Generalized eigenvalues and eigenvectors
use QZ decomposition (generalized Schur decomposition).
The decomposition needs an N-by-N non-symmetric
matrix A or paired matrices (A,B) with eigenvalues reordering
mechanism. The decomposition functions are mainly based Fortran
subroutines in complex*16 and double precision of LAPACK
library (version 3.10.0 or later).

Approximation to the Survival Functions of Quadratic Forms of
Gaussian Variables
Calculates the right-tail probability of quadratic forms of Gaussian variables using the skewness-kurtosis ratio matching method, modified Liu-Tang-Zhang method and Satterthwaite-Welch method. The technical details can be found in Hong Zhang, Judong Shen and Zheyang Wu (2020) <arXiv:2005.00905>.

Quantile Autoregressive Distributed Lag Model
Fit the quantile autoregressive distributed lag model proposed by ( Cho et al. (2015) <doi:10.1016/j.jeconom.2015.05.003>).

c(person("Gianluca", "Sottile", role=c("aut", "cre"), email = "gianluca.sottile@unipa.it"),
person("Paolo", "Frumento", role=c("aut")))
Gianluca Sottile [aut, cre],
Paolo Frumento [aut]
Quantile-based estimators (Q-estimators) can be used to fit any parametric distribution, using its quantile function. Q-estimators are usually more robust than standard maximum likelihood estimators. The method is described in: Sottile G. and Frumento P. (2022). Robust estimation and regression with parametric quantile functions. <doi:10.1016/j.csda.2022.107471>.
pch, survival, matrixStats, methods, utils

Quantile-Based Predictors for Survival Outcome
Misung Yi [aut, cph],
Tingting Zhan [aut, cre, cph] (<https://orcid.org/0000-0001-9971-4844>),
Inna Chervoneva [aut, cph]
Select optimal quantile-based predictors for
survival outcome, to include the means to handle
dichotomizing the quantiles, mean-signal-intensity or
other continuous markers, and to obtain estimates for
dichotomized predictors by Bootstrap-based bias
correction.
c(person(given = 'Misung', family = 'Yi', role =
c('aut', 'cph'), email = 'Misung.Yi@jefferson.edu'),
person(given = 'Tingting', family = 'Zhan', role =
c('aut', 'cre', 'cph'), email =
'tingtingzhan@gmail.com', comment = c(ORCID =
'0000-0001-9971-4844')), person(given = 'Inna', family
= 'Chervoneva', role = c('aut', 'cph'), email =
'Inna.Chervoneva@jefferson.edu'))
stats, boot, rpart, survival, matrixStats
'0info.R' 'pkg_data_doc.R' 'optQp.R' 'sampleQp.R'
'BBC_dichotom.R'