https://github.com/yrosseel/lavaan

an R package for structural equation modeling and more
https://github.com/yrosseel/lavaan

factor-analysis growth-curve-models latent-variables missing-data multilevel-models multivariate-analysis path-analysis psychometrics statistical-modeling structural-equation-modeling

Last synced: 3 months ago
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an R package for structural equation modeling and more

• Host: GitHub
• URL: https://github.com/yrosseel/lavaan
• Owner: yrosseel
• Created: 2011-04-27T19:38:59.000Z (over 13 years ago)
• Default Branch: master
• Last Pushed: 2024-04-12T12:20:32.000Z (7 months ago)
• Last Synced: 2024-04-12T15:05:36.347Z (7 months ago)
• Topics: factor-analysis, growth-curve-models, latent-variables, missing-data, multilevel-models, multivariate-analysis, path-analysis, psychometrics, statistical-modeling, structural-equation-modeling
• Language: R
• Homepage: http://lavaan.org
• Size: 8.53 MB
• Stars: 396
• Watchers: 44
• Forks: 93
• Open Issues: 22
• Metadata Files:
  • Readme: README.md
  • Contributing: contributing.md
  • Code of conduct: code_of_conduct.md

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README

        
# lavaan

lavaan is a free, open source R package for latent variable analysis. You can

use lavaan to estimate a large variety of multivariate statistical models,

including path analysis, confirmatory factor analysis, structural equation

modeling and growth curve models.

The lavaan package is developed to provide useRs, researchers and teachers a

free open-source, but commercial-quality package for latent variable modeling.

The long-term goal of lavaan is to implement all the state-of-the-art

capabilities that are currently available in commercial packages. However,

lavaan is still under development, and much work still needs to be done.

To get a first impression of how lavaan works in practice, consider the

following example of a SEM model (the Political Democracy Example from 

Bollen's 1989 book):

```R

library(lavaan)

model <- '

   # latent variable definitions

     ind60 =~ x1 + x2 + x3

     dem60 =~ y1 + y2 + y3 + y4

     dem65 =~ y5 + y6 + y7 + y8

   # regressions

     dem60 ~ ind60

     dem65 ~ ind60 + dem60

   # residual covariances

     y1 ~~ y5

     y2 ~~ y4 + y6

     y3 ~~ y7

     y4 ~~ y8

     y6 ~~ y8

'

fit <- sem(model, data = PoliticalDemocracy)

summary(fit)

```

More information can be found on the website: https://lavaan.org