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.