Combining tree-boosting with Gaussian process and mixed effects models
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Updated
Nov 21, 2024 - C++
Combining tree-boosting with Gaussian process and mixed effects models
A Julia package for fitting (statistical) mixed-effects models
A meta-analysis package for R
Generic curve fitting package with nonlinear mixed effects model
Tools for multiple imputation in multilevel modeling
CRAN Task View: Mixed, Multilevel, and Hierarchical Models in R
R package providing utilities for INLA
Julia package for fitting mixed-effects models with flexible random/repeated covariance structure.
Linear Models With R and Extending the Linear Model with R: Generalized Linear, Mixed Effects and Nonparametric Regression Models - by J Faraway
RCall support for MixedModels.jl and lme4
scikit-learn wrapper for generalized linear mixed model methods in R
Bayesian network analysis in R
Curated list of the sources about multilevel models
Featured Nonlinear Mixed effects Models
A Nonlinear Modeling Library (ANML)
Approximate Bayesian inference for mixed effects models with heterogeneity
R Package for fitting latent multivariate mixed effects location scale models.
R package for Bayesian measurement invariance assessment using mixed effects and shrinkage.
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