### Usage

```
crch(formula, data, subset, na.action, weights, offset,
link.scale = c("log", "identity", "quadratic"),
dist = c("gaussian", "logistic", "student"), df = NULL,
left = -Inf, right = Inf, truncated = FALSE,
type = c("ml", "crps"), control = crch.control(...),
model = TRUE, x = FALSE, y = FALSE, ...)
trch(formula, data, subset, na.action, weights, offset,
link.scale = c("log", "identity", "quadratic"),
dist = c("gaussian", "logistic", "student"), df = NULL,
left = -Inf, right = Inf, truncated = TRUE,
type = c("ml", "crps"), control = crch.control(...),
model = TRUE, x = FALSE, y = FALSE, ...)
crch.fit(x, z, y, left, right, truncated = FALSE, dist = "gaussian",
df = NULL, link.scale = "log", type = "ml", weights = NULL, offset = NULL,
control = crch.control())
```

### Details

`crch`

fits censored (tobit) or truncated regression models with conditional heteroscedasticy with maximum likelihood estimation. Student-t, Gaussian, and logistic distributions can be fitted to left- and/or right censored or truncated responses. Different regressors can be used to model the location and the scale of this distribution. If `control=crch.boost()`

optimization is performed by boosting.

`trch`

is a wrapper function for `crch`

with default `truncated = TRUE`

.

`crch.fit`

is the lower level function where the actual fitting takes place.

### References

Messner JW, Mayr GJ, Zeileis A (2016). Heteroscedastic Censored and Truncated Regression with crch. *The R Journal*, **3**(1), 173–181. doi:10.32614/RJ-2016-012

Messner JW, Zeileis A, Broecker J, Mayr GJ (2014). Probabilistic Wind Power Forecasts with an Inverse Power Curve Transformation and Censored Regression. *Wind Energy*, **17**(11), 1753–1766. doi:10.1002/we.1666