Tools for Analyzing Finite Mixture Models
Augmented Predictor Function
Performs Parametric Bootstrap for Sequentially Testing the Number of C...
Performs Parametric Bootstrap for Standard Error Approximation
Plot the Component CDF
Density Function for the Dirichlet Distribution
Normal kernel density estimate for nonparametric EM output
Normal kernel density estimate for semiparametric EM output
Elliptical and Spherical Depth
The Multivariate Normal Density
Draw Two-Dimensional Ellipse Based on Mean and Covariance
EM algorithm for Reliability Mixture Models (RMM) with right Censoring
EM Algorithm for Mixtures of Regressions with Flare
EM Algorithm for Mixtures of Gamma Distributions
EM Algorithm for Mixtures-of-Experts
Initializations for Various EM Algorithms in 'mixtools'
Integrated Squared Error for a selected density from npEM output
Perturbation of Mixing Proportions
Local Estimation for Lambda in Mixtures of Regressions
Log-Density for Multinomial Distribution
EM Algorithm for Mixtures of Logistic Regressions
Produce Cutpoint Multinomial Data
Calculates the Square Root of a Diagonalizable Matrix
Internal 'mixtools' Functions
Mixturegrams
EM Algorithm for Mixtures of Multinomials
Model Selection Mixtures of Multinomials
EM Algorithm for Mixtures of Multivariate Normals
EM-like Algorithm for Nonparametric Mixture Models with Conditionally ...
EM Algorithm for Mixtures of Univariate Normals
Fast EM Algorithm for 2-Component Mixtures of Univariate Normals
EC-MM Algorithm for Mixtures of Univariate Normals with linear constra...
Nonparametric EM-like Algorithm for Mixtures of Independent Repeated M...
Nonparametric EM-like Algorithm for Mixtures of Independent Repeated M...
Constraint Function
Permutation Function
Various Plots Pertaining to Mixture Model Output Using MCMC Methods
Various Plots Pertaining to Mixture Models
Plots of Marginal Density Estimates from the mvnpEM Algorithm Output
Plot Nonparametric or Semiparametric EM Output
Plot mixture pdf for the semiparametric mixture model output by spEMsy...
Plot sequences from the EM algorithm for censored mixture of exponenti...
Plot False Discovery Rate (FDR) estimates from output by EM-like strat...
Plot the Component CDF using plotly
Draw Two-Dimensional Ellipse Based on Mean and Covariance using `plotl...
Plot sequences from the EM algorithm for censored mixture of exponenti...
Plot False Discovery Rate (FDR) estimates from output by EM-like strat...
Visualization of Integrated Squared Error for a selected density from ...
Visualization of output of mixEM function using plotly
Various Plots Pertaining to Mixture Model Output Using MCMC Methods us...
Mixturegrams
Plot Nonparametric or Semiparametric EM Output
Visualization of Posterior Regression Coefficients in Mixtures of Rand...
Plotting sequences of estimates from non- or semiparametric EM-like Al...
Plot mixture pdf for the semiparametric mixture model output by `spEMs...
Plot output from Stochastic EM algorithm for semiparametric scaled mix...
Plot sequences from the Stochastic EM algorithm for mixture of Weibull...
Plotting sequences of estimates from non- or semiparametric EM-like Al...
Plot output from Stochastic EM algorithm for semiparametric scaled mix...
Plot sequences from the Stochastic EM algorithm for mixture of Weibull
EM Algorithm for Mixtures of Poisson Regressions
Summary of Posterior Regression Coefficients in Mixtures of Random Eff...
Printing of Results from the mvnpEM Algorithm Output
Printing non- and semi-parametric multivariate mixture model fits
Add a Confidence Region or Bayesian Credible Region for Regression Lin...
EM Algorithm for Mixtures of Regressions with Local Lambda Estimates
Iterative Algorithm Using EM Algorithm for Mixtures of Regressions wit...
EM Algorithm for Mixtures of Regressions with Random Effects
EM Algorithm for Mixtures of Regressions
Metropolis-Hastings Algorithm for Mixtures of Regressions
Model Selection in Mixtures of Regressions
EM Algorithm for Mixtures of Normals with Repeated Measurements
Model Selection in Mixtures of Normals with Repeated Measures
Simulate from Mixtures of Exponentials
Simulate from a Multivariate Normal Distribution
Simulate from Multivariate (repeated measures) Mixtures of Normals
Simulate from Mixtures of Normals
Simulate from Mixtures of Weibull distributions
ECM Algorithm for Mixtures of Regressions with Changepoints
Semiparametric EM-like Algorithm for Mixtures of Independent Repeated ...
Semiparametric EM-like Algorithm for univariate symmetric location mix...
semiparametric EM-like algorithm for univariate mixture in False Disco...
EM-like Algorithm for Semiparametric Mixtures of Regressions
Stochastic EM algorithm for semiparametric scaled mixture of censored ...
Summarizing EM mixture model fits
Summarizing Fits for Nonparametric Mixture Models with Conditionally I...
Summarizing non- and semi-parametric multivariate mixture model fits
Summarizing fits from Stochastic EM algorithm for semiparametric scale...
Special EM Algorithm for three-component tau equivalence model
Performs Chi-Square Test for Mixed Effects Mixtures
Performs Chi-Square Tests for Scale and Location Mixtures
Mixtures of Regressions with Flare MM Algorithm
St-EM algorithm for Reliability Mixture Models (RMM) of Weibull with r...
Weighted Univariate (Normal) Kernel Density Estimate
Weighted quantiles
Analyzes finite mixture models for various parametric and semiparametric settings. This includes mixtures of parametric distributions (normal, multivariate normal, multinomial, gamma), various Reliability Mixture Models (RMMs), mixtures-of-regressions settings (linear regression, logistic regression, Poisson regression, linear regression with changepoints, predictor-dependent mixing proportions, random effects regressions, hierarchical mixtures-of-experts), and tools for selecting the number of components (bootstrapping the likelihood ratio test statistic, mixturegrams, and model selection criteria). Bayesian estimation of mixtures-of-linear-regressions models is available as well as a novel data depth method for obtaining credible bands. This package is based upon work supported by the National Science Foundation under Grant No. SES-0518772 and the Chan Zuckerberg Initiative: Essential Open Source Software for Science (Grant No. 2020-255193).