Changes in version 0.1.3.9000                      

  - Refined argument documentation for rOpenSci review, including
    explicit state-space constraints, separate documentation of the four
    bivariate sequence inputs, and clarification that bivariate
    empirical count matrices may use integer or double storage modes.

                 Changes in version 0.1.3 (2026-09-15)                  

  - Added a sensitivity-analysis vignette reproducing the published
    sequence-length analysis for 1,000 simulated dyads at five sequence
    lengths, with shorter worked examples for 30- and 90-point
    sequences.
  - Added the fixed simulation datasets used in the sensitivity analysis
    and documented their structure, binary state space, historical
    labels, and separate simulation at each sequence length.
  - Clarified the terminology used in the sensitivity analysis and noted
    the small numerical differences from the published sensitivity and
    specificity results.
  - Documented how exact AIC ties are handled in partial and complete
    bivariate pattern selection.
  - Improved validation of the digits argument used by print, summary,
    and LaTeX methods.
  - Added a check for state-space sizes that are too large to construct
    the required transition-count matrices safely.
  - Updated the statistical-software-review annotations to reflect the
    legacy implementation parity tests.
  - Added and updated tests for the changes above.
  - Added base-R state-strip plot() methods for univariate pattern
    results and binary bivariate case and pattern results, with
    accessible defaults and retained sequence metadata.
  - Added inferential summary tables and LaTeX representations for
    empirical-count and MLE matrices.

                 Changes in version 0.1.2 (2026-08-21)                  

  - Clarified that the univariate pattern-identification procedure is an
    LRT procedure evaluated using Pearson Chi-squared, while the global
    bivariate nested-model/LRT framework implements two chi-squared
    tests for A1 and B1, also evaluated using Pearson Chi-squared.
  - Clarified that local bivariate pattern selection computes the
    G-squared deviance before applying AIC = G^2 + 2k.
  - Corrected the univariate pattern-identification and global bivariate
    case boundaries so that p-values equal to alpha are treated as
    rejection (p <= alpha).
  - Documented that the univariate workflow supports multiple
    categorical states, while the bivariate workflow is defined for two
    dichotomous variables.
  - Added focused tests that distinguish Pearson's chi-squared statistic
    from G-squared and verify both partial and complete bivariate AIC
    paths.
  - Updated the maintainer email address and package version for this
    release.
  - Made the manual simulated-parity script stop when a comparison
    fails.
  - Declared srr as a development/documentation dependency.

                 Changes in version 0.1.1 (2026-06-21)                  

  - Updated package wording and metadata for the CRAN submission.
  - Added S3 classes and print/summary support for pattern and case
    identification results.
  - Added S3 classes for empirical count matrices and MLE transition
    probability matrices while preserving ordinary matrix behavior.
  - Added two synthetic 90-point example datasets for package workflow
    examples.
  - Rewrote the workflow vignette around the built-in univariate and
    bivariate example datasets.
  - Improved internal input validation for count, estimation, and
    pattern-identification functions.
  - Updated tests and documentation for the new S3 return objects.
  - Improved validation for extreme state-space inputs, non-finite chain
    values, and malformed empirical matrices.
  - Refactored selected internal validation and AIC helper code to
    reduce function complexity while preserving exported behavior.
  - Improved bivariate count validation coverage for unsupported and
    malformed inputs.

                 Changes in version 0.1.0 (2026-03-16)                  

  - Initial CRAN submission.