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PST (version 0.84.1)

Probabilistic Suffix Trees and Variable Length Markov Chains

Description

This package provides a framework for analysing state sequences with probabilistic suffix trees (PST), the construction that stores variable length Markov chains (VLMC). Besides functions for learning and optimizing VLMC models, the PST library includes many additional tools to analyse sequence data with these models: visualization tools, functions for sequence prediction and artificial sequences generation, as well as for context and pattern mining. The package is specifically adapted to the field of social sciences by allowing to learn VLMC models from sets of individual sequences possibly containing missing values, and by accounting for case weights. The library also allows to compute probabilistic divergence between two models, and to fit segmented VLMC, where sub-models fitted to distinct strata of the learning sample are stored in a single PST. This software results from research work executed within the framework of the Swiss National Centre of Competence in Research LIVES, which is financed by the Swiss National Science Foundation. The authors are grateful to the Swiss National Science Foundation for its financial support.

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Version

Install

install.packages('PST')

Monthly Downloads

233

Version

0.84.1

License

GPL (>= 2)

Maintainer

Alexis Gabadinho

Last Published

December 20th, 2013

Functions in PST (0.84.1)

summary-methods

Summary of variable length Markov chain model
tune

AIC, AICc or BIC based model selection
PSTf-class

Flat representation of a probabilistic suffix tree
cmine

Mining contexts
logLik

Log-Likelihood of a variable length Markov chain model
pdist

Compute probabilistic divergence between two PST
print

Print method for objects of class PSTf and PSTr
subtree

Extract a subtree from a segmented PST
nobs

Extract the number of observations to which a VLMC model is fitted
pmine

PST based pattern mining
query

Retrieve counts or next symbol probability distribution
ppplot

Plotting a branch of a probabilistic suffix tree
nodenames

Retrieve the node labels of a PST
PSTr-class

Nested representation of a probabilistic suffix tree
pstree

Build a probabilistic suffix tree
impute

Impute missing values using a probabilistic suffix tree
generate

Generate sequences using a probabilistic suffix tree
predict

Compute the probability of categorical sequences using a probabilistic suffix tree
prune

Prune a probabilistic suffix tree
SRH

Longitudinal data on self rated health
cprob

Empirical conditional probability distributions of order L
pqplot

Prediction quality plot
plot-PSTr

Plot a PST
s1

Example sequence data set
cplot

Plot single nodes of a probabilistic suffix tree