maxLik v1.4-6


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Maximum Likelihood Estimation and Related Tools

Functions for Maximum Likelihood (ML) estimation, 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 Maximum Likelihood viewpoint. It also includes a number of convenience tools for testing and developing your own models.

Functions in maxLik

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


Date 2020-11-24
License GPL (>= 2)
ByteCompile yes
NeedsCompilation no
Packaged 2020-11-24 15:51:02 UTC; ott
Repository CRAN
Date/Publication 2020-11-24 16:30:05 UTC

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