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Web Data Analysis by Bayesian Mixture of Markov Models

v0.1 · Feb 13, 2023 · MIT + file LICENSE

Description

Designed for web usage data analysis, it implements tools to process web sequences and identify web browsing profiles through sequential classification. Sequences' clusters are identified by using a model-based approach, specifically mixture of discrete time first-order Markov models for categorical web sequences. A Bayesian approach is used to estimate model parameters and identify sequences classification as proposed by Fruehwirth-Schnatter and Pamminger (2010) <doi:10.1214/10-BA606>.

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OK 14 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE Mar 9, 2026

Dependency Network

Dependencies Reverse dependencies DiscreteWeibull mclust MCMCpack clickb

Version History

new 0.1 Mar 9, 2026