an R package for structural equation modeling and more

🎓 Tidy tools for academics

An R package for Bayesian structural equation modeling

Causing: CAUsal INterpretation using Graphs

REGAIN (Regularised Graphical Inference)

An Introduction to Structural Equation Modeling

Data Analysis on Mental Health.

Tensor Extraction of Latent Features (T-ELF). Within T-ELF's arsenal are non-negative matrix and tensor factorization solutions, equipped with automatic model determination (also known as the estimation of latent factors - rank) for accurate data modeling. Our software suite encompasses cutting-edge data pre-processing and post-processing modules.

This is the source code for HDNO: a hierarchical model for task-oriented dialogue system.

AI that generates human faces which have never been seen before. The future is now 😁

PyTorch implementation of "Variational Autoencoders with Jointly Optimized Latent Dependency Structure" [ICLR 2019]

Ωnyx - Structural Equation Modeling

An R package for the Latent Environmental & Genetic InTeraction (LEGIT) model

Match Predictions for Professional League of Legends Matches

High-Performance Implementation of Spectral Learning of Latent-Variable PCFGs (Cohen et al., 2013)

Jumping across biomedical contexts using compressive data fusion

[Pytorch] Minimal implementation of a Variational Autoencoder (VAE) with Categorical Latent variables inspired from "Categorical Reparameterization with Gumbel-Softmax".

A Mutually-Dependent Hadamard Kernel for Modelling Latent Variable Couplings (ACML 2017)

Coordinate ascent mean-field variational inference (CAVI) using the evidence lower bound (ELBO) to iteratively perform the optimal variational factor distribution parameter updates for clustering.

This R tutorial automates the 3-step ML auxiliary variable procedure using the MplusAutomation package (Hallquist & Wiley, 2018) to estimate models and extract relevant parameters. To learn more about auxiliary variable integration methods and why multi-step methods are necessary for producing un-biased estimates see Asparouhov & Muthén (2014).