Package index • mixtools

All functions

CO2data: GNP and CO2 Data Set
Habituationdata: Infant habituation data
NOdata: Ethanol Fuel Data Set
RTdata: Reaction Time (RT) Data Set
RTdata2: Reaction Time (RT) Data Set (No. 2)
RanEffdata: Simulated Data from 2-Component Mixture of Regressions with Random Effects
RodFramedata: Rod and Frame Task Data Set
Waterdata: Water-Level Task Data Set
boot.comp(): Performs Parametric Bootstrap for Sequentially Testing the Number of Components in Various Mixture Models
boot.se(): Performs Parametric Bootstrap for Standard Error Approximation
compCDF(): Plot the Component CDF
density(<npEM>): Normal kernel density estimate for nonparametric EM output
density(<spEM>): Normal kernel density estimate for semiparametric EM output
depth(): Elliptical and Spherical Depth
dmvnorm() logdmvnorm(): The Multivariate Normal Density
ellipse(): Draw Two-Dimensional Ellipse Based on Mean and Covariance
expRMM_EM(): EM algorithm for Reliability Mixture Models (RMM) with right Censoring
flaremixEM(): EM Algorithm for Mixtures of Regressions with Flare
gammamixEM(): EM Algorithm for Mixtures of Gamma Distributions
hmeEM(): EM Algorithm for Mixtures-of-Experts
ise.npEM(): Integrated Squared Error for a selected density from npEM output
logisregmixEM(): EM Algorithm for Mixtures of Logistic Regressions
makemultdata(): Produce Cutpoint Multinomial Data
mixturegram(): Mixturegrams
multmixEM(): EM Algorithm for Mixtures of Multinomials
multmixmodel.sel(): Model Selection Mixtures of Multinomials
mvnormalmixEM(): EM Algorithm for Mixtures of Multivariate Normals
mvnpEM(): EM-like Algorithm for Nonparametric Mixture Models with Conditionally Independent Multivariate Component Densities
normalmixEM(): EM Algorithm for Mixtures of Univariate Normals
normalmixEM2comp(): Fast EM Algorithm for 2-Component Mixtures of Univariate Normals
normalmixMMlc(): EC-MM Algorithm for Mixtures of Univariate Normals with linear constraints
npEM(): Nonparametric EM-like Algorithm for Mixtures of Independent Repeated Measurements
npMSL(): Nonparametric EM-like Algorithm for Mixtures of Independent Repeated Measurements - Maximum Smoothed Likelihood version
plot(<mixMCMC>): Various Plots Pertaining to Mixture Model Output Using MCMC Methods
plot(<mixEM>): Various Plots Pertaining to Mixture Models
plot(<mvnpEM>): Plots of Marginal Density Estimates from the mvnpEM Algorithm Output
plot(<npEM>) plot(<spEM>): Plot Nonparametric or Semiparametric EM Output
plot(<spEMN01>): Plot mixture pdf for the semiparametric mixture model output by spEMsymlocN01
plotFDR(): Plot False Discovery Rate (FDR) estimates from output by EM-like strategies
plotexpRMM(): Plot sequences from the EM algorithm for censored mixture of exponentials
plotly_FDR(): Plot False Discovery Rate (FDR) estimates from output by EM-like strategies using plotly
plotly_compCDF(): Plot the Component CDF using plotly
plotly_ellipse(): Draw Two-Dimensional Ellipse Based on Mean and Covariance using plotly
plotly_expRMM(): Plot sequences from the EM algorithm for censored mixture of exponentials using plotly
plotly_ise.npEM(): Visualization of Integrated Squared Error for a selected density from npEM output using plotly
plotly_mixEM(): Visualization of output of mixEM function using plotly
plotly_mixMCMC(): Various Plots Pertaining to Mixture Model Output Using MCMC Methods using plotly
plotly_mixturegram(): Mixturegrams
plotly_npEM() plotly_spEM(): Plot Nonparametric or Semiparametric EM Output
plotly_seq.npEM(): Plotting sequences of estimates from non- or semiparametric EM-like Algorithm using plotly
plotly_spEMN01(): Plot mixture pdf for the semiparametric mixture model output by spEMsymlocN01 using plotly.
