lslx: Semi-Confirmatory Structural Equation Modeling via Penalized Likelihood
Abstract
Sparse estimation via penalized likelihood (PL) is now a popular approach to learn the associations among a large set of variables. This paper describes an R package called lslx that implements PL methods for semi-confirmatory structural equation modeling (SEM). In this semi-confirmatory approach, each model parameter can be specified as free/fixed for theory testing, or penalized for exploration. By incorporating either a L1 or minimax concave...
Paper Details
Title
lslx: Semi-Confirmatory Structural Equation Modeling via Penalized Likelihood
Published Date
Jan 1, 2020
Volume
93
Issue
7
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