Boosting and bagging are two widely used ensemble methods for classification. Their common goal is to improve the accuracy of a classifier combining single classifiers which are slightly better than random guessing. Among the family of boosting algorithms, AdaBoost (adaptive boosting) is the best known, although it is suitable only for dichotomous tasks. AdaBoost.M1 and SAMME (stagewise additive modeling using a multi-class exponential loss function) are two easy and natural extensions to the general case of two or more classes. In this paper, the adabag R package is introduced. This version implements AdaBoost.M1, SAMME and bagging algorithms with classification trees as base classifiers. Once the ensembles have been trained, they can be used to predict the class of new samples. The accuracy of these classifiers can be estimated in a separated data set or through cross validation. Moreover, the evolution of the error as the ensemble grows can be analysed and the ensemble can be pruned. In addition, the margin in the class prediction and the probability of each class for the observations can be calculated. Finally, several classic examples in classification literature are shown to illustrate the use of this package.

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The success on the design of new oral nanocarriers greatly depends on the identification of the best physicochemical properties that would allow their diffusion across the mucus layer that protects the intestinal epithelium. In this context, particle tracking (PT) has arisen in the pharmaceutical field as an excellent tool to evaluate the diffusion of individual particles across the intestinal mucus. In PT, the trajectories of individual particles are characterized by the mean square displacement (MSD), which is used to calculate the coefficient of diffusion (D) and the anomalous diffusion parameter (α) as . Unfortunately, there is no stablished criteria to evaluate the goodness-of-fit of the experimental data to the mathematical model. This work shows that the commonly used R2 parameter may lead to an overestimation of the diffusion capacity of oral nanocarriers. We propose a screening approach based on a combination of R2 with further statistical parameters. We have analyzed the effect of this approach to study the intestinal mucodiffusion of lipid oral nanocarriers, compared to the conventional screening approach. Last, we have developed software able to perform the whole PT analysis in a time-saving, user-friendly, and rational fashion.

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