About me

Nora M. Villanueva is Assistant Professor at the Department of Statistics and O. R. at the University of Vigo. In 2021, she received her PhD in Statistics and O. R form this university, with the title "New contributions to the identification of groups in nonparametric curves". Previously, she worked as data scientist focused on the prediction of events in the Big Data framework with applications to telecommunications field (Optare Solutions Company) or to energy (Ecomanagement Technology company). More recently, she worked for about 6 years at the Galician Research and Development Center in Advanced Telecommunications (Gradiant). During these years, she has combined her work with some researching and teaching as a member of the Department of Statistics and O. R., University of Vigo. Her research lines are focused on computational statistics, nonparametric regression, survival analysis and software development. So far, her contributions have been published in 7 articles within high impact journals (Statistics and Probability) resulting from her participation in 17 research projects and 39 international/national conferences and she has also developed 4 R packages.

Contact

Mail: noramvillanueva@gmail.com

Mail: nmvillanueva@uvigo.gal

Twitter: @noramvillanueva

Github: https://github.com/noramvillanueva

LinkedIn: Nora M. Villanueva

Portal Científico: Nora M. Villanueva

Research

Journal articles

PhD thesis

Technical documents

Software


  • clustcurv package. Author and mantainer of this package (https://cran.r-project.org/web/packages/clustcurv/) for determining groups in multiple curves with an automatic selection of their number based on k-means or k-medians algorithms. The selection of the optimal number is provided by bootstrap methods. The methodology can be applied both in regression and survival framework. Implemented methods are: Grouping multiple survival curves described by Villanueva et al. (2019).


  • npregfast package. Website link Author and maintainer of this R package (http://cran.r-project.org/web/packages/npregfast/) to perform nonparametric estimation for analyzing interactions factor-by-curve. npregfast allows the user to obtain nonparametric estimates using local linear kernel smoothers and compare them between factor’s levels. Also a feature of the package is its ability to draw inference about critical points, such as maxima or change points linked to the derivative curves. The inference (confidence intervals and tests) is based on bootstrap. This package allows not only to obtain smooth estimates also based on classical parametric models, as allometric model, one of the most used models in biology frameworks usually used to study the relationship between two biometrical variables. Additionally, we have implemented binning type acceleration techniques.


  • FWDselect package. Author and mantainer of this package (http://cran.r-project.org/web/packages/FWDselect/), an R package that introduces a simple method to select the best model or best subset of variables using different types of responses (gaussian, binary or poisson) and applying it in different contexts (parametric or nonparametric).


  • seq2R package. Author of this R package (http://cran.r-project.org/web/packages/seq2R) to detect compositional changes in genomic sequences. This software is useful for loading .fasta or .gbk files, and for retrieving sequences from GenBank dataset. The package allows to detect differences or asymmetries based on nucleotide composition by using local linear kernel smoothers. Also, it is possible to draw inference about critical points (i. e. maximum or minimum points) related with the derivative curves. Additionally, bootstrap methods have been used for estimating confidence intervals and speed computational techniques (binning techniques) have been implemented in seq2R.

Teaching


University

  • Department of Statistics and Operations Research, University of Vigo, Spain. 2010-2011


  • Department of Statistics and Operations Research, University of Vigo, Spain. 2019/2020


  • Department of Statistics and Operations Research, University of Vigo, Spain. 2020/2021


  • Department of Statistics and Operations Research, University of Vigo, Spain. 2021/2022


  • Department of Statistics and Operations Research, University of Vigo, Spain. 2022/2023


  • Department of Mathematics, Universidad Intercontinental de la Empresa, Spain. 2023/2024


  • Department of Statistics and Operations Research, University of Vigo, Spain. 2023/2024


  • Department of Statistics and Operations Research, University of Vigo, Spain. 2024/2025


Courses