lclGWAS-package: Efficient Estimation of Multivariate Frailty Model Using...
In lclGWAS: Efficient Estimation of Discrete-Time Multivariate Frailty Model Using Exact Likelihood Function for Grouped Survival Data
Description
Details
Author(s)
References
See Also
Description
The core of this Rcpp based package is several functions to estimate the baseline hazard, frailty variance, and fixed effect parameter for a discrete-time shared frailty model with random effects. The functions are designed to analyze grouped time-to-event data accounting for family structure of related individuals (i.e., trios). The core functions include two processes: (1) evaluate the multivariable integration to compute the exact proportional hazards model based likelihood and (2) estimate the desired parameters using maximum likelihood estimation. The integration is evaluated by the Cuhre algorithm from the Cuba library (Hahn, T., Cuba-a library for multidimensional numerical integration, Comput. Phys. Commun. 168, 2005, 78-95), and the source files of the Cuhre function are included in this package. The maximization process is carried out using Brent's algorithm, with the C++ code file from John Burkardt and John Denker (Brent, R.,Algorithms for Minimization without Derivatives, Dover, 2002).
License: GPL (>= 2)
Details
Package: lclGWAS
Type: Package
Version: 1.0.2
Date: 2017-02-20
License: GPL-3
Please refer to the individual function documentation or the included vignette for more information. The package vignette serves as a tutorial for using this package.
Author(s)
Jiaxing Lin, Alexander Sibley, Tracy Truong, Nancy Cox, Eileen Dolan, Yu Jiang, Janice McCarthy, Andrew Allen, Kouros Owzar, Zhiguo Li
Maintainer: Jiaxing Lin <[email protected]>
References
Ripatti, S. and Palmgren, J., Estimation of Multivariate Frailty Models Using Penalized Partial Likelihood. Biometrics, 56, 2000, 1016-1022.
Hahn, T., Cuba-a library for multidimensional numerical integration, Computer Physics Communications, 168, 2005, 78-95.
Brent, R., Algorithms for Minimization without Derivatives, Dover, 2002.
See Also
lclGWAS documentation built on May 2, 2019, 2:46 p.m.
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Description Details Author(s) References See Also
Description
The core of this Rcpp based package is several functions to estimate the baseline hazard, frailty variance, and fixed effect parameter for a discrete-time shared frailty model with random effects. The functions are designed to analyze grouped time-to-event data accounting for family structure of related individuals (i.e., trios). The core functions include two processes: (1) evaluate the multivariable integration to compute the exact proportional hazards model based likelihood and (2) estimate the desired parameters using maximum likelihood estimation. The integration is evaluated by the Cuhre algorithm from the Cuba library (Hahn, T., Cuba-a library for multidimensional numerical integration, Comput. Phys. Commun. 168, 2005, 78-95), and the source files of the Cuhre function are included in this package. The maximization process is carried out using Brent's algorithm, with the C++ code file from John Burkardt and John Denker (Brent, R.,Algorithms for Minimization without Derivatives, Dover, 2002).
License: GPL (>= 2)
Details
| Package: | lclGWAS |
| Type: | Package |
| Version: | 1.0.2 |
| Date: | 2017-02-20 |
| License: | GPL-3 |
Please refer to the individual function documentation or the included vignette for more information. The package vignette serves as a tutorial for using this package.
Author(s)
Jiaxing Lin, Alexander Sibley, Tracy Truong, Nancy Cox, Eileen Dolan, Yu Jiang, Janice McCarthy, Andrew Allen, Kouros Owzar, Zhiguo Li Maintainer: Jiaxing Lin <[email protected]>
References
Ripatti, S. and Palmgren, J., Estimation of Multivariate Frailty Models Using Penalized Partial Likelihood. Biometrics, 56, 2000, 1016-1022.
Hahn, T., Cuba-a library for multidimensional numerical integration, Computer Physics Communications, 168, 2005, 78-95.
Brent, R., Algorithms for Minimization without Derivatives, Dover, 2002.
See Also
R Package Documentation
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