Description: Convert odds ratio to relative risk in cohort studies with partial data information.

Description: Convert odds ratio to relative risk in cohort studies with partial data information (Wang (2013) <doi:10.18637/jss.v055.i05>).

Package: orth
Title: Multivariate Logistic Regressions Using Orthogonalized
Residuals.
Version: 1.5.1
Author: Kunthel By, Bahjat F. Qaqish, and John S. Preisser
Maintainer: Kunthel By <kby@bios.unc.edu>
Description: Performs multivariate logistic regressions by way of
orthogonalized residuals. As a special case, the methodology
recasts alternating logistic regressions in a way that is
consistent with standard estimating equation theory. Cluster
diagnostics and observation level diagnostics such as leverage
and Cook's distance are computed based on an approximation.
Exact versions of these diagnostics are computationally
demanding and as of this version, will not be included.
Depends: methods
LazyLoad: yes
License: GPL (>= 2)
Packaged: 2011-01-25 17:17:49 UTC; euclid
Repository: CRAN
Date/Publication: 2011-01-25 18:16:15

Package: orth
Title: Multivariate Logistic Regressions Using Orthogonalized Residuals.
Version: 1.5
Author: Kunthel By, Bahjat F. Qaqish, and John S. Preisser
Maintainer: Kunthel By <kby@bios.unc.edu>
Description: Performs multivariate logistic regressions by way of orthogonalized residuals.
As a special case, the methodology recasts alternating logistic regressions
in a way that is consistent with standard estimating equation theory.
Cluster diagnostics and observation level diagnostics such as
leverage and Cook's distance are computed based on an approximation.
Exact versions of these diagnostics are computationally demanding and
as of this version, will not be included.
Depends: methods
LazyLoad: yes
License: GPL (>= 2)
Packaged: Thu May 15 10:47:39 2008; euclid

Title: An Orthogonality Constrained Optimization Approach for
Author: Ruilin Zhao, Jiyang Zhang, and Ruoqing Zhu
Description: Utilize an orthogonality constrained optimization algorithm of Wen & Yin (2013) <DOI:10.1007/s10107-012-0584-1> to solve a variety of dimension reduction problems in the semiparametric framework, such as Ma & Zhu (2013) <DOI:10.1214/12-AOS1072>, and Sun, Zhu, Wang & Zeng (2017) <arXiv:1704.05046>. It also serves as a general purpose optimization solver for problems with orthogonality constraints.
Imports: Rcpp (>= 0.12.12), survival, pracma, plot3D, rgl