired by the \pkg{huge} package.

However, \pkg{pulsar} includes some major differences from other R packages for graphical model estimation and selection (\pkg{glasso}, \pkg{huge}, \pkg{QUIC}, \pkg{XMRF}, \pkg{clime}, \pkg{flare}, etc). The underlying graphical model is computed by passing a function as an argument to \code{\link{pulsar}}. Thus, any algorithm for penalized graphical models can be used in this framework (see \code{\link{pulsar-function}} for more details), including those from the above packages. \pkg{pulsar} brings computational experiments under one roof by separating subsampling and calculation of summary criteria from the user-specified core model. The typical workflow in \pkg{pulsar} is to perform subsampling first (via the \code{\link{pulsar}}) and then refit the model on the full dataset using \code{\link{refit}}.

Previous StARS implementations can be inefficient for large graphs or when many subsamples are required. \code{\link{pulsar}} can compute upper and lower bounds on the regularization path for the StARS criterion after only \eqn{2} subsamples which makes it possible to neglect lambda values that are far from the desired StARS regularization parameter, reducing computation time for the rest of the \eqn{N-2} subsamples (Bounded StARS (B-StARS)).

We also implement additional subsampling-based graph summary criteria which can be used for more informed model selection. For example, we have shown that induced subgraph (graphlet) stability (G-StARS) improves empirical performance over StARS but other criteria are also offered.

Subsampling amounts to running the specified core model for \eqn{N} independent computations. Using the \pkg{batchtools} framework, we provide a simple wrapper, \code{batch.pulsar}, for running \code{\link{pulsar}} in embarrassingly parallel mode in an hpc environment. Summary criteria are computed using a Map/Reduce strategy, which lowers memory footprint for large models.
}
\references{
MÃ¼ller, C. L., Bonneau, R. A., & Kurtz, Z. D. (2016).Generalized Stability Approach for Regularized Graphical Models.arXiv: https://arxiv.org/abs/1605.07072.
}
\seealso{
\code{\link{pulsar-function}}, \code{\link{pulsar}}, \code{\link{batch.pulsar}}
}
