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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.These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.