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
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Details

Date 2019-05-18
License GPL (>= 2)
ByteCompile yes
NeedsCompilation no
Packaged 2019-05-19 05:40:23 UTC; siim
Repository CRAN
Date/Publication 2019-05-19 15:45:49 UTC

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