Oh bla di
Oh blavaan
Life goes onnnnn, oh!
Oh blavaan
Life goes onnnnn, oh!
February 20, 2026 at 2:46 PM
Oh bla di
Oh blavaan
Life goes onnnnn, oh!
Oh blavaan
Life goes onnnnn, oh!
Learning #bayesian #SEM #rstats #package #blavaan then found this tutorial sites mc-stan.org/learn-stan/c... ecmerkle.github.io/blavaan/
Bayesian Structural Equation Modeling using blavaan
mc-stan.org
October 19, 2025 at 10:11 AM
Learning #bayesian #SEM #rstats #package #blavaan then found this tutorial sites mc-stan.org/learn-stan/c... ecmerkle.github.io/blavaan/
I am joining bsky around my one-year anniversary of joining mastodon. I have recently worked on Bayesian latent variable modeling in Stan, with progress including
Two-level models: ecmerkle.github.io/blavaan/arti...
Models of ordinal variables: ecmerkle.github.io/blavaan/arti...
Two-level models: ecmerkle.github.io/blavaan/arti...
Models of ordinal variables: ecmerkle.github.io/blavaan/arti...
October 27, 2023 at 2:08 PM
I am joining bsky around my one-year anniversary of joining mastodon. I have recently worked on Bayesian latent variable modeling in Stan, with progress including
Two-level models: ecmerkle.github.io/blavaan/arti...
Models of ordinal variables: ecmerkle.github.io/blavaan/arti...
Two-level models: ecmerkle.github.io/blavaan/arti...
Models of ordinal variables: ecmerkle.github.io/blavaan/arti...
At last week's NCME conference, I did a workshop on Bayesian latent variable modeling with a focus on the blavaan package. We discussed factor analysis, item response models, and two-level structural equation models. Slides and code are at the first bullet:
Resources
blavaan
ecmerkle.github.io
April 17, 2024 at 2:36 PM
At last week's NCME conference, I did a workshop on Bayesian latent variable modeling with a focus on the blavaan package. We discussed factor analysis, item response models, and two-level structural equation models. Slides and code are at the first bullet:
Here is a newly-accepted paper (at Methodology) discussing issues and unexpected behavior of WAIC and PSIS-LOO when applied to psychometric models. It is based on situations that I initially thought were bugs in blavaan, but they turned out not to be.
(download link is at the bottom of the page)
(download link is at the bottom of the page)
Nuances of information criteria for Bayesian psychometric models [Author Accepted Manuscript] | PsychArchives
PsychArchives is a disciplinary repository for psychological science and neighboring disciplines.
doi.org
April 29, 2026 at 4:40 PM
Here is a newly-accepted paper (at Methodology) discussing issues and unexpected behavior of WAIC and PSIS-LOO when applied to psychometric models. It is based on situations that I initially thought were bugs in blavaan, but they turned out not to be.
(download link is at the bottom of the page)
(download link is at the bottom of the page)
blavaan 0.5-6 was recently released to CRAN. It has always done Bayesian structural equation modeling, but in recent years we have added some functionality for ordinal observed variables and for two-level SEM (random intercepts). More info here:
Bayesian Latent Variable Analysis
Fit a variety of Bayesian latent variable models, including confirmatory factor analysis, structural equation models, and latent growth curve models. References: Merkle & Rosseel (2018) <doi:10.18637/...
ecmerkle.github.io
September 20, 2024 at 7:10 PM
blavaan 0.5-6 was recently released to CRAN. It has always done Bayesian structural equation modeling, but in recent years we have added some functionality for ordinal observed variables and for two-level SEM (random intercepts). More info here:
Bayesian estimation sometimes makes distributional assumptions about the data that bootstrap does not. We put blavaan into this paper (on nonnormality) in part to illustrate that it is not inherently robust to distributional assumption violations: osf.io/preprints/ps...
OSF
osf.io
September 22, 2025 at 2:26 AM
Bayesian estimation sometimes makes distributional assumptions about the data that bootstrap does not. We put blavaan into this paper (on nonnormality) in part to illustrate that it is not inherently robust to distributional assumption violations: osf.io/preprints/ps...
