A major obstacle hampering the therapeutic application of regulatory T (Treg) cells is the lack of suitable extracellular markers, which complicates their identification/isolation. Treg cells are normally isolated via CD25 (IL-2Ra) targeting, but this protein is also expressed by activated CD4

In multiple regression models, when there are a large number (p) of explanatory variables which may or may not be relevant for predicting the response, it is useful to be able to reduce the model. To this end, it is necessary to determine the best subset of q (q ≤ p) predictors which will establish the model with the best prediction capacity. FWDselect package introduces a new forward stepwisebased selection procedure to select the best model in different regression frameworks (parametric or nonparametric). The developed methodology, which can be equally applied to linear models, generalized linear models or generalized additive models, aims to introduce solutions to the following two topics: i) selection of the best combination of q variables by using a step-by-step method; and, perhaps, most importantly, ii) search for the number of covariates to be included in the model based on bootstrap resampling techniques. The software is illustrated using real and simulated data.

We present the R npregfast package via some applications involved with the study of living organisms. The package implements nonparametric estimation procedures in regression models with or without factor-by-curve interactions. The main feature of the package is its ability to perform inference regarding these models. Namely, the implementation of different procedures to test features of the estimated regression curves: on the one hand, the comparisons between curves which may vary across groups defined by levels of a categorical variable or factor; on the other hand, the comparisons of some critical points of the curve (e.g., maxima, minima or inflection points), studying for this purpose the derivatives of the curve.

Survival analysis includes a wide variety of methods for analyzing time‐to‐event data. One basic but important goal in survival analysis is the comparison of survival curves between groups. Several nonparametric methods have been proposed in the literature to test for the equality of survival curves for censored data. When the null hypothesis of equality of curves is rejected, leading to the clear conclusion that at least one curve is different, it can be interesting to ascertain whether curves can be grouped or if all these curves are different from each other. A method is proposed that allows determining groups with an automatic selection of their number. The validity and behavior of the proposed method was evaluated through simulation studies. The applicability of the proposed method is illustrated using real data. Software in the form of an R package has been developed implementing the proposed method.

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In many situations, it could be interesting to ascertain whether groups of curves can be performed, especially when confronted with a considerable number of curves. This paper introduces an R package, known as clustcurv, for determining clusters of curves with an automatic selection of their number. The package can be used for determining groups in multiple survival curves as well as for multiple regression curves. Moreover, it can be used with large numbers of curves. An illustration of the use of clustcurv is provided, using both real data examples and artificial data.

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Identifying the mutational processes that shape the nucleotide composition of the mitochondrial genome (mtDNA) is fundamental to better understand how these genomes evolve. Several methods have been proposed to analyze DNA sequence nucleotide composition and skewness, but most of them lack any measurement of statistical support or were not developed taking into account the specificities of mitochondrial genomes. A new methodology is presented, which is specifically developed for mtDNA to detect compositional changes or asymmetries (AT and CG skews) based on nonparametric regression models and their derivatives. The proposed method also includes the construction of confidence intervals, which are built using bootstrap techniques. This paper introduces an R package, known as seq2R, that implements the proposed methodology. Moreover, an illustration of the use of seq2R is provided using real data, specifically two publicly available complete mtDNAs: the human (Homo sapiens) sequence and a nematode (Radopholus similis) mitogenome sequence.