# maxLik v1.4-6

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## Maximum Likelihood Estimation and Related Tools

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.

## Functions in maxLik

 Name Description bread.maxLik Bread for Sandwich Estimator gradient Extract Gradients Evaluated at each Observation MaxControl-class Class "MaxControl" fnSubset Call fnFull with variable and fixed parameters hessian Hessian matrix logLik.maxLik Return the log likelihood value maxBFGS BFGS, conjugate gradient, SANN and Nelder-Mead Maximization compareDerivatives function to compare analytic and numeric derivatives condiNumber Print matrix condition numbers column-by-column maxLik-internal Internal maxLik Functions activePar free parameters under maximization maxNR Newton- and Quasi-Newton Maximization maxValue Function value at maximum maxSGA Stochastic Gradient Ascent maximType Type of Minimization/Maximization AIC.maxLik Methods for the various standard functions objectiveFn Optimization Objective Function vcov.maxLik Variance Covariance Matrix of maxLik objects returnCode Success or failure of the optimization sumt Equality-constrained optimization maxLik Maximum likelihood estimation maxLik-package Maximum Likelihood Estimation summary.maxim Summary method for maximization nIter Return number of iterations for iterative models nParam.maxim Number of model parameters nObs.maxLik Number of Observations storedValues Return the stored values of optimization numericGradient Functions to Calculate Numeric Derivatives summary.maxLik summary the Maximum-Likelihood estimation No Results!