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clickb

Web Data Analysis by Bayesian Mixture of Markov Models

Installation

About

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.

Key Metrics

Version 0.1
Published 2023-02-13 441 days ago
Needs compilation? no
License MIT
License File
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Maintainer

Maintainer

Furio Urso

furio.urso@unipa.it

Authors

Furio Urso

aut / cre

Reza Mohammadi

aut

Antonino Abbruzzo

aut

Maria Francesca Cracolici

aut

Material

Reference manual
Package source

macOS

r-release

arm64

r-oldrel

arm64

r-release

x86_64

r-oldrel

x86_64

Windows

r-develnot available

x86_64

r-releasenot available

x86_64

r-oldrelnot available

x86_64

Imports

DiscreteWeibull
mclust
MCMCpack
parallel

Suggests

seqHMM