Maximum Likelihood Estimation and Related Tools
free parameters under maximization
Bread for Sandwich Estimator
function to compare analytic and numeric derivatives
Print matrix condition numbers column-by-column
confint method for maxLik objects
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"
Type of Minimization/Maximization
Internal maxLik Functions
Methods for the various standard functions
Maximum Likelihood Estimation
Maximum likelihood estimation
Newton- and Quasi-Newton Maximization
Stochastic Gradient Ascent
Function value at maximum
Return number of iterations for iterative models
Number of Observations
Number of model parameters
Functions to Calculate Numeric Derivatives
Optimization Objective Function
Objects exported from other packages
Success or failure of the optimization
Return the stored values of optimization
Summary method for maximization
summary the Maximum-Likelihood estimation
Equality-constrained optimization
tidy and glance methods for maxLik objects
Variance Covariance Matrix of maxLik objects
Functions for Maximum Likelihood (ML) estimation, non-linear optimization, and related tools. It includes a unified way to call different optimizers, and classes and methods to handle the results from the Maximum Likelihood viewpoint. It also includes a number of convenience tools for testing and developing your own models.