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mixdist (version 0.5-5)

Finite Mixture Distribution Models

Description

Fit finite mixture distribution models to grouped data and conditional data by maximum likelihood using a combination of a Newton-type algorithm and the EM algorithm.

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Version

Install

install.packages('mixdist')

Monthly Downloads

598

Version

0.5-5

License

GPL (>= 2)

Homepage

https://www.r-project.org/

Maintainer

Peter Macdonald

Last Published

June 4th, 2018

Functions in mixdist (0.5-5)

Construct Starting Values for Parameters

Grouped Poisson Data

Starting Values of Parameters for the Pike Data

Mix Object Plotting

Summarizing Mixture Model Fits

Find the Parameters to be Estimated

Length-Frequency Data with Subsamples for Heming Lake Pike

Length-Frequency Data for Heming Lake Pike

Starting Values of Parameters for the Pearson's Data

Mixdata Object Plotting

Print Mix Object

Compute the Mean and Standard Deviation of Weibull Distribution

Heming Lake Pike Data

Check Constraints

Starting Values of Parameters for the Poisson Data Set

Compute Shape and Scale Parameters for Weibull Distribution

Check Parameters

A Sample of Pike Lengths

Add Conditional Data to Grouped Data

Grouped Binomial Data

Starting Values of Parameters for the Binomial Data Set

ANOVA Tables for Mixture Model Objects

Estimate Parameters of One-Component Mixture Distribution

A Mixed Data with Fifteen Normal Components

Scale Mixture Data with Three Normal Components

Compute All of Parameters from the Estimated Parameters

Construct Grouped Data from Raw Data

Estimate Parameters of Mixture Distributions

Extract Mixture Model Coefficients

A Mixture Data of Three Exponential Distributions

Compute Mixture Model Fitted Values

Cassie's Length-Frequency Example

Construct Constraints on Parameters

Compute Probabilities of an Observation Falling into a Grouping Interval

Karl Pearson's Crab Data