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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>.
| Version: | 0.1 |
| Imports: | DiscreteWeibull, mclust, MCMCpack, parallel |
| Suggests: | seqHMM |
| Published: | 2023-02-13 |
| Author: | Furio Urso [aut, cre], Reza Mohammadi [aut], Antonino Abbruzzo [aut], Maria Francesca Cracolici [aut] |
| Maintainer: | Furio Urso <furio.urso at unipa.it> |
| License: | MIT + file LICENSE |
| NeedsCompilation: | no |
| CRAN checks: | clickb results |
| Reference manual: | clickb.pdf |
| Package source: | clickb_0.1.tar.gz |
| Windows binaries: | r-devel: clickb_0.1.zip, r-release: clickb_0.1.zip, r-oldrel: clickb_0.1.zip |
| macOS binaries: | r-release (arm64): clickb_0.1.tgz, r-oldrel (arm64): clickb_0.1.tgz, r-release (x86_64): clickb_0.1.tgz, r-oldrel (x86_64): clickb_0.1.tgz |
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