Man pages for maxLik
Maximum Likelihood Estimation and Related Tools
| activePar | free parameters under maximization |
| bread.maxLik | Bread for Sandwich Estimator |
| compareDerivatives | function to compare analytic and numeric derivatives |
| condiNumber | Print matrix condition numbers column-by-column |
| fnSubset | Call fnFull with variable and fixed parameters |
| gradient | Extract Gradients Evaluated at each Observation |
| hessian | Hessian matrix |
| logLik.maxLik | Return the log likelihood value |
| maxBFGS | BFGS, conjugate gradient, SANN and Nelder-Mead Maximization |
| maxControl | Class '"MaxControl"' |
| maximType | Type of Minimization/Maximization |
| maxLik | Maximum likelihood estimation |
| maxLik-internal | Internal maxLik Functions |
| maxLik-methods | Methods for the various standard functions |
| maxLik-package | Maximum Likelihood Estimation |
| maxNR | Newton- and Quasi-Newton Maximization |
| maxSGA | Stochastic Gradient Ascent |
| maxValue | Function value at maximum |
| nIter | Return number of iterations for iterative models |
| nObs | Number of Observations |
| nParam | Number of model parameters |
| numericGradient | Functions to Calculate Numeric Derivatives |
| objectiveFn | Optimization Objective Function |
| returnCode | Success or failure of the optimization |
| storedValues | Return the stored values of optimization |
| summary.maxim | Summary method for maximization |
| summary.maxLik | summary the Maximum-Likelihood estimation |
| sumt | Equality-constrained optimization |
| vcov.maxLik | Variance Covariance Matrix of maxLik objects |
