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maxLik (version 1.3-6)

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

24,551

Version

1.3-6

License

GPL (>= 2)

Maintainer

Ott Toomet

Last Published

May 19th, 2019

Functions in maxLik (1.3-6)

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