New: R package for creating online exams

Check out my new package examinr for creating, administering, and grading online exams. All by writing a single R markdown file!

Exams are delivered as Shiny app and allow you to have a randomized version to each learner. The package has built-in support for automatic grading as well as a user interface for manual grading and to return individualized feedback to your learners.

Learn more

About

I obtained a PhD in Statistics in 2020 from the University of British Columbia where I was advised by Dr. Gabriela Cohen Freue.

Before that, I obtained a Master of Science in Statistics from the Vienna University of Technology under supervision by Prof. Peter Filzmoser.

My research agenda comprises methodological and computational aspects of robust estimation in high-dimensional problems as well as their application to Biomedical Sciences. I am working on statistical methods with reliable performance under presence of adverse contamination anywhere in the data.

For regression problems, for instance, I work on estimators which are resilient to outliers in the response but also to unusual values in the (potentially) explanatory variables. If not handled appropriately, unusual values in the explanatory variables can have a much more detrimental affect on the analysis than outliers in the response alone.

Teaching

University of British Columbia

Winter 2019/20 Term 2: STAT 305 – Introduction to Statistical Inference

Publications

2019

Cohen Freue GV, Kepplinger D*, Salibián-Barrera M, Smucler E. Robust elastic net estimators for variable selection and identification of proteomic biomarkers. Annals of Applied Statistics.2019;13(4). online pdf (* in alphabetical order)

2017

Kepplinger D, Takhar M, Sasaki M, Hollander Z, Smith D, McManus B, et al. PGCA: An algorithm to link protein groups created from MS/MS data. PLOS ONE. 2017;12(5). online
Kepplinger D, Filzmoser P, Varmuza K. Variable selection with genetic algorithms using repeated cross- validation of PLS regression models as fitness measure. preprint

2013

Kepplinger D, Templ M, Upadhyaya S. Analysis of energy intensity in manufacturing industry using mixed-effects models. Energy. 2013;59:754 – 763. online

A complete list of publications, conference presentations, and other research experience can be found in my CV.

Code

I am maintaining several stable R packages on CRAN and Bioconductor as well as a few experimental software tools available on my GitHub and GitLab pages.

examinr

Create online exams from R markdown documents.

Write online exams as R markdown documents and publish them as shiny app. Allows for randomized exams, different question types (including R coding questions), and grading of submissions.

More info

pense

Implementation of penalized adaptive Elastic Net S/MM-Estimators of Regression

Robust penalized adaptive elastic net S- and MM-estimators for linear regression.

More info View on CRAN

pyinit

Peña-Yohai Initial Estimator for Robust S-Regression

Fast and deterministic procedure to compute initial estimates for robust S-estimators of regression using as described in Peña-Yohai (1999).

View on Github View on CRAN

gaselect

Genetic algorithms for variable selection.

Multi-threaded genetic algorithms applicable to a wide range of variable selection methods, but particularly suited for Partial Least Squared Regression.

View on CRAN

complmrob

Robust Linear Regression with Compositional Covariates

Methods for robustly fitting regression models where the explanatory variables are compositional. Includes bootstrap methods for classical robust regression and compositional robust regression.

View on CRAN

nsoptim

Algorithms for non-smooth optimization

C++ template library, wrapped in an R package, providing modern and fast algorithms for optimizing non-smooth functions (e.g., L₁ regularized objective functions).

View on GitLab

PGCA

Link Protein Groups Created from MS/MS Data

Protein Group Code Algorithm (PGCA) is a computationally inexpensive algorithm to merge protein summaries from multiple experimental quantitative proteomics data.

View on Bioconductor