IRTM trades off some generality in specification, as packages such as brms or blavaan can achieve, for substantially increased speed: it runs on data with 10k respondents and 200+ items with 4 latent dimensions in about 10 minutes, regardless of the complexity of the pre-specified connections. 3/7
May 22, 2025 at 2:46 PM
IRTM trades off some generality in specification, as packages such as brms or blavaan can achieve, for substantially increased speed: it runs on data with 10k respondents and 200+ items with 4 latent dimensions in about 10 minutes, regardless of the complexity of the pre-specified connections. 3/7
#statstab #278 Bayesian Latent Variable Modeling in R with
{blavaan}
Thoughts: This model has grown a lot, but there is little complete guidance on how to use it. Here is the dev talking about it.
#bayesian #SEM #CFA #blavaan #r #lavaan #pathanalysis
youtu.be/xJy5zIkzgvg
{blavaan}
Thoughts: This model has grown a lot, but there is little complete guidance on how to use it. Here is the dev talking about it.
#bayesian #SEM #CFA #blavaan #r #lavaan #pathanalysis
youtu.be/xJy5zIkzgvg
Bayesian Latent Variable Modeling in R with {blavaan}
YouTube video by UseR Oslo
youtu.be
February 12, 2025 at 6:30 PM
#statstab #278 Bayesian Latent Variable Modeling in R with
{blavaan}
Thoughts: This model has grown a lot, but there is little complete guidance on how to use it. Here is the dev talking about it.
#bayesian #SEM #CFA #blavaan #r #lavaan #pathanalysis
youtu.be/xJy5zIkzgvg
{blavaan}
Thoughts: This model has grown a lot, but there is little complete guidance on how to use it. Here is the dev talking about it.
#bayesian #SEM #CFA #blavaan #r #lavaan #pathanalysis
youtu.be/xJy5zIkzgvg
It does seem to implement models already available in blavaan (ecmerkle.github.io/blavaan/) and/or ctsem (github.com/cdriveraus/c...), but I didn't read that closely so not sure.
You prob know already there is a mplus2lavaan() function --- people tell me it works usually?
You prob know already there is a mplus2lavaan() function --- people tell me it works usually?
March 7, 2025 at 9:50 AM
It does seem to implement models already available in blavaan (ecmerkle.github.io/blavaan/) and/or ctsem (github.com/cdriveraus/c...), but I didn't read that closely so not sure.
You prob know already there is a mplus2lavaan() function --- people tell me it works usually?
You prob know already there is a mplus2lavaan() function --- people tell me it works usually?
Very cool! There's also blavaan that uses stan, so would be great to have a comparison.
December 17, 2024 at 4:28 PM
Very cool! There's also blavaan that uses stan, so would be great to have a comparison.
semPlot 1.2.0
• Support for psychonetrics lvm() and varcov() models
• Standardized psychonetrics parameters
• Support for PLS-SEM, lavaan.mi, piecewiseSEM, metaSEM, psych::fa(), psych::omega(), and blavaan
• New importer API
• Lighter dependencies
• Support for psychonetrics lvm() and varcov() models
• Standardized psychonetrics parameters
• Support for PLS-SEM, lavaan.mi, piecewiseSEM, metaSEM, psych::fa(), psych::omega(), and blavaan
• New importer API
• Lighter dependencies
July 18, 2026 at 3:57 AM
semPlot 1.2.0
• Support for psychonetrics lvm() and varcov() models
• Standardized psychonetrics parameters
• Support for PLS-SEM, lavaan.mi, piecewiseSEM, metaSEM, psych::fa(), psych::omega(), and blavaan
• New importer API
• Lighter dependencies
• Support for psychonetrics lvm() and varcov() models
• Standardized psychonetrics parameters
• Support for PLS-SEM, lavaan.mi, piecewiseSEM, metaSEM, psych::fa(), psych::omega(), and blavaan
• New importer API
• Lighter dependencies
Motivation: #brms is popular but uses conditional likelihood; #blavaan can’t handle random slopes; which criteria to use given software constraints? Lowest value wins is best?
Repo: github.com/falkcarl/bML...
Has code with lmer-like syntax to estimate models with #stan, marginal WAIC and LOO-CV.
Repo: github.com/falkcarl/bML...
Has code with lmer-like syntax to estimate models with #stan, marginal WAIC and LOO-CV.
