lagdynamics 0.32

New features

• transitions() now reads inference results, not only fitted models. It accepts the output of bootstrap_lsa(), certainty_lsa(), stability_lsa(), permute_lsa(), compare_lsa(), bayes_compare_lsa() and lsa_lags(), and returns the same tidy one-row-per-transition data frame. significant = TRUE keeps the transitions the method itself flags, and sort = "strength" orders them by that method’s signed effect. Previously the per-transition table of an inference result was reachable only through as.data.frame(), because the print methods show a header summary alone.
• The transitions() generic is now transitions(fit, ...), so each class declares only the arguments backed by columns it actually has. Existing calls on a fit are unaffected.

Documentation

• Vignettes restore the user’s options() after changing digits, as CRAN requires.
• Vignettes read every result with transitions() rather than coercing it with as.data.frame(), and select rows with the significant argument rather than subsetting.
• Removed the citations, reference lists, DOIs, documentation URLs and linked author metadata that 0.31 added to the shipped vignettes.

lagdynamics 0.31

CRAN candidate

• Prepared the package for first CRAN submission.
• Moved ggplot2 and cograph from Suggests to Imports so the plotting surface works out of the box; plotting examples now run during checks. The analytical core still depends only on base R.
• Added a dedicated interoperability vignette covering wide data, long event logs, tna, Nestimate, cograph, and lsa_to_tna().
• Added linked author metadata and Dynalytics framework links to all shipped vignettes.
• Restored lsa_to_tna() for handing an lsa fit to tna tooling.
• Added ingestion of Nestimate::build_network() netobjects through their prepared sequence data.
• Stored the bundled engagement data as a data frame so tna::tna(engagement) works directly.
• Removed dead package-site URLs from CRAN-visible metadata.
• Added future CRAN installation instructions to the README.
• Included NEWS.md in the source package.

lagdynamics 0.3.0

Interoperability and documentation

• Added integration tests for cograph, Nestimate, and the Dynalytics evidence surface.
• Added native TNA-style aliases: weights = "tna" and weights = "relative" now map to transition probabilities.
• Updated plotting documentation and vignettes to use weights = "tna" for probability-weighted transition networks.
• Added and reorganised vignettes:
  • intro: conceptual overview and package map.
  • lagdynamics: concise quick start.
  • workflow: complete applied workflow.
  • interop: interoperability with sibling packages.
  • lag-transition-networks: transition-network interpretation.
  • confirmatory: evidence and uncertainty workflow.
  • plotting: plot gallery.
• Removed public documentation references to unexported internals.
• Made long-format input more flexible: action is the only mandatory long-format column, with optional actor, session, time, and order.
• Added warnings for single-sequence bootstrap and permutation cases where the requested procedure has limited inferential meaning.

lagdynamics 0.2.0

Confirmatory workflow and group comparison

• Added the Dynalytics-style confirmatory evidence battery: certainty_lsa(), bootstrap_lsa(), reliability_lsa(), stability_lsa(), and permute_lsa().
• Added group comparison with compare_lsa() and Bayesian group comparison with bayes_compare_lsa().
• Added grouped lsa() fits through group = ..., with grouped methods for transitions(), nodes(), tests(), initial(), plotting, reliability, and comparison workflows.
• Added tidy as.data.frame() methods for inference and comparison result objects.
• Added the unified plotting surface: residual heatmaps, residual networks, TNA probability networks, chord diagrams, sunbursts, uncertainty forests, and group-comparison plots.
• Added native transition and initial probabilities: transition_probabilities() and initial().
• Added bundled long-format data for examples and tests.

lagdynamics 0.1.0

Initial implementation

• Created a from-scratch, clean-room implementation of lag sequential analysis for categorical event sequences.
• Added the unified lsa() constructor and canonical sequence handling through lsa_data() and lsa_transitions().
• Added five built-in engines: classical, two_cell, bidirectional, parallel_dominance, and nonparallel_dominance.
• Added convenience wrappers: lsa_classical(), lsa_two_cell(), lsa_bidirectional(), lsa_parallel_dominance(), and lsa_nonparallel_dominance().
• Added the pluggable engine registry: register_lsa_engine(), get_lsa_engine(), list_lsa_engines(), and unregister_lsa_engine().
• Added tidy reading verbs: transitions(), nodes(), tests(), initial(), and summary().
• Added multi-lag helpers with lsa_lags() and lag_profile().
• Added structural-zero handling through loops = FALSE and arbitrary structural-zero matrices.
• Added experimental transfer_entropy() for directed categorical information-flow analysis.
• Kept runtime dependencies minimal: only base R packages are imported (grDevices, grid, stats, and utils).