Analyzing State Sequences with Probabilistic Suffix Trees: The PST R Package
This article presents the PST R package for categorical sequence analysis with probabilistic suffix trees (PSTs), i.e., structures that store variable-length Markov chains (VLMCs). VLMCs allow to model high-order dependencies in categorical sequences with parsimonious models based on simple estimati...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Foundation for Open Access Statistics
2016-08-01
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| Series: | Journal of Statistical Software |
| Subjects: | |
| Online Access: | https://www.jstatsoft.org/index.php/jss/article/view/2801 |
| _version_ | 1828976585224486912 |
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| author | Alexis Gabadinho Gilbert Ritschard |
| author_facet | Alexis Gabadinho Gilbert Ritschard |
| author_sort | Alexis Gabadinho |
| collection | DOAJ |
| description | This article presents the PST R package for categorical sequence analysis with probabilistic suffix trees (PSTs), i.e., structures that store variable-length Markov chains (VLMCs). VLMCs allow to model high-order dependencies in categorical sequences with parsimonious models based on simple estimation procedures. The package is specifically adapted to the field of social sciences, as it allows for VLMC models to be learned from sets of individual sequences possibly containing missing values; in addition, the package is extended to account for case weights. This article describes how a VLMC model is learned from one or more categorical sequences and stored in a PST. The PST can then be used for sequence prediction, i.e., to assign a probability to whole observed or artificial sequences. This feature supports data mining applications such as the extraction of typical patterns and outliers. This article also introduces original visualization tools for both the model and the outcomes of sequence prediction. Other features such as functions for pattern mining and artificial sequence generation are described as well. The PST package also allows for the computation of probabilistic divergence between two models and the fitting of segmented VLMCs, where sub-models fitted to distinct strata of the learning sample are stored in a single PST. |
| first_indexed | 2024-12-14T14:41:23Z |
| format | Article |
| id | doaj.art-10791a9b056d40df849a14834b060d1f |
| institution | Directory Open Access Journal |
| issn | 1548-7660 |
| language | English |
| last_indexed | 2024-12-14T14:41:23Z |
| publishDate | 2016-08-01 |
| publisher | Foundation for Open Access Statistics |
| record_format | Article |
| series | Journal of Statistical Software |
| spelling | doaj.art-10791a9b056d40df849a14834b060d1f2022-12-21T22:57:24ZengFoundation for Open Access StatisticsJournal of Statistical Software1548-76602016-08-0172113910.18637/jss.v072.i031030Analyzing State Sequences with Probabilistic Suffix Trees: The PST R PackageAlexis GabadinhoGilbert RitschardThis article presents the PST R package for categorical sequence analysis with probabilistic suffix trees (PSTs), i.e., structures that store variable-length Markov chains (VLMCs). VLMCs allow to model high-order dependencies in categorical sequences with parsimonious models based on simple estimation procedures. The package is specifically adapted to the field of social sciences, as it allows for VLMC models to be learned from sets of individual sequences possibly containing missing values; in addition, the package is extended to account for case weights. This article describes how a VLMC model is learned from one or more categorical sequences and stored in a PST. The PST can then be used for sequence prediction, i.e., to assign a probability to whole observed or artificial sequences. This feature supports data mining applications such as the extraction of typical patterns and outliers. This article also introduces original visualization tools for both the model and the outcomes of sequence prediction. Other features such as functions for pattern mining and artificial sequence generation are described as well. The PST package also allows for the computation of probabilistic divergence between two models and the fitting of segmented VLMCs, where sub-models fitted to distinct strata of the learning sample are stored in a single PST.https://www.jstatsoft.org/index.php/jss/article/view/2801state sequencescategorical sequencessequence visualizationsequence data miningvariable-length Markov chainsprobabilistic suffix treesR |
| spellingShingle | Alexis Gabadinho Gilbert Ritschard Analyzing State Sequences with Probabilistic Suffix Trees: The PST R Package Journal of Statistical Software state sequences categorical sequences sequence visualization sequence data mining variable-length Markov chains probabilistic suffix trees R |
| title | Analyzing State Sequences with Probabilistic Suffix Trees: The PST R Package |
| title_full | Analyzing State Sequences with Probabilistic Suffix Trees: The PST R Package |
| title_fullStr | Analyzing State Sequences with Probabilistic Suffix Trees: The PST R Package |
| title_full_unstemmed | Analyzing State Sequences with Probabilistic Suffix Trees: The PST R Package |
| title_short | Analyzing State Sequences with Probabilistic Suffix Trees: The PST R Package |
| title_sort | analyzing state sequences with probabilistic suffix trees the pst r package |
| topic | state sequences categorical sequences sequence visualization sequence data mining variable-length Markov chains probabilistic suffix trees R |
| url | https://www.jstatsoft.org/index.php/jss/article/view/2801 |
| work_keys_str_mv | AT alexisgabadinho analyzingstatesequenceswithprobabilisticsuffixtreesthepstrpackage AT gilbertritschard analyzingstatesequenceswithprobabilisticsuffixtreesthepstrpackage |