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traminer
TraMineR:Trajectory Miner: a Sequence Analysis Toolkit
Set of sequence analysis tools for manipulating, describing and rendering categorical sequences, and more generally mining sequence data in the field of social sciences. Although this sequence analysis package is primarily intended for state or event sequences that describe time use or life courses such as family formation histories or professional careers, its features also apply to many other kinds of categorical sequence data. It accepts many different sequence representations as input and provides tools for converting sequences from one format to another. It offers several functions for describing and rendering sequences, for computing distances between sequences with different metrics (among which optimal matching), original dissimilarity-based analysis tools, and functions for extracting the most frequent event subsequences and identifying the most discriminating ones among them. A user's guide can be found on the TraMineR web page.
Maintained by Gilbert Ritschard. Last updated 3 months ago.
54.3 match 11 stars 8.24 score 534 scripts 13 dependents
maraab23
ggseqplot:Render Sequence Plots using 'ggplot2'
A set of wrapper functions that mainly re-produces most of the sequence plots rendered with TraMineR::seqplot(). Whereas 'TraMineR' uses base R to produce the plots this library draws on 'ggplot2'. The plots are produced on the basis of a sequence object defined with TraMineR::seqdef(). The package automates the reshaping and plotting of sequence data. Resulting plots are of class 'ggplot', i.e. components can be added and tweaked using '+' and regular 'ggplot2' functions.
Maintained by Marcel Raab. Last updated 4 months ago.
ggplot2sequence-analysistraminervisualization
10.9 match 14 stars 5.70 score 18 scripts
helske
seqHMM:Mixture Hidden Markov Models for Social Sequence Data and Other Multivariate, Multichannel Categorical Time Series
Designed for fitting hidden (latent) Markov models and mixture hidden Markov models for social sequence data and other categorical time series. Also some more restricted versions of these type of models are available: Markov models, mixture Markov models, and latent class models. The package supports models for one or multiple subjects with one or multiple parallel sequences (channels). External covariates can be added to explain cluster membership in mixture models. The package provides functions for evaluating and comparing models, as well as functions for visualizing of multichannel sequence data and hidden Markov models. Models are estimated using maximum likelihood via the EM algorithm and/or direct numerical maximization with analytical gradients. All main algorithms are written in C++ with support for parallel computation. Documentation is available via several vignettes in this page, and the paper by Helske and Helske (2019, <doi:10.18637/jss.v088.i03>).
Maintained by Jouni Helske. Last updated 2 years ago.
categorical-dataem-algorithmhidden-markov-modelshmmmixture-markov-modelstime-seriesopenblascppopenmp
2.0 match 97 stars 8.51 score 92 scripts 1 dependentstraminer
TraMineRextras:TraMineR Extension
Collection of ancillary functions and utilities to be used in conjunction with the 'TraMineR' package for sequence data exploration. Includes, among others, specific functions such as state survival plots, position-wise group-typical states, dynamic sequence indicators, and dissimilarities between event sequences. Also includes contributions by non-members of the TraMineR team such as methods for polyadic data and for the comparison of groups of sequences.
Maintained by Gilbert Ritschard. Last updated 7 months ago.
6.8 match 2.43 score 89 scripts 1 dependents