dyadicMarkov 0.1.2
- Clarified that the univariate pattern-identification procedure is an
LRT procedure evaluated using Pearson’s chi-squared statistic, while the
global bivariate nested-model/LRT framework implements two chi-squared
tests for A1 and B1, also evaluated using Pearson’s chi-squared
statistic.
- 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.
dyadicMarkov 0.1.1
- 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.
dyadicMarkov 0.1.0