Lslx: Semi-confirmatory structural equation modeling via penalized likelihood

Po Hsien Huang

Research output: Contribution to journalArticlepeer-review

9 Citations (Scopus)


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 penalty, the sparsity pattern of the parameter matrix can be efficiently explored. Package lslx minimizes the PL criterion through a quasi-Newton method. The algorithm conducts line search and checks the first-order condition in each iteration to ensure the optimality of the obtained solution. A numerical comparison between competing packages shows that lslx can reliably find PL estimates with the least time. The current package also supports other advanced functionalities, including a two-stage method with auxiliary variables for missing data handling and a reparameterized multi-group SEM to explore population heterogeneity.

Original languageEnglish
JournalJournal of Statistical Software
Issue number7
Publication statusPublished - 2020

All Science Journal Classification (ASJC) codes

  • Software
  • Statistics and Probability
  • Statistics, Probability and Uncertainty


Dive into the research topics of 'Lslx: Semi-confirmatory structural equation modeling via penalized likelihood'. Together they form a unique fingerprint.

Cite this