Changes in version 0.1.2 (2026-06-18)                  

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.