<?xml version="1.0" encoding="utf-8" ?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom" xmlns:r="https://r-universe.dev"><channel><title>mrprajwalb.r-universe.dev</title><link>https://mrprajwalb.r-universe.dev</link><description>Recent package updates in mrprajwalb</description><generator>R-universe</generator><image><url>https://github.com/mrprajwalb.png</url><title>R packages by mrprajwalb</title><link>https://mrprajwalb.r-universe.dev</link></image><lastBuildDate>Fri, 18 Jul 2025 15:20:29 GMT</lastBuildDate><item><title>[mrprajwalb] dagHMM 0.1.1</title><author>prajwal.bende@gmail.com (Prajwal Bende)</author><description>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.</description><link>https://github.com/r-universe/mrprajwalb/actions/runs/26657930481</link><pubDate>Fri, 18 Jul 2025 15:20:29 GMT</pubDate><r:package>dagHMM</r:package><r:version>0.1.1</r:version><r:status>success</r:status><r:repository>https://mrprajwalb.r-universe.dev</r:repository><r:upstream>https://github.com/cran/dagHMM</r:upstream></item></channel></rss>