% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/qshap-package.R
\docType{package}
\name{qshap-package}
\alias{qshap-package}
\title{Calculating Feature-Specific R-Squared Values for Boosting Trees}
\description{
The qshap package computes feature-specific R-squared values using Shapley
decomposition of the total R-squared for boosting trees built in \pkg{xgboost} and \pkg{lightgbm}.
It supports parallel computing.
}
\details{
The package provides fast computation of feature importance through Shapley
values for tree ensemble models. Main functions include:

\itemize{
  \item \code{gazer()}: Create a Q-SHAP explainer from a trained model
  \item \code{rsq()}: Calculate feature-specific R-squared values
  \item \code{loss()}: Calculate feature-specific loss contributions
  \item \code{plot()}: Visualize R-squared values
}

The method uses polynomial-time complexity for Shapley value calculation and
includes built-in support for multi-core processing.
}
\examples{
library(xgboost)
set.seed(42)
n <- 100
p <- 100
X <- matrix(rnorm(n * p), nrow = n, ncol = p)
y <- X[, 1] - X[, 2] + rnorm(n, sd = 0.2)
model <- xgboost(X, y, nrounds = 15, max_depth = 2, verbose = 0)
explainer <- gazer(model)
phi_rsq <- rsq(explainer, X, y)

}
\references{
Zhongli Jiang, Min Zhang, and Dabao Zhang. 2025. Fast 
calculation of feature contributions in boosting trees. In 
Proceedings of the Forty-First Conference on Uncertainty in Artificial Intelligence (UAI '25), Vol. 286. JMLR.org, Article 
82, 1859–1875.
}
\seealso{
Useful links:
\itemize{
  \item \url{https://github.com/catstats/Q-SHAP_R}
  \item Report bugs at \url{https://github.com/catstats/Q-SHAP_R/issues}
}

}
\author{
Steven He, Zhongli Jiang, Min Zhang, Dabao Zhang
}
\keyword{internal}
