<?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>mgamiz.r-universe.dev</title><link>https://mgamiz.r-universe.dev</link><description>Recent package updates in mgamiz</description><generator>R-universe</generator><image><url>https://github.com/mgamiz.png</url><title>R packages by mgamiz</title><link>https://mgamiz.r-universe.dev</link></image><lastBuildDate>Tue, 19 Nov 2024 14:20:06 GMT</lastBuildDate><item><title>[mgamiz] HMMRel 0.1.1</title><author>mgamiz@ugr.es (M.L. Gamiz)</author><description>Reliability Analysis and Maintenance Optimization using
Hidden Markov Models (HMM). The use of HMMs to model the state
of a system which is not directly observable and instead
certain indicators (signals) of the true situation are provided
via a control system. A hidden model can provide key
information about the system dependability, such as the
reliability of the system and related measures. An estimation
procedure is implemented based on the Baum-Welch algorithm.
Classical structures such as K-out-of-N systems and Shock
models are illustrated. Finally, the maintenance of the system
is considered in the HMM context and two functions for new
preventive maintenance strategies are considered. Maintenance
efficiency is measured in terms of expected cost. Methods are
described in Gamiz, Limnios, and Segovia-Garcia (2023)
&lt;doi:10.1016/j.ejor.2022.05.006&gt;.</description><link>https://github.com/r-universe/mgamiz/actions/runs/28013333093</link><pubDate>Tue, 19 Nov 2024 14:20:06 GMT</pubDate><r:package>HMMRel</r:package><r:version>0.1.1</r:version><r:status>success</r:status><r:repository>https://mgamiz.r-universe.dev</r:repository><r:upstream>https://github.com/cran/HMMRel</r:upstream></item></channel></rss>