In ophthalmology, the early detection of keratoconus is still a crucial problem. Placido disk corneal topographers are essential in clinical practice, and many indices for diagnosing corneal irregularities exist. The main goal of this work is to present the R package rPACI, providing several functions to handle and analyze corneal data. This package implements primary indices of corneal irregularity (based on geometrical properties) and compound indices built from the primary ones, either using a generalized linear model or as a Bayesian classifier using a hybrid Bayesian network and performing approximate inference. rPACI aims to make the analysis of corneal data accessible for practitioners and researchers in the field. Moreover, a shiny app was developed to use rPACI in any web browser in a truly user-friendly graphical interface without installing R or writing any R code. It is openly deployed at https://admaldonado.shinyapps.io/rPACI/.
Bayesian, GraphicalModels, HighPerformanceComputing, TeachingStatistics, WebTechnologies
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For attribution, please cite this work as
Ramos-López & Maldonado, "The R Journal: Analysis of Corneal Data in R with the rPACI Package", The R Journal, 2021
BibTeX citation
@article{RJ-2021-099,
author = {Ramos-López, Darío and Maldonado, Ana D.},
title = {The R Journal: Analysis of Corneal Data in R with the rPACI Package},
journal = {The R Journal},
year = {2021},
note = {https://doi.org/10.32614/RJ-2021-099},
doi = {10.32614/RJ-2021-099},
volume = {13},
issue = {2},
issn = {2073-4859},
pages = {321-335}
}