**[FMM] fmm estimation** -- Fitting finite mixture models

__Description__

Finite mixture models (FMMs) are used to classify observations, to adjust
for clustering, and to model unobserved heterogeneity. In finite mixture
modeling, the observed data are assumed to belong to several unobserved
subpopulations called classes, and mixtures of probability densities or
regression models are used to model the outcome of interest. After
fitting the model, class membership probabilities can also be predicted
for each observation.

**Linear regression models**

**[FMM] fmm: regress** Linear regression
**[FMM] fmm: truncreg** Truncated regression
**[FMM] fmm: intreg** Interval regression
**[FMM] fmm: tobit** Tobit regression
**[FMM] fmm: ivregress** Instrumental-variables regression

**Binary-response regression models**

**[FMM] fmm: logit** Logistic regression, reporting
coefficients
**[FMM] fmm: probit** Probit regression
**[FMM] fmm: cloglog** Complementary log-log regression

**Ordinal-response regression models**

**[FMM] fmm: ologit** Ordered logistic regression
**[FMM] fmm: oprobit** Ordered probit regression

**Categorical-response regression models**

**[FMM] fmm: mlogit** Multinomial (polytomous) logistic
regression

**Count-response regression models**

**[FMM] fmm: poisson** Poisson regression
**[FMM] fmm: nbreg** Negative binomial regression
**[FMM] fmm: tpoisson** Truncated Poisson regression

**Generalized linear models**

**[FMM] fmm: glm** Generalized linear models

**Fractional-response regression models**

**[FMM] fmm: betareg** Beta regression

**Survival regression models**

**[FMM] fmm: streg** Parametric survival models

**fmm:** allows different regression models for different components of the
mixture; see **[FMM] fmm**. **fmm:** also allows one or more components to be a
degenerate distribution taking on a single integer value with probability
one; see **[FMM] fmm: pointmass**.