README.md

In labuzzetta/conformr: Conformal Prediction Research R Package

conformr

Conformal Prediction Research R Package based on the work of Charles Labuzzetta and Yingchao Zhou

Labuzzetta, C., Zhou, Y., and Zhu, Z. (2022) Conformal prediction under covariate shift: Nearest neighbor subsampling to achieve approximate exchangeability. Submitted to NeurIPS 2022.

Install this R Package with the following command:

devtools::install_github("https://github.com/labuzzetta/conformr.git", force = TRUE, build_vignettes = TRUE, upgrade = FALSE)

You may view the code used to run the experiments described in Labuzzetta, et al. (2022) by running:

vignette("labuzzetta_2022_neurips", package="conformr")

Several datasets in this package are subset from data in the UCI Machine Learning Repository. We would like to acknowledge their contributions.

Dheeru Dua and Casey Graff. UCI machine learning repository, 2017. URL http://archive.ics.uci.edu/ml.

Covtype data set: Copyright Jock A. Blackard and Colorado State University.

Jock Blackard and Denis Dean. Comparative accuracies of artificial neural networks and discriminant analysis in predicting forest cover types from cartographic variables. Computers and Electronics in Agriculture, 24:131–151, 1999.

Crop mapping using fused optical-radar data set: Copyright Iman Khosravi and University of Tehran

Iman Khosravi and Seyed Kazem Alavipanah. A random forest-based framework for crop mapping using temporal, spectral, textural and polarimetric observations. International Journal of Remote Sensing, 40(18):7221–7251, 2019.

Iman Khosravi, Abdolreza Safari, and Saeid Homayouni. MSMD: maximum separability and minimum dependency feature selection for cropland classification from optical and radar data. International Journal of Remote Sensing, 39(8):2159–2176, 2018.

labuzzetta/conformr documentation built on May 13, 2022, 12:50 a.m.