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htna 0.3.1

Fitted MMM conversion

• build_htna() now explicitly consumes the fitted net_mmm object returned by Nestimate::cluster_mmm(). It materializes one HTNA network per fitted sequence cluster without rerunning MMM and preserves assignments, posterior probabilities, mixing proportions, information criteria, fitted weights, and the original actor partition.
• Nestimate::as_htna() now supports the same fitted net_mmm input and returns an equivalent htna_group. Nestimate::cluster_network() remains the function that directly returns materialized cluster networks.

Datasets

• New per-actor long frames human_simplified and ai_simplified, ready for the named-list form of build_htna().
• New tidy code → actor-type lookup human_ai_codebook, ready to pass as node_groups to build_htna().
• human_ai gains a phase factor column tagging each session as "Early" or "Late" (chronological split), so grouped examples can call build_htna(human_ai, group = "phase") without pre-processing.

Documentation

• htna_palette is now exported (previously an internal constant referenced as a default argument in exported plot functions).
• New article vignette Clustering inputs to build_htna() covering Nestimate::build_clusters() and Nestimate::cluster_mmm(), model selection with cluster_choice() / compare_mmm() / cluster_diagnostics(), and cohort-pair comparison with compare_htna().

htna 0.3.0

Actor-aware interoperability and comparison

• Added compare_htna(), an actor-aware wrapper over Nestimate::compare_model(). It aligns partial node coverage, annotates every edge with source and target actors, reports summaries for each actor-pair block, retains the original HTNA networks, and compares every cohort pair when passed an htna_group.
• Added as.igraph() methods for htna and htna_group. Converted graphs preserve actor types as vertex attributes and canonical actor ordering as graph metadata; grouped inputs convert every cohort.
• summary.htna() and summary.htna_group() now include Nestimate’s standard network-level descriptive metrics alongside the existing actor-partition and actor-to-actor edge summaries.
• build_htna(method = "co_occurrence") is now documented and covered across all constructor forms, grouped networks, MCML expansion, Nestimate distance clustering, and legacy TNA clustering, with numerical equivalence tests against both Nestimate and tna::ctna().

htna 0.2.2

MCML expansion

• build_htna() now accepts a fitted Nestimate::build_mcml() object. The MCML node clusters become actor groups, and one full node-level HTNA is rebuilt from the retained or explicitly supplied original source. This is equivalent to Nestimate::as_htna(): it intentionally preserves global cross-cluster transitions rather than stitching together fitted macro or within-cluster weights.
• HTNA partition injection now consistently stores character labels in $node_groups$group and a factor with the full actor level set in $nodes$groups.

htna 0.2.1

Verification

• Added extensive randomized equivalence coverage for constructing HTNA models from Nestimate distance clustering, fitted mixed-Markov clustering, and legacy tna clustering results, including actor-partition preservation and no-refit guarantees.

htna 0.2.0

New features

• build_htna() now consumes Nestimate distance clustering (net_clustering), fitted MMM clustering (cluster_mmm() / net_mmm), materialized cluster networks (netobject_group), and legacy tna::cluster_sequences() results. It returns an htna_group, restores the actor partition on every child, and preserves clustering assignments, posterior probabilities, diagnostics, and fitted MMM weights. A partition preserved upstream is used automatically; older or independently-created clustering results can supply node_groups.

Six htna-named functions are added, extending htna’s coverage of the major transition-network-analysis (tna) verbs. All are thin ...-forwarding wrappers over the ‘Nestimate’ engine and follow the same durable design as the existing re-exports (stable documented formals; new engine arguments flow through ...).

• Pruning family — prune_htna(), deprune_htna(), reprune_htna(), and pruning_details_htna() (over Nestimate::net_prune() and friends). The actor partition and htna class are preserved across pruning, so the pruned network stays htna-aware and plots with [plot_htna()].
• bayes_compare_htna() — Bayesian complement of permutation_htna() (over Nestimate::bayes_compare()). Preserves the actor partition on $x/$y and carries class net_permutation, so it renders with plot_htna_diff().
• certainty_htna() — closed-form counterpart of bootstrap_htna() (over Nestimate::certainty()), joining the confirmatory-analysis family alongside reliability_htna() and casedrop_reliability_htna().

htna 0.1.2

• The six re-exported Nestimate functions (permutation_htna(), association_rules_htna(), casedrop_reliability_htna(), markov_order_test_htna(), state_distribution_htna(), state_frequencies_htna()) are now thin ...-forwarding wrappers instead of direct aliases. Their documented formals are fixed, so a future change to any of these functions’ signatures in Nestimate can no longer produce a code/documentation-mismatch (codoc) WARNING in R CMD check. No behavioural change: each call forwards unchanged to Nestimate (including S3 dispatch for state_distribution_htna()), and extra arguments pass straight through ....
• sequence_compare_htna() no longer restates Nestimate’s sequence_compare() arguments (sub, min_freq, test, iter, adjust) as its own formals; they now pass through .... Its signature is x, group, level, ... — the htna-specific level argument is retained. Same defaults and behaviour, but new Nestimate arguments flow through automatically and the code/documentation coupling is removed. mosaic_plot_htna()’s documentation is likewise decoupled from Nestimate’s signature (it was already a ... wrapper).

htna 0.1.1

• Compatibility with Nestimate 0.8.0. Nestimate::permutation() gained a measures argument (for centrality permutation tests); since permutation_htna() re-exports that function, its documentation is regenerated to include the new argument, resolving a code/documentation mismatch flagged by R CMD check. No behavioural change.
• Imports: Nestimate (>= 0.8.0).

htna 0.1.0

• Initial release.
• Builds heterogeneous transition networks for sequences mixing two or more actor groups (e.g. Human and AI) within the same interaction, via build_htna().
• Re-exports the most commonly used Nestimate and cograph downstream functions under _htna-suffixed aliases so callers do not need to attach Nestimate or cograph explicitly: permutation_htna(), casedrop_reliability_htna(), markov_order_test_htna(), mosaic_plot_htna(), state_distribution_htna(), state_frequencies_htna(), association_rules_htna(), sequence_compare_htna().
• htna-side wrappers add ergonomics over the upstream API: frequencies_htna(), plot_frequencies_htna() (treemap / bars / facet views), centralities_htna(), plot_centralities(), plot_htna(), plot_htna_bootstrap(), plot_htna_diff(), sequence_plot_htna(), reliability_htna(), centrality_stability_htna(), edge_betweenness_htna(), extract_meta_paths() (state-level by default; level = "type" for the type-level meta-path rollup; supports schemas mixing types, concrete codes, and *).

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.