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maxLik (version 1.6-10)

Maximum Likelihood Estimation and Related Tools

Description

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.

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Version

Install

install.packages('maxLik')

Monthly Downloads

33,782

Version

1.6-10

License

GPL (>= 2)

Issues

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Repository

https://github.com/arne-henningsen/maxLik

Maintainer

Ott Toomet

Last Published

October 1st, 2026

Functions in maxLik (1.6-10)

maxLik-internal

Internal maxLik Functions

Methods for the various standard functions

Newton- and Quasi-Newton Maximization

Stochastic Gradient Ascent

Maximum likelihood estimation

MaxControl-class

Class "MaxControl"

Return number of iterations for iterative models

Type of Minimization/Maximization

Maximum Likelihood Estimation

Objects exported from other packages

summary the Maximum-Likelihood estimation

Function value at maximum

Number of model parameters

Return the stored values of optimization

numericGradient

Functions to Calculate Numeric Derivatives

tidy and glance methods for maxLik objects

Optimization Objective Function

Equality-constrained optimization

Variance Covariance Matrix of maxLik objects

Summary method for maximization

Success or failure of the optimization

Number of Observations

Call fnFull with variable and fixed parameters

confint method for maxLik objects

Return the log likelihood value

Extract Gradients Evaluated at each Observation

Bread for Sandwich Estimator

BFGS, conjugate gradient, SANN and Nelder-Mead Maximization

Print matrix condition numbers column-by-column

compareDerivatives

function to compare analytic and numeric derivatives

free parameters under maximization