Bayesian Latent Variable Modeling in R with {blavaan} Published 2022-03-11 Download video MP4 360p Recommendations 2:30:30 Mild introduction to Structural Equation Modeling (SEM) using R 1:18:12 Stanford CS330 I Variational Inference and Generative Models l 2022 I Lecture 11 1:40:41 Meta-Analysis in R with {metafor} 11:58 A general framework for thinking about longitudinal models 49:15 {ggstatsplot}: An R Package for {ggplot2}-Based Plots With Statistical Details 2:42:36 Introduction to Structural Equation Modeling 1:31:06 Item Response Theory in R with {mirt} 11:25 A visual guide to Bayesian thinking 05:24 Introduction to LCA with Bethany Bray 1:40:16 Some Bayesian Modeling Techniques in Stan 2:06:43 Latent growth models (LGM) and Measurement Invariance with R in lavaan 17:33 Structural Equation Modeling (SEM) Basics in R 22:17 Jonathan Blow on Deep Work: The Shape of a Problem Doesn't Start Anywhere 38:19 Bayesian Statistics: An Introduction 1:27:46 CppCon 2014: Mike Acton "Data-Oriented Design and C++" 32:35 Meta analysis of dependent effect sizes Robust variance estimation with {clubSandwich} 06:28 Principal Component Analysis (PCA) Similar videos 1:36:53 Advances in Latent Variable Modeling with Bayesian Estimation (Mplus series part 1) 1:02:32 [DeepBayes2019]: Day 1, Lecture 4. Latent variable models and EM-algorithm 1:17:43 Statistical Methods Series: Spatial Occupancy Models 26:20 Item Response Theory - Bayesian Models 19:01 Applied Spatial Data Analysis with R - 10.5 Bayesian Geoadditive Models 37:27 Introduction to latent variables 1:21:06 Statistical Methods Series: Structural Equation Modeling 21:02 StanCon 2020. Developer Talk 4: Charles Margossian. Approximate Bayesian inference for latent GPs 30:41 Psychoco 2021: Paul Bürkner - Bayesian Item Response Modeling in R with brms and Stan 49:57 Latent Variables: Bayesian Mixed Graph Models in Supervised and Unsupervised Learning More results