plotly_spRMM(): Plot output from Stochastic EM algorithm for semiparametric scaled mixture of censored data using plotly.
plotly_weibullRMM(): Plot sequences from the Stochastic EM algorithm for mixture of Weibull using plotly
plotseq(<npEM>): Plotting sequences of estimates from non- or semiparametric EM-like Algorithm
plotspRMM(): Plot output from Stochastic EM algorithm for semiparametric scaled mixture of censored data
plotweibullRMM(): Plot sequences from the Stochastic EM algorithm for mixture of Weibull
poisregmixEM(): EM Algorithm for Mixtures of Poisson Regressions
post.beta(): Summary of Posterior Regression Coefficients in Mixtures of Random Effects Regressions
print(<mvnpEM>): Printing of Results from the mvnpEM Algorithm Output
print(<npEM>): Printing non- and semi-parametric multivariate mixture model fits
regcr(): Add a Confidence Region or Bayesian Credible Region for Regression Lines to a Scatterplot
regmixEM(): EM Algorithm for Mixtures of Regressions
regmixEM.lambda(): EM Algorithm for Mixtures of Regressions with Local Lambda Estimates
regmixEM.loc(): Iterative Algorithm Using EM Algorithm for Mixtures of Regressions with Local Lambda Estimates
regmixEM.mixed(): EM Algorithm for Mixtures of Regressions with Random Effects
regmixMH(): Metropolis-Hastings Algorithm for Mixtures of Regressions
regmixmodel.sel(): Model Selection in Mixtures of Regressions
repnormmixEM(): EM Algorithm for Mixtures of Normals with Repeated Measurements
repnormmixmodel.sel(): Model Selection in Mixtures of Normals with Repeated Measures
rexpmix(): Simulate from Mixtures of Exponentials
rmvnorm(): Simulate from a Multivariate Normal Distribution
rmvnormmix(): Simulate from Multivariate (repeated measures) Mixtures of Normals
rnormmix(): Simulate from Mixtures of Normals
rweibullmix(): Simulate from Mixtures of Weibull distributions
segregmixEM(): ECM Algorithm for Mixtures of Regressions with Changepoints
spEM(): Semiparametric EM-like Algorithm for Mixtures of Independent Repeated Measurements
spEMsymloc(): Semiparametric EM-like Algorithm for univariate symmetric location mixture
spEMsymlocN01(): semiparametric EM-like algorithm for univariate mixture in False Discovery Rate (FDR) estimation
spRMM_SEM(): Stochastic EM algorithm for semiparametric scaled mixture of censored data
spregmix(): EM-like Algorithm for Semiparametric Mixtures of Regressions
summary(<mixEM>): Summarizing EM mixture model fits
summary(<mvnpEM>) print(<summary.mvnpEM>): Summarizing Fits for Nonparametric Mixture Models with Conditionally Independent Multivariate Component Densities
summary(<npEM>) print(<summary.npEM>): Summarizing non- and semi-parametric multivariate mixture model fits
summary(<spRMM>): Summarizing fits from Stochastic EM algorithm for semiparametric scaled mixture of censored data
tauequivnormalmixEM(): Special EM Algorithm for three-component tau equivalence model
test.equality(): Performs Chi-Square Tests for Scale and Location Mixtures
test.equality.mixed(): Performs Chi-Square Test for Mixed Effects Mixtures
tonedata: Tone perception data
weibullRMM_SEM(): St-EM algorithm for Reliability Mixture Models (RMM) of Weibull with right Censoring
wkde(): Weighted Univariate (Normal) Kernel Density Estimate
wquantile() wIQR(): Weighted quantiles