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algebraic.mle 1.2.0
New features
-
joint() composes independent MLEs with disjoint parameter sets into a joint MLE with block-diagonal covariance structure
-
combine() optimally weights independent MLEs for the same parameter via inverse-variance (Fisher information) weighting
-
as_dist() converts MLE objects to their asymptotic normal distributions, bridging to the algebraic.dist distribution algebra
- Distribution methods on MLE objects:
density(), cdf(), inv_cdf(), sup(), dim(), mean(), conditional()
Documentation
- Rewrote package Description around “MLE as technology” narrative
- Rewrote README to lead with the algebra (joint, combine, rmap, as_dist)
- Added “The Algebra of MLEs” vignette demonstrating the full composition pipeline
Bug fixes
- Fixed name-propagation issue in
rmap() where c() merged parameter names with transformation output names
algebraic.mle 1.1.0
- Add
coef() S3 method for base R compatibility (delegates to params())
- Add
logLik() S3 method returning proper logLik object with df and nobs attributes, enabling automatic AIC() and BIC() support from base R
- Fix
rmap() to accept numeric n parameter (previously required integer)
algebraic.mle 1.0.0
- Initial CRAN release
- Core MLE class (
mle) with methods for:
- Parameter extraction (
params, nparams)
- Variance-covariance (
vcov, se)
- Confidence intervals (
confint)
- Model comparison (
aic, loglik_val)
- Bias and MSE estimation (
bias, mse)
- Fisher information (
observed_fim)
- Sampling from MLE distribution (
sampler)
- Predictive intervals (
pred)
- Expected values (
expectation)
- Marginal distributions (
marginal)
- Numerical optimization wrapper (
mle_numerical) for optim() results
- Bootstrap MLE (
mle_boot) for small samples
- MLE transformations via invariance property (
rmap)
- Weighted combination of MLEs (
mle_weighted)
- Three vignettes demonstrating usage:
- Fitting common distributions to a DGP
- Statistics and characteristics of the MLE
- Data generating processes