transitiontrees 0.1.2

Initial CRAN release.

Fitting

• 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.
• Five smoothing schemes via the unified 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.
• Grouped fits: 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().

Pathway-centric API

• 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.

Prediction, scoring, and imputation

• 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.

Resampling and group comparison

• 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.

Visualisation

• 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.

Bundled data

• trajectories, group_regulation_long, ai_long, and engagement for examples and tests.

Validation

• Equivalence-tested at machine precision against an independent external reference implementation of the model — counts exact, probabilities within 1.11e-16 — and cross-checked against an independent first-order Markov reference and a standard long-to-sequence reshaper. The equivalence suite lives outside the package and is run locally.