GaussianHMM1d
1.1.2Inference, Goodness-of-Fit and Forecast for Univariate Gaussian Hidden Markov Models
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Overview
About
Inference, goodness-of-fit test, and prediction densities and intervals for univariate Gaussian Hidden Markov Models (HMM). The goodness-of-fit is based on a Cramer-von Mises statistic and uses parametric bootstrap to estimate the p-value. The description of the methodology is taken from Chapter 10.2 of Remillard (2013) doi:10.1201/b14285.
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Slowest check: 1.0 min · r-oldrel-windows-x86_64
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- OK2026-03-1014 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
Documentation
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- Examples that run
- 92%
- Documented parameters
- 100%
- Return-value docs
- 100%
- References docs
- 54%
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Dependencies
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2 external dependencies (excludes base and recommended)
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R >= 3.5.0doParallelparallelforeachstats
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Listed in earlier versions (3)
no longer listed · 1.0.1
no longer listed · 1.1.0 to 1.1.2
no longer listed · 1.1.0 to 1.1.2
Package Timeline
4 releases. Pick two to compare their code metrics. R releases are shown for context.
- RR 4.6.0 released · 2026-04-24
- RR 4.5.0 released · 2025-04-11
- 1.1.2Latest
- RR 4.4.0 released · 2024-04-24
- 1.1.12023-07-08 · diff ↗
- 1.1.02023-06-24 · diff ↗
- RR 4.3.0 released · 2023-04-21
- RR 4.2.0 released · 2022-04-22
- RR 4.1.0 released · 2021-05-18
- RR 4.0.0 released · 2020-04-24
- RR 3.6.0 released · 2019-04-26
- 1.0.12019-03-07
- RR 3.5.0 released · 2018-04-23
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Package metadata
- First published
- 2019-03-07
- Total releases
- 4 / 7 yrs
- License
- GPL (>= 2) OSI
- Minimum R
- ≥ 3.5.0
- Download size
- 12 KB
- Installed size
- not tracked yet
- With dependencies
- not tracked yet