AMLGM provides efficient computational tools for approximate marginal likelihood estimation and Bayesian inference in Gaussian and latent Gaussian models. Built to complement the R-INLA ecosystem with scalable, reproducible, and research-oriented workflows.
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install.packages("INLA")
library(INLA)
library(amlgm)
result <- amlgm(...)
summary(result)
Designed for modern Bayesian statistical modelling.
Efficient numerical approximations for complex Gaussian models without computationally intensive sampling.
Robust estimation procedures suitable for large datasets and high-dimensional models.
Compatible with the R-INLA ecosystem and existing Bayesian workflows.
Efficient computation for model comparison and Bayesian evidence.
Designed to work naturally with Gaussian latent structures.
Posterior summaries, uncertainty estimates, and predictive evaluation.
Developed for reproducible statistical research.
Explore documentation, tutorials, and examples to begin using AMLGM in your research.