Fit Two-Component Normal and Lognormal Mixture Models

Fits, bootstraps, and evaluates two-component normal and lognormal mixture models. Parameters are searched within data-derived bounds by differential-evolution global optimization, as implemented in the 'DEoptim' package (Mullen, Ardia, Gil, Windover and Cline, 2011) , followed by a local 'L-BFGS-B' refinement step via optim(). Also provides preliminary diagnostic plots, diagnostic plots for fitted mixtures, automatic normal-versus-lognormal model selection by the Bayesian or Akaike information criterion, and parametric or nonparametric bootstrap confidence intervals for the fitted parameters.