For the last eight years, microarray-based Classifying Equipment has been a major topic in statistics, bioinformatics and biomedicine research. Traditional methods often yield unsatisfactory results or may even be inapplicable in the so-called setting where the number of predictors p by far exceeds the number of observations n, hence the term. Careful model selection and evaluation satisfying accepted good-practice standards is a very complex task for statisticians without experience in this area or for scientists with limited statistical background. The multiplicity of available methods for class prediction based on high-dimensional data is an additional practical challenge for inexperienced researchers.

Conventional class prediction methods often yield poor results or may even be inapplicable in the context of high-dimensional data with more predictors than observations like microarray data. Microarray studies have thus stimulated the development of new approaches and motivated the adaptation of known traditional methods to the high-dimensional setting. Meanwhile, the latter has established itself as a standard tool for analyzing various types of high-throughput genomic data including microarray data. Throughout this article, the focus is on microarray data, but the presented package can be applied to any supervised classification problem involving a large number of continuous predictors such as, e.g. proteomic, metabolomic, or signal data. Model selection and evaluation of prediction rules turn out to be highly difficult setting for several reasons) the hazard of overfitting, which is common to all prediction problems, is considerably increased by high dimensionality) the usual evaluation scheme based on the splitting into learning and test data sets often applies only partially in the case of small samples, iii) modern classification techniques rely on the proper choice of hyperparameters whose optimization is highly computer-intensive, especially with high-dimensional data.

The multiplicity of available methods for class prediction based on high-dimensional data is an additional practical challenge for inexperienced researchers. Whereas logistic regression is well-established as the standard method to be used when analyzing classical data sets with much more observations than variables, there is no unique reference standard method. Moreover, the programs implementing well-known popular methods such as penalized logistic regression, nearest shrunken centroids, random forests, or partial least squares are characterized by a high heterogeneity as far as input format, output format, and tuning procedures are concerned. Inexperienced users have thus to spend much effort understanding each of the programs and modifying the data formats, while potentially introducing severe errors which may considerably affect the final results. Furthermore, the users may overlook important tuning parameters or detail settings that sometimes noticeably contribute to the success of the classifier. Note that circumventing the problem of the multiplicity of methods by always using a single "favorite method" (usually the method in the user's expertise area or a method which has been identified as top-performing method in a seminal comparison study) potentially leads to poor results, especially when the considered method involves strong assumptions on the data structure.

From the difficulties outlined above, we conclude that careful model selection and evaluation satisfying accepted good-practice standards is a very complex task for inexperienced users with limited statistical background.

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