REMLA: An R package for robust expectation-maximization estimation for latent variable models
Factor analysis is a widely used statistical method for describing a large number of observed, correlated variables in terms of a smaller number of unobserved variables. Applications of this method usually impose the same latent variable model on all individuals in the sample, but this assumption mi...
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| Language: | English |
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Elsevier
2025-05-01
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352711025000792 |
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| author | Kenneth J. Nieser Bryan Saúl Ortiz-Torres Gabriel Zayas-Cabán Amy Cochran |
| author_facet | Kenneth J. Nieser Bryan Saúl Ortiz-Torres Gabriel Zayas-Cabán Amy Cochran |
| author_sort | Kenneth J. Nieser |
| collection | DOAJ |
| description | Factor analysis is a widely used statistical method for describing a large number of observed, correlated variables in terms of a smaller number of unobserved variables. Applications of this method usually impose the same latent variable model on all individuals in the sample, but this assumption might not hold as individuals can differ in attributes (e.g., age, gender) that influence model parameters. REMLA is an R package that implements a robust expectation–maximization (REM) algorithm to estimate the parameters for factor analysis models in a way that automatically acknowledges, and even detects, differences among individuals within the sample. This paper explains the methodological background of the estimation process, describes the algorithms employed, and illustrates how REMLA can be used to perform exploratory and confirmatory factor analyses through examples. In the future, we plan to extend this package to other latent variable models, such as mixture models. |
| format | Article |
| id | doaj-art-93c8f435226a407eabe9ebd2609823fc |
| institution | Kabale University |
| issn | 2352-7110 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Elsevier |
| record_format | Article |
| series | SoftwareX |
| spelling | doaj-art-93c8f435226a407eabe9ebd2609823fc2025-08-20T03:48:13ZengElsevierSoftwareX2352-71102025-05-013010211210.1016/j.softx.2025.102112REMLA: An R package for robust expectation-maximization estimation for latent variable modelsKenneth J. Nieser0Bryan Saúl Ortiz-Torres1Gabriel Zayas-Cabán2Amy Cochran3Center for Innovation to Implementation, VA Palo Alto Health Care System, Menlo Park, CA, United States of America; Stanford-Surgery Policy Improvement Research and Education Center, Department of Surgery, Stanford University, Stanford, CA, United States of America; Correspondence to: 795 Willow Road, Bldg. 324, Menlo Park, CA, United States of America.Department of Industrial and Systems Engineering, University of Wisconsin–Madison, Madison, WI, United States of AmericaDepartment of Industrial and Systems Engineering, University of Wisconsin–Madison, Madison, WI, United States of AmericaDepartment of Population Health Sciences, University of Wisconsin–Madison, Madison, WI, United States of America; Department of Mathematics, University of Wisconsin–Madison, Madison, WI, United States of AmericaFactor analysis is a widely used statistical method for describing a large number of observed, correlated variables in terms of a smaller number of unobserved variables. Applications of this method usually impose the same latent variable model on all individuals in the sample, but this assumption might not hold as individuals can differ in attributes (e.g., age, gender) that influence model parameters. REMLA is an R package that implements a robust expectation–maximization (REM) algorithm to estimate the parameters for factor analysis models in a way that automatically acknowledges, and even detects, differences among individuals within the sample. This paper explains the methodological background of the estimation process, describes the algorithms employed, and illustrates how REMLA can be used to perform exploratory and confirmatory factor analyses through examples. In the future, we plan to extend this package to other latent variable models, such as mixture models.http://www.sciencedirect.com/science/article/pii/S2352711025000792Expectation–maximization algorithmRobust statisticsStructural equation modelingLatent variablesFactor analysis |
| spellingShingle | Kenneth J. Nieser Bryan Saúl Ortiz-Torres Gabriel Zayas-Cabán Amy Cochran REMLA: An R package for robust expectation-maximization estimation for latent variable models SoftwareX Expectation–maximization algorithm Robust statistics Structural equation modeling Latent variables Factor analysis |
| title | REMLA: An R package for robust expectation-maximization estimation for latent variable models |
| title_full | REMLA: An R package for robust expectation-maximization estimation for latent variable models |
| title_fullStr | REMLA: An R package for robust expectation-maximization estimation for latent variable models |
| title_full_unstemmed | REMLA: An R package for robust expectation-maximization estimation for latent variable models |
| title_short | REMLA: An R package for robust expectation-maximization estimation for latent variable models |
| title_sort | remla an r package for robust expectation maximization estimation for latent variable models |
| topic | Expectation–maximization algorithm Robust statistics Structural equation modeling Latent variables Factor analysis |
| url | http://www.sciencedirect.com/science/article/pii/S2352711025000792 |
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