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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
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.
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