Learn R Programming

maxLik (version 1.5-2.1)

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.

Copy Link

Version

Install

install.packages('maxLik')

Monthly Downloads

30,069

Version

1.5-2.1

License

GPL (>= 2)

Maintainer

Last Published

March 24th, 2024

Functions in maxLik (1.5-2.1)

BFGS, conjugate gradient, SANN and Nelder-Mead Maximization

Print matrix condition numbers column-by-column

compareDerivatives

function to compare analytic and numeric derivatives

MaxControl-class

Class "MaxControl"

Newton- and Quasi-Newton Maximization

Methods for the various standard functions

Stochastic Gradient Ascent

Return number of iterations for iterative models

Maximum likelihood estimation

Maximum Likelihood Estimation

Type of Minimization/Maximization

maxLik-internal

Internal maxLik Functions

Summary method for maximization

Equality-constrained optimization

Success or failure of the optimization

Objects exported from other packages

tidy and glance methods for maxLik objects

Function value at maximum

Variance Covariance Matrix of maxLik objects

numericGradient

Functions to Calculate Numeric Derivatives

Return the stored values of optimization

summary the Maximum-Likelihood estimation

Optimization Objective Function

Number of Observations

Number of model parameters

confint method for maxLik objects

Return the log likelihood value

Extract Gradients Evaluated at each Observation

Bread for Sandwich Estimator

Call fnFull with variable and fixed parameters

free parameters under maximization