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lavaan
lavaan is a freely available R package for structural equation modelling - which IMO serves as the main competition against MPlus.
lavaan is wonderful because it is free. It has a massive user base - and there is an open Google conversations group free to access and post to. Developers consistently engage with users there for free advice.
At NSB, many of us prefer lavaan over MPlus - particularly for more basic models. However, one cannot run person-centred dimension reduction (or mixture modelling) in lavaan - one would have to move to MPlus for this (or look around for other packages!)
If you are working mostly in R anyway, I would typically suggest lavaan over MPlus. For people more familiar with coding especially, lavaan is typically much more intuitive.
Please check out the following pages for tutorials and other helpful information on using lavaan :
The package itself + some of the basic capabilities:
https://lavaan.ugent.be/tutorial/
https://m-clark.github.io/docs/FA_notes.html
https://m-clark.github.io/docs/lv_sim.html
Running mediation:
https://ademos.people.uic.edu/Chapter15.html
Group comparisons:
https://m-clark.github.io/posts/2019-08-05-comparing-latent-variables/
Sensitivity analyses:
https://github.com/sfcheung/semfindr
https://cran.r-project.org/web/packages/SEMsens/vignettes/SEMsens.html
Other helpful packages (e.g. for using multiple imputed datasets, probing at levels of a moderator, rerunning with confidence intervals, etc)
https://cran.r-project.org/web/packages/semTools/index.html
https://github.com/TDJorgensen/lavaan.mi
https://sfcheung.github.io/manymome/
https://sfcheung.github.io/semlbci/
https://sfcheung.github.io/semhelpinghands/
https://sfcheung.github.io/semptools/
https://sfcheung.github.io/stdmod/
Bayesian lavaan:
- 0.0 Home
- 0.1 Neuroscience fundamentals
- 0.2 Reproducible Science
- 0.3 MRI Physics, BIDS, DICOM, and data formats
- 0.4 Introduction to Diffusion MRI
- 0.5 Introduction to Functional MRI
- 0.6 Measuring functional and effective connectivity
- 0.7 Connectomics, graph theory, and complexity
- 0.8 Statistical and Mathematical Tidbits
- 0.9 Introduction to Psychopathology
- 0.10 Introduction to Genetics and Bioinformatics
- 0.11 Introduction to Programming
- 1.0 Working on the Cluster
- 2.0 Programming Languages
- 2.1 Python
- 2.2 MATLAB
- 2.3 R and RStudio
- 2.4 Programming Intro Exercises
- 2.5 git and GitHub
- 2.6 SLURM and Job Submission
- 3.0 Neuroimaging Tools and Packages
- 3.1 BIDS
- 3.2 FreeSurfer
- 3.2.1 Qdec
- 3.3 FSL
- 3.3.1 ICA-FIX
- 3.4 Connectome Workbench/wb_command
- 3.5 fMRIPrep
- 3.6 QSIPrep
- 3.7 HCP Pipeline
- 3.8 tedana
- 4.0 Quality control
- 4.1 MRIQC
- 4.2 Common Artefacts
- 4.3 T1w
- 4.4 rs-fMRI
- 5.0 Specialist Tools
- 6.0 Putting it all together
- 7.0 Data management