compositional.mle: Compositional Maximum Likelihood Estimation
Provides composable optimization strategies for maximum likelihood
estimation (MLE). Solvers are first-class functions that combine via
sequential chaining, parallel racing, and random restarts. Implements
gradient ascent, Newton-Raphson, quasi-Newton (BFGS), and derivative-free
methods with support for constrained optimization and tracing. Returns
'mle' objects compatible with 'algebraic.mle' for downstream analysis.
Methods based on Nocedal J, Wright SJ (2006) "Numerical Optimization"
<doi:10.1007/978-0-387-40065-5>.
| Version: |
2.0.0 |
| Depends: |
R (≥ 3.5.0), algebraic.mle (≥ 2.0.0) |
| Imports: |
MASS, numDeriv |
| Suggests: |
rmarkdown, dplyr, knitr, ggplot2, tibble, testthat (≥
3.0.0), cli, future, hypothesize, likelihood.model |
| Published: |
2026-03-19 |
| DOI: |
10.32614/CRAN.package.compositional.mle |
| Author: |
Alexander Towell
[aut, cre] |
| Maintainer: |
Alexander Towell <queelius at gmail.com> |
| BugReports: |
https://github.com/queelius/compositional.mle/issues |
| License: |
MIT + file LICENSE |
| URL: |
https://github.com/queelius/compositional.mle,
https://queelius.github.io/compositional.mle/ |
| NeedsCompilation: |
no |
| Citation: |
compositional.mle citation info |
| Materials: |
README, NEWS |
| CRAN checks: |
compositional.mle results |
Documentation:
Downloads:
Reverse dependencies:
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