# maxLik v1.3-6

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## Maximum Likelihood Estimation and Related Tools

Functions for Maximum Likelihood (ML) estimation and 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 ML viewpoint. It also includes a number of convenience tools for testing and developing your own models.

## Functions in maxLik

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