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Some standard strategies include censored regression models such as the tobit (zero-censored Gaussian) or continuous hurdle models (combining a binary and a zero-truncated model). Especially if the nu …

Hence, I'll explain the latter here using somewhat simplified R code. …

You are correct that sctest() from the R package strucchange does not implement tests for the $k$ vs. $k+1$ break problem. …

The lmtree() function as well as the the underlying mob() function distinguish weights being used as case weights (default) or proportionality weights. In the former case, the number of observations i …

This is also the standard link in glm.nb() in R - as well as in other R packages implementing the model. … An R implementation is available in the VGAM package, see https://www.rdocumentation.org/packages/VGAM/versions/1.0-6/topics/nbcanlink …

To compute these quantities in R using the betareg package you can simply use the predict() method. For example, you can easily obtain $\mu_i$ using predict(object, type = "mean"). …

If you want a convenience function to carry out this test in R, you can use the encomptest() function from the lmtest package: encomptest(m1, m2) The package also provides other tests for non-nested …

For an unordered factor the default in R is to use treatment contrasts, i.e., restrict the first coefficient to be zero and then estimate the other two in relation to that. …

Of course, there are more machine learning models implemented in R than supported by the PMML standard or the pmml R package but there is quite a range of supported models. … The pmml package is also employed by the rattle data mining GUI in R. …

The model-based recursive partitioning algorithm (MOB) decides in each step whether the parameters of the model are stable across the partitioning variables or whether there is a significant instabili …

The strategy you describe looks very reasonable. For evaluation you can use the usual kinds of measures that you employ for other binary classiers (or trees in particular): misclassification rate (or …

Without censoring the tobit model just reduces to a standard linear regression model (as correctly pointed out). The standard tobit model uses a Gaussian assumption where the maximum likelihood (ML) e …

This is the algorithm which is implemented in the R package CHAID. …

Simple things first: As in many other regression models, the intercept is not of interest per se but included to be flexible enough in the model. Whether or not it is significant is often not of inter …

I share the critical view of upsampling raised in some of the previous comments. Especially for conditional inference trees there is the additional issue that the meaning of the $p$-values in the tree …