Description
Wasserstein Regression and Inference.
Description
Implementation of the methodologies described in 1) Alexander Petersen, Xi Liu and Afshin A. Divani (2021) <doi:10.1214/20-aos1971>, including global F tests, partial F tests, intrinsic Wasserstein-infinity bands and Wasserstein density bands, and 2) Chao Zhang, Piotr Kokoszka and Alexander Petersen (2022) <doi:10.1111/jtsa.12590>, including estimation, prediction, and inference of the Wasserstein autoregressive models.
README.md
WRI
An R package for the paper “Wasserstein F-tests and confidence bands for the Frechet regression of density response curves”.
Installation
You can install the released version of WRI from CRAN with:
install.packages("WRI")
Example
This is a basic example which shows you how to solve a common problem:
library(WRI)
data(strokeCTdensity)
predictor = strokeCTdensity$predictors
dSup = strokeCTdensity$densitySupport
densityCurves = strokeCTdensity$densityCurve
xpred = predictor[3, ]
res = wass_regress(rightside_formula = ~., Xfit_df = predictor,
Ytype = 'density', Ymat = densityCurves, Sup = dSup)
# compute the density band for the third observation
confidence_Band1 = confidenceBands(res, Xpred_df = xpred, type = 'density')
Main components
strokeCTdensity: clinical, radiological scalar variables and density curves of the hematoma of 393 stroke patientswass_regress: perform Frechet Regression with the Wasserstein Distancewass_R2: compute Wasserstein coefficient of determinationglobalFtest: perform global F test for Wasserstein regressionpartialFtest: perform partial F test for Wasserstein regressionsummary.WRI: provide summary information of Wasserstein regressionconfidenceBands: compute intrinsic confidence bands and density bands.