maxLik

free parameters under maximization

Bread for Sandwich Estimator

function to compare analytic and numeric derivatives

Print matrix condition numbers column-by-column

Call fnFull with variable and fixed parameters

Extract Gradients Evaluated at each Observation

Hessian matrix

Return the log likelihood value

BFGS, conjugate gradient, SANN and Nelder-Mead Maximization

Class "MaxControl"

Maximum likelihood estimation

Internal maxLik Functions

Methods for the various standard functions

Maximum Likelihood Estimation

Newton- and Quasi-Newton Maximization

Stochastic Gradient Ascent

Function value at maximum

Type of Minimization/Maximization

Return number of iterations for iterative models

Number of Observations

Number of model parameters

Functions to Calculate Numeric Derivatives

Optimization Objective Function

Success or failure of the optimization

Return the stored values of optimization

summary the Maximum-Likelihood estimation

Summary method for maximization

Equality-constrained optimization

Variance Covariance Matrix of maxLik objects