dyadicMarkov 0.1.3
- 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
digitsargument 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.
dyadicMarkov 0.1.2
CRAN release: 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
srras a development/documentation dependency.
dyadicMarkov 0.1.1
CRAN release: 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.