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maxLik (version 1.3-4)

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

33,012

Version

1.3-4

License

GPL (>= 2)

Maintainer

Ott Toomet

Last Published

November 9th, 2015

Functions in maxLik (1.3-4)

estfun.maxLik

Extract Gradients Evaluated at each Observation
numericGradient

Functions to Calculate Numeric Derivatives
AIC.maxLik

Methods for the various standard functions
vcov.maxLik

Variance Covariance Matrix of maxLik objects
maximType

Type of Minimization/Maximization
condiNumber

Print matrix condition numbers column-by-column
MaxControl-class

Class "MaxControl"
nParam.maxim

Number of model parameters
nObs.maxLik

Number of Observations
fnSubset

Call fnFull with variable and fixed parameters
maxBFGS

BFGS, conjugate gradient, SANN and Nelder-Mead Maximization
summary.maxim

Summary method for maximization
nIter

Return number of iterations for iterative models
activePar

free parameters under maximisation
maxLik

Maximum likelihood estimation
hessian

Hessian matrix
compareDerivatives

function to compare analytic and numeric derivatives
summary.maxLik

summary the Maximum-Likelihood estimation
logLik.maxLik

Return the log likelihood value
maxLik-package

Maximum Likelihood Estimation
returnCode

Success or failure of the optimization
sumt

Equality-constrained optimization
maxNR

Newton- and Quasi-Newton Maximization
bread.maxLik

Bread for Sandwich Estimator
maxLik-internal

Internal maxLik Functions