lcmix: Layered and chained mixture models

Fit layered and chained mixture models, which are special cases of Bayesian belief networks, to lists of data.

AuthorDaniel Dvorkin
Date of publication2012-12-01 21:36:54
MaintainerDaniel Dvorkin <>
LicenseGPL (>= 2)

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Man pages

categorical: The Categorical Distribution

constants: "Constants" Used in Program Control

convergencePlot: Show EM Algorithm Convergence

covariance_etc: Covariance and Standard Deviation MLE

data_sets: Example Data Sets

distance_functions: Euclidean and Manhattan Distance

evpat: Evaluate Parsable Text

force.range: Force Within Range

isMDF: Test For Matrix Or Data Frame

lcmix-package: Layered and Chained Mixture Models

likelihood_functions: Likelihood-Based Mixture Model Statistics

list_functions: Convenience Functions For Lists

marginals: Extract Marginal Models

matrix_functions: Convenience Functions For Matrices

mdmixmod: Multiple Data Mixture Models

mixmod: Single Data Source Mixture Model

model_reporting: Mixture Model Reporting

multivariate_gamma_functions: Multivariate Gamma Functions

mvexp: The Multivariate Exponential Distribution

mvgamma: The Multivariate Gamma Distribution

mvnorm: The Multivariate Normal Distribution

mvpvii: The Multivariate Pearson Type VII (PVII) Distribution

mvweisd: The Multivariate Weibull (Shape-Decay) Distribution

numericols: Numeric Columns Of Data Frame

posteriors: Joint And Marginal Model Posterior Probabilities

pvii: The Pearson Type VII (PVII) Distribution

rlapply: Recursive List Apply

roc_curves: Receiver Operating Characteristic (ROC) Curves

simulation: Generate Simulated Data

standardize: Standardize Data

string_functions: Functions for Creating or Manipulating Strings

thetahat: Parameter Estimation for Common Distributions

uniform.quantiles: Evenly Spaced Sample Quantiles

weisd: The Weibull (Shape-Decay) Distribution

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