Overview

When developing new statistical methods, I usually create R packages that make it easier to apply them. When this is not possible due to time or funding constraints, I upload the R code I used on my github page.

My involvement in the R community so far can be summarized as follows:

Below you can find more details about some of these contributions.

R package pencal

pencal is an abbreviation that stands for Penalized Regression Calibration (PRC), a method that can be used to predict a survival outcome using predictors that are measured repeatedly over time (“longitudinal data”). PRC is implemented in the R package pencal, available from CRAN.

PRC was first described in Signorelli et al. (2021). Penalized regression calibration: a method for the prediction of survival outcomes using complex longitudinal and high-dimensional data. Statistics in Medicine, 40 (27), 6178-6196. A detailed description of how to use PRC for dynamic prediction can be found in Signorelli (2024). pencal: an R Package for the Dynamic Prediction of Survival with Many Longitudinal Predictors. The R Journal, 16 (2), 134-153.

To find out more about PRC and pencal:

R package neat

NEAT is an acronym that stands for Network Enrichment Analysis Test, a test that can be used to assess relations between sets of nodes (typically genes) in a network.

The method is described in Signorelli, Vinciotti and Wit (2016). NEAT: an efficient network enrichment analysis test. BMC Bioinformatics, 17:352, and it is implemented in the R package neat, available from CRAN.

If you want to learn more about NEAT, you can:

News: since November 2020, the NEAT test can be computed also using the Bioconductor package EnrichmentBrowser.

R package ptmixed

ptmixed is an abbreviation that stands for Poisson-Tweedie generalized linear mixed model, a mixed-effects model that has been developed to flexibly model longitudinal count data that feature overdispersion, zero inflation and/or heavy tails.

The model is described in Signorelli, M., Spitali, P., Tsonaka, R. (2021). Poisson-Tweedie mixed-effects model: a flexible approach for the analysis of longitudinal RNA-seq data. Statistical Modelling, 21 (6), 520-545, and it is implemented in the R package ptmixed, available from CRAN.

To find out more about ptmixed:

R package success

success (short for: Survival Control Charts Estimation Software) is an R package that implements methods to compute and visualize the following charts for quality control of survival outcomes:

  1. funnel plot;
  2. Bernoulli CUSUM;
  3. Biswas & Kalbfleisch’s CUSUM (BK-CUSUM);
  4. Continuous Time Generalized Rapid Response CUSUM (CGR-CUSUM).

Note that the funnel plot and Bernoulli CUSUM require a dychotomization of the survival outcome into a binary outcome, whereas the CGR-CUSUM and BK-CUSUM can properly handle the survival outcome.

The R package is described in: Gomon, D., Fiocco, M., Putter, H., Signorelli, M. (2023). SUrvival Control Chart EStimation Software in R: the success package. The R Journal, 15 (4), 270-291.

To find out more about success:

European R Users Meeting 2020 conference (e-Rum2020)

I was one of the organizers of the European R Users Meeting 2020 conference (e-Rum2020), both as member of the organizing committee and as responsible of the promotion of the conference.

e-Rum2020 was originally planned as a physical event to be held in Milan in May 2020. However, due to the COVID19 pandemic we decided to turn the event into a free virtual conference - the very first fully virtual R conference ever!

If you would like to know more about e-Rum2020, you can: