Initial CRAN release.
context_tree() fits a variable-depth pathway tree (prediction
suffix tree; Ron, Singer & Tishby 1996) from a wide character
matrix / data.frame, a list of character vectors, a long event log
(actor / time / action / order / session arguments), or a
transition/network object. NA, "", and the TraMineR codes %
(void) and * (missing) are treated as gaps in wide / list input.prepare_input() reshapes a long event log to a wide sequence frame
(timestamp / session logic), and can carry per-sequence metadata
through the reshape via meta.smoothing argument
("floor", "laplace", "kneser_ney", "witten_bell",
"jelinek_mercer"); hyperparameters as list(method, ...).prune_tree() supports four criteria: likelihood-ratio G2,
Kullback-Leibler, AIC, BIC.smooth_tree() re-smooths a fitted tree; model_fit() /
n_nodes() are tidy fit-summary accessors.context_tree(..., group =) (a per-sequence vector or
a column name) fits one tree per group over a shared alphabet and
returns a transitiontrees_group. block = carries a stratifying id
(e.g. subject) for compare_groups().tree_pathways(), common_pathways(), divergent_pathways(),
sharp_pathways() rank pathways by frequency, divergence from the
suffix-parent, or modal-flip status.tree_dependence() is the per-context entropy/divergence diagnostic
table; query_pathway(), pathway_exists(), subtree() provide
tree introspection.predict() / simulate() / generate_sequences() for next-state
prediction and sampling.logLik(), nobs(), AIC(), BIC(), perplexity(),
score_sequences(), score_positions() form the predictive-
evaluation toolchain.impute_sequences() fills internal gaps in incomplete sequences.mine_contexts() / mine_sequences() scan for contexts where a
state is unusually likely or unlikely and for the best/worst-fit held-out
sequences.tune_tree() k-fold cross-validates max_depth, min_count,
smoothing, and pruning.bootstrap_pathways() reports per-pathway stability and
informativeness with bootstrap CIs.compare_trees() runs a permutation test for two-tree divergence.compare_groups() compares a transitiontrees_group on two axes ---
behavioral (Jensen-Shannon divergence of next-state distributions)
and usage (prevalence) --- with a permutation null (optionally
stratified by block for repeated-measures designs), Benjamini-
Hochberg FDR, and a between-group distance matrix.tree_distance() computes count-weighted symmetric KL between two
trees.plot() on a transitiontrees offers four styles: "horizontal"
(default), "dendrogram", and "icicle" (all pure ggplot2), plus
"interactive" (visNetwork). plot() on a transitiontrees_group
draws one figure per group.plot_pathways(), plot_divergence(), plot_distributions(),
plot_predictive(), plot_pathway_resamples(), and the
bootstrap / comparison / tuning plot methods.plot_difference() renders the early-vs-late style difference
between two groups as a per-context map (Pearson residuals against
the no-difference null, or raw probability difference) or on the
context-tree layout.trajectories, group_regulation_long, ai_long, and
engagement for examples and tests.