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>.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
2.0.0 |
2026-04-09 windows/windows R-4.5 | compositional.mle_2.0.0.zip |
593.2 KiB |