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maxLik (version 1.3-0)

Maximum Likelihood Estimation and Related Tools

Description

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.

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Version

Install

install.packages('maxLik')

Monthly Downloads

35,858

Version

1.3-0

License

GPL (>= 2)

Maintainer

Ott Toomet

Last Published

October 24th, 2015

Functions in maxLik (1.3-0)

bread.maxLik

Bread for Sandwich Estimator
compareDerivatives

function to compare analytic and numeric derivatives
numericGradient

Functions to Calculate Numeric Derivatives
logLik.maxLik

Return the log likelihood value
estfun.maxLik

Extract Gradients Evaluated at each Observation
AIC.maxLik

Methods for the various standard functions
nIter

Return number of iterations for iterative models
nObs.maxLik

Number of Observations
maxLik-internal

Internal maxLik Functions
returnCode

Success or failure of the optimization
summary.maxLik

summary the Maximum-Likelihood estimation
MaxControl-class

Class "MaxControl"
maximType

Type of Minimization/Maximization
hessian

Hessian matrix
maxLik-package

Maximum Likelihood Estimation
summary.maxim

Summary method for maximization
fnSubset

Call fnFull with variable and fixed parameters
sumt

Equality-constrained optimization
activePar

free parameters under maximisation
maxLik

Maximum likelihood estimation
nParam.maxim

Number of model parameters
vcov.maxLik

Variance Covariance Matrix of maxLik objects
condiNumber

Print matrix condition numbers column-by-column
maxBFGS

BFGS, conjugate gradient, SANN and Nelder-Mead Maximization
maxNR

Newton- and Quasi-Newton Maximization