Title: Semi-Parametric Joint Modeling of Longitudinal and Survival Data
applying customized linear scan algorithms, proposed by Li and colleagues (2021) <arXiv:2110.14822>. The time-to-event data is
is estimated using an Expectation Maximization algorithm.
Packaged: 2022-01-10 20:50:27 UTC; shanpengli

Packaged: 2022-02-08 07:31:49 UTC; shanpengli

applying customized linear scan algorithms, proposed by Li and colleagues (2022) <doi:10.1155/2022/1362913>. The time-to-event data is
Imports: Rcpp (>= 1.0.7), survival, dplyr, nlme, mvtnorm
Packaged: 2022-02-09 17:02:50 UTC; shanpengli

Packaged: 2022-02-15 16:30:26 UTC; shanpengli

Packaged: 2022-06-01 16:32:53 UTC; shanpengli

Imports: Rcpp (>= 1.0.7), survival, dplyr, nlme, mvtnorm, Hmisc
Packaged: 2022-08-06 01:56:48 UTC; shanpengli

Title: A Fast Multiple-Kernel Method Based on a Low-Rank Approximation
Author: Rachel Marceau, Wenbin Lu, Michele M. Sale, Bradford B. Worrall, Stephen R. Williams, Fang-Chi Hsu, Jung-Ying Tzeng, and Shannon T. Holloway
Description: A computationally efficient and statistically rigorous fast Kernel Machine method for multi-kernel analysis. The approach is based on a low-rank approximation to the nuisance effect kernel matrices. The algorithm is applicable to continuous, binary, and survival traits and is implemented using the existing single-kernel analysis software 'SKAT' and 'coxKM'. 'coxKM' can be obtained from http://www.hsph.harvard.edu/xlin/software.html.

Author: Rachel Marceau, Wenbin Lu, Michele M. Sale, Bradford B. Worrall,
Stephen R. Williams, Fang-Chi Hsu, Jung-Ying Tzeng, and Shannon T. Holloway
Description: A computationally efficient and statistically rigorous fast
Kernel Machine method for multi-kernel analysis. The approach is based on
a low-rank approximation to the nuisance effect kernel matrices. The
algorithm is applicable to continuous, binary, and survival traits and
is implemented using the existing single-kernel analysis software 'SKAT'
and 'coxKM'. 'coxKM' can be obtained from

Description: Compute labels for a test set according to the k-Nearest Neighbors classification. This is a fast way to do k-Nearest Neighbors classification because the distance matrix -between the features of the observations- is an input to the function rather than being calculated in the function itself every time.