ngme2
0.9.8Linear Latent Non-Gaussian Models with Flexible Distributions
Overview
Fits and analyzes linear latent non-Gaussian models for temporal, spatial, and space-time data. The package provides model components for autoregressive and Ornstein-Uhlenbeck processes, random walks, Matern fields based on stochastic partial differential equations, separable and non-separable space-time models, graph-based Matern models, bivariate type-G fields, and user-defined sparse operators. Latent fields and observation models can use Gaussian and non-Gaussian noise distributions, including normal inverse Gaussian, generalized asymmetric Laplace, and skew-t distributions. Functions are included for simulation, likelihood-based estimation, prediction, cross-validation, convergence diagnostics, stochastic gradient optimization, batch-means confidence intervals, and posterior-like sampling. The modeling framework is described in Bolin, Jin, Simas and Wallin (2026) "A Unified and Computationally Efficient Non-Gaussian Statistical Modeling Framework" doi:10.48550/arXiv.2602.23987.
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Health
- ERROR r-devel-linux-x86_64-fedora-clang
- ERROR r-devel-linux-x86_64-fedora-gcc
- ERROR2026-04-293 OK · 0 NOTE · 0 WARNING · 2 ERROR · 0 FAILURE
Documentation
- Examples that run
- 100%
- Documented parameters
- 94%
- Return-value docs
- 100%
- References docs
- 6%
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- 0.9.8Latest
- 0.9.72026-04-28
- RR 4.6.0 released · 2026-04-24
Package metadata
- First published
- 2026-04-28
- Total releases
- 2 / 1 yrs
- License
- GPL (>= 3) OSI
- Minimum R
- ≥ 3.5.0
- Bundled data
- 52 KB / 4 files
- Download size
- not tracked yet
- Installed size
- not tracked yet
- With dependencies
- not tracked yet