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bgev 0.3

Estimation

• bgev_mle() was rewritten. It now uses a multistart Nelder-Mead search on a reparametrised scale (log(sigma), log(delta)), seeded by quantile matching plus loose data-driven box starts. Estimation is restricted to delta > 0 (bimodality requires it, and for delta < 0 the likelihood is unbounded at x = mu); the distribution functions still accept the full delta > -1.
• The return value changed: instead of the old DEoptim object ($optim$bestmem), bgev_mle() now returns $par, $se, $loglik and diagnostics. This is a breaking change.
• Standard errors (se) are returned from the inverse observed-information Hessian, for an admissible (regular) optimum only; near the parameter-dependent support boundary they are not reliable and are returned as NA.
• New diagnostics on every fit: convergence, agree, admissible (a positive-definite-Hessian acceptance gate that rejects spurious optima), boundary, and optimum.
• likelihood = "grouped_likelihood" added for discrete or rounded data, using the interval likelihood F(x + h/2) - F(x - h/2); tied data under the continuous density now raises a warning.
• New bgev_profile_likelihood() for the profile log-likelihood of a parameter.

Documentation

• New vignette “Maximum Likelihood Estimation for the BGEV Distribution”, including a Monte Carlo validation of the estimator.

Internal

• distCheck() renamed to dist_check().
• Imports: dropped DEoptim and lhs; added numDeriv and graphics.

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.