Mixture Models: Parametric, Semiparametric, and Robust
Complete Likelihood Frequency Method for Label Switching
Euclidean Distance Based Labeling Method for Label Switching
Parameter Estimation of Normal Mixture Using EM Algorithm
Kernel Density-based EM-type algorithm for Semiparametric Mixture Regr...
Kernel Density-based EM-type algorithm with Least Square Estimation fo...
Kernel Density-based EM-type algorithm for Semiparametric Mixture Regr...
Clustering with Mixtures of Log-concave Distributions using EM Algorit...
Clustering with Mixtures of Log-concave Distributions using EM Algorit...
Semiparametric Mixture Model by Minimizing Profile Hellinger Distance
Parameter Estimation for Uni- or Multivariate Normal Mixture Models
Two-component Normal Mixture Estimation with One Known Component
Profile Likelihood Method for Normal Mixture with Unequal Variance
MLE of Mixture Regression with Normal Errors
Robust EM Algorithm For Mixture of Linear Regression Based on Bisquare...
Robust Mixture Regression with Laplace Distribution
Mixture of Regression Models with Varying Mixing Proportions
Varying Proportion Mixture Data Generator
Robust Mixture Regression with Thresholding-Embedded EM Algorithm for ...
Robust Mixture Regression with T-distribution
Robust Regression Estimator Using Trimmed Likelihood
Continuous Scale Mixture Approach for Normal Scale Mixture Model
Goodness of Fit Test for Finite Mixture Models
Semiparametric Mixture of Binomial Regression with a Degenerate Compon...
Semiparametric Mixture of Binomial Regression with a Degenerate Compon...
Semiparametric Mixture of Binomial Regression with a Degenerate Compon...
Semiparametric Mixture Regression Models with Single-index Proportion ...
Semiparametric Mixture Data Generator
Semiparametric Mixtures of Nonparametric Regressions with Global EM-ty...
Semiparametric Mixtures of Nonparametric Regressions with Local EM-typ...
Semiparametric Mixture Regression Models with Single-index and One-ste...
Dimension Reduction Based on Sliced Inverse Regression
Various functions are provided to estimate parametric mixture models (with Gaussian, t, Laplace, log-concave distributions, etc.) and non-parametric mixture models. The package performs hypothesis tests and addresses label switching issues in mixture models. The package also allows for parameter estimation in mixture of regressions, proportion-varying mixture of regressions, and robust mixture of regressions.