The R package partykit provides a flexible toolkit for learning, representing, summarizing, and visualizing a wide range of tree-structured regression and classification models. The functionality encompasses: (a) basic infrastructure for representing trees (inferred by any algorithm) so that...

Variable importance measures for random forests have been receiving increased attention as a means of variable selection in many classification tasks in bioinformatics and related scientific fields, for instance to select a subset of genetic markers relevant for the prediction of a certain...

To obtain a probabilistic model for a dependent variable based on some set of explanatory variables, a distributional approach is often adopted where the parameters of the distribution are linked to regressors. In many classical models this only captures the location of the distribution but over...

The quest of the best classifier for a discriminant analysis problem is often rather hard and a combination of classifiers of different type promises to lead to improved predictive models compared to selecting one of the competitors. We propose to use the out-of-bag sample for training of...

The combination of classifiers leads to substantial reduction of misclassification error in a wide range of applications and benchmark problems. We suggest to use the out-of-bag sample for combining different classifiers. In our setup, a Linear Discriminant Analysis is performed using the...

In this paper a projection pursuit method is developed which determines optimal multivariate latent factor models based on a flexible loss function. This way, the unknown model coefficients are estimated with respect to optimal predictive power. The specification of the loss function in...

Precise knowledge about factors influencing the habitat suitability of a certain species forms the basis for the implementation of effective programs to conserve biological diversity. Such knowledge is frequently gathered from studies relating abundance data to a set of influential variables in...

The Bioconductor project is an initiative for the collaborative creation of the extensible software for computational biology and bioinformatics. The goals of the project include: fostering collaborative development and widespread use of innovative software, reducing barriers to entry into...