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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

450

Version

0.5-5

License

GPL (>= 2)

Maintainer

Last Published

June 4th, 2018

Functions in mixdist (0.5-5)

mixparam

Construct Starting Values for Parameters
poisdat

Grouped Poisson Data
pikepar

Starting Values of Parameters for the Pike Data
plot.mix

Mix Object Plotting
summary.mix

Summarizing Mixture Model Fits
mixpar2theta

Find the Parameters to be Estimated
pike65sg

Length-Frequency Data with Subsamples for Heming Lake Pike
pike65

Length-Frequency Data for Heming Lake Pike
pearsonpar

Starting Values of Parameters for the Pearson's Data
plot.mixdata

Mixdata Object Plotting
print.mix

Print Mix Object
weibullparinv

Compute the Mean and Standard Deviation of Weibull Distribution
pikdat5

Heming Lake Pike Data
testconstr

Check Constraints
poispar

Starting Values of Parameters for the Poisson Data Set
weibullpar

Compute Shape and Scale Parameters for Weibull Distribution
testpar

Check Parameters
pikeraw

A Sample of Pike Lengths
conditdat

Add Conditional Data to Grouped Data
bindat

Grouped Binomial Data
binpar

Starting Values of Parameters for the Binomial Data Set
anova.mix

ANOVA Tables for Mixture Model Objects
groupstats

Estimate Parameters of One-Component Mixture Distribution
fiftn80

A Mixed Data with Fifteen Normal Components
normals

Scale Mixture Data with Three Normal Components
mixtheta2par

Compute All of Parameters from the Estimated Parameters
mixdata

Mixed Data
mixgroup

Construct Grouped Data from Raw Data
mix

Estimate Parameters of Mixture Distributions
coef.mix

Extract Mixture Model Coefficients
expdat

A Mixture Data of Three Exponential Distributions
fitted.mix

Compute Mixture Model Fitted Values
cassie

Cassie's Length-Frequency Example
mixconstr

Construct Constraints on Parameters
grpintprob

Compute Probabilities of an Observation Falling into a Grouping Interval
pearson

Karl Pearson's Crab Data