dagHMM: Directed Acyclic Graph HMM with TAN Structured Emissions

Hidden Markov models (HMMs) are a formal foundation for making probabilistic models of linear sequence. They provide a conceptual toolkit for building complex models just by drawing an intuitive picture. They are at the heart of a diverse range of programs, including genefinding, profile searches, multiple sequence alignment and regulatory site identification. HMMs are the Legos of computational sequence analysis. In graph theory, a tree is an undirected graph in which any two vertices are connected by exactly one path, or equivalently a connected acyclic undirected graph. Tree represents the nodes connected by edges. It is a non-linear data structure. A poly-tree is simply a directed acyclic graph whose underlying undirected graph is a tree. The model proposed in this package is the same as an HMM but where the states are linked via a polytree structure rather than a simple path.

Version: 0.1.0
Imports: gtools, future, matrixStats, PRROC, bnlearn, bnclassify
Published: 2023-01-10
Author: Prajwal Bende [aut, cre], Russ Greiner [ths], Pouria Ramazi [ths]
Maintainer: Prajwal Bende <prajwal.bende at gmail.com>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2.0.0)]
NeedsCompilation: no
CRAN checks: dagHMM results


Reference manual: dagHMM.pdf


Package source: dagHMM_0.1.0.tar.gz
Windows binaries: r-devel: dagHMM_0.1.0.zip, r-release: dagHMM_0.1.0.zip, r-oldrel: dagHMM_0.1.0.zip
macOS binaries: r-release (arm64): dagHMM_0.1.0.tgz, r-oldrel (arm64): dagHMM_0.1.0.tgz, r-release (x86_64): dagHMM_0.1.0.tgz, r-oldrel (x86_64): dagHMM_0.1.0.tgz


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