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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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r-devel-linux-x86_64-debian-clang	OK	28.1s
r-devel-linux-x86_64-debian-gcc	OK	23s
r-devel-linux-x86_64-fedora-clang	OK	42.7s
r-devel-linux-x86_64-fedora-gcc	OK	40.8s
r-devel-macos-arm64	OK	11s
r-devel-windows-x86_64	OK	49s
r-oldrel-macos-arm64	OK
r-oldrel-macos-x86_64	OK	24s
r-oldrel-windows-x86_64	OK	58s
r-patched-linux-x86_64	OK	25.7s
r-release-linux-x86_64	OK	25s
r-release-macos-arm64	OK
r-release-macos-x86_64	OK	33s
r-release-windows-x86_64	OK	47s

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OK 14 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE Mar 10, 2026

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new 0.1 Mar 10, 2026