clickb
Web Data Analysis by Bayesian Mixture of Markov Models
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.
- Version0.1
- R versionunknown
- LicenseMIT
- LicenseLICENSE
- Needs compilation?No
- Last release02/13/2023
Team
Furio Urso
Reza Mohammadi
Show author detailsRolesAuthorAntonino Abbruzzo
Show author detailsRolesAuthorMaria Francesca Cracolici
Show author detailsRolesAuthor
Insights
Last 30 days
This package has been downloaded 241 times in the last 30 days. Enough downloads to make a small wave in the niche community. The curiosity is spreading! The following heatmap shows the distribution of downloads per day. Yesterday, it was downloaded 7 times.
The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.
Last 365 days
This package has been downloaded 2,652 times in the last 365 days. Now we’re talking! This work is officially 'heard of in academic circles', just like those wild research papers on synthetic bananas. The day with the most downloads was Aug 22, 2024 with 30 downloads.
The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.
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Dependencies
- Imports3 packages
- Suggests1 package