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. Other features such as functions for pattern mining and artificial sequence generation are described as well. This article also introduces original visualization tools for both the model and the outcomes of sequence prediction. This feature supports data mining applications such as the extraction of typical patterns and outliers. The PST can then be used for sequence prediction, i.e., to assign a probability to whole observed or artificial sequences. This article describes how a VLMC model is learned from one or more categorical sequences and stored in a PST. 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. VLMCs allow to model high-order dependencies in categorical sequences with parsimonious models based on simple estimation procedures.
It currently supports building virtual machines on hosts running VMware Fusion for OS X, VMware Workstation for Linux and Windows, and VMware Player on Linux. 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). This VMware Packer builder is able to create VMware virtual machines from an ISO file as a source.
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