GitHub - falkcarl/bMLMFit: Data and code repository for Linear Multilevel Bayesian Regression Selection: Comparing Three Predictive Information Criteria
Data and code repository for Linear Multilevel Bayesian Regression Selection: Comparing Three Predictive Information Criteria - falkcarl/bMLMFit
github.com
May 14, 2026 at 1:04 PM
Motivation: #brms is popular but uses conditional likelihood; #blavaan can’t handle random slopes; which criteria to use given software constraints? Lowest value wins is best?
Repo: github.com/falkcarl/bML...
Has code with lmer-like syntax to estimate models with #stan, marginal WAIC and LOO-CV.
Repo: github.com/falkcarl/bML...
Has code with lmer-like syntax to estimate models with #stan, marginal WAIC and LOO-CV.
Finally, IMPS 2025 will feature a number of preconference workshops. First, Ed Merkle will teach a workshop on Bayesian estimation of latent variable models using Blavaan. psychology.missouri.edu/people/merkle
Ed Merkle | Psychological Sciences - Psychological Sciences
Facebook accountTwitter account
psychology.missouri.edu
February 18, 2025 at 10:31 AM
Finally, IMPS 2025 will feature a number of preconference workshops. First, Ed Merkle will teach a workshop on Bayesian estimation of latent variable models using Blavaan. psychology.missouri.edu/people/merkle
Updates on CRAN: ascribe (0.2.0), baselinr (0.6.0), blavaan (0.6-1), circles (0.1.2), diffcp (0.1.2), NeuroDataSets (0.3.1), socialmixr (0.7.0), varPro (3.2.0), ymd (0.1.7)
August 23, 2026 at 2:24 AM
Updates on CRAN: ascribe (0.2.0), baselinr (0.6.0), blavaan (0.6-1), circles (0.1.2), diffcp (0.1.2), NeuroDataSets (0.3.1), socialmixr (0.7.0), varPro (3.2.0), ymd (0.1.7)
New CRAN package lvmPlot with initial version 0.1.0
#rstats
https://cran.r-project.org/package=lvmPlot
#rstats
https://cran.r-project.org/package=lvmPlot
CRAN: Package lvmPlot
Converts output from latent variable model tools into publication-ready path diagrams and model schematics. 'lavaan' fit objects and parameter tables are supported as a primary workflow, with graph adapters for objects from 'blavaan', 'lavaan.mi', 'semPlot', 'mirt', 'eRm', 'OpenMx', 'psych', 'poLCA', 'mclust', 'flexmix', 'lcmm', 'tidyLPA', and 'MplusAutomation' workflows when those packages are available. Supports structural equation and confirmatory factor analysis diagrams, multilevel structural equation models, growth models, higher-order factor models, latent class and profile models, item response theory models, and common mixture outputs through a unified graph grammar with model-aware defaults, geometry diagnostics, layout quality scoring, automatic layout selection, customizable publication styles, 'RStudio' preview, SVG/PDF/PNG export, 'TikZ' output, and reproducible publication bundles.
cran.r-project.org
June 30, 2026 at 8:02 PM
New CRAN package lvmPlot with initial version 0.1.0
#rstats
https://cran.r-project.org/package=lvmPlot
#rstats
https://cran.r-project.org/package=lvmPlot
New CRAN package rmedsem with initial version 1.0.0
#rstats
https://cran.r-project.org/package=rmedsem
#rstats
https://cran.r-project.org/package=rmedsem
CRAN: Package rmedsem
Conducts mediation analysis for structural equation models (SEM) estimated with 'lavaan', 'blavaan', 'cSEM', or 'modsem'. Implements the Baron and Kenny (1986) <<a href="https://doi.org/10.1037%2F0022-3514.51.6.1173" target="_top">doi:10.1037/0022-3514.51.6.1173</a>> and Zhao, Lynch & Chen (2010) <<a href="https://doi.org/10.1086%2F651257" target="_top">doi:10.1086/651257</a>> approaches to determine the presence and type of mediation. Supports covariance-based SEM, partial least squares SEM, Bayesian SEM, and moderated mediation models. Reports indirect effects with standard errors from Sobel, Delta, Monte-Carlo, and bootstrap methods, along with effect size measures (RIT, RID).
cran.r-project.org
March 16, 2026 at 5:02 PM
New CRAN package rmedsem with initial version 1.0.0
#rstats
https://cran.r-project.org/package=rmedsem
#rstats
https://cran.r-project.org/package=rmedsem