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.
|Author||Alexis Gabadinho [aut, cre, cph]|
|Date of publication||2016-11-10 17:55:35|
|Maintainer||Alexis Gabadinho <firstname.lastname@example.org>|
|License||GPL (>= 2)|
cmine: Mining contexts
cplot: Plot single nodes of a probabilistic suffix tree
cprob: Empirical conditional probability distributions of order 'L'
generate: Generate sequences using a probabilistic suffix tree
impute: Impute missing values using a probabilistic suffix tree
logLik: Log-Likelihood of a variable length Markov chain model
nobs: Extract the number of observations to which a VLMC model is...
nodenames: Retrieve the node labels of a PST
pdist: Compute probabilistic divergence between two PST
plot-PSTr: Plot a PST
pmine: PST based pattern mining
ppplot: Plotting a branch of a probabilistic suffix tree
pqplot: Prediction quality plot
predict: Compute the probability of categorical sequences using a...
print: Print method for objects of class 'PSTf' and 'PSTr'
prune: Prune a probabilistic suffix tree
PSTf-class: Flat representation of a probabilistic suffix tree
PSTr-class: Nested representation of a probabilistic suffix tree
pstree: Build a probabilistic suffix tree
query: Retrieve counts or next symbol probability distribution
s1-data: Example sequence data set
SRH-data: Longitudinal data on self rated health
subtree: Extract a subtree from a segmented PST
summary: Summary of variable length Markov chain model
tune: AIC, AICc or BIC based model selection