Efficient Estimation of Discrete-Time Multivariate Frailty Model Using Exact Likelihood Function for Grouped Survival Data

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, ISBN 0-486-41998-3).


lclGWAS v1.0.2 (Release date: 2017-02-20)

  • Fix a NOTE for R_useDynamicSymbols and R_registerRoutines

lclGWAS v1.0.2 (Release date: 2016-11-30)

  • Removed argument 'g' from alphaEst() (unused).
  • Removed argument 'm' from alphaEst(). Function now estimates alpha for all intervals.
  • Replaced 'fam_size' and 'm' argument with family indicator vector in betaEst() and varEst().
  • Revised documentation.

lclGWAS v1.0.1 (Release date: 2016-11-15)

  • Fix bug to prevent error when using clang compiler.

Reference manual

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1.0.3 by Jiaxing Lin, 5 years ago

Browse source code at https://github.com/cran/lclGWAS

Authors: Jiaxing Lin , Alexander Sibley , Tracy Truong , Nancy Cox , Eileen Dolan , Yu Jiang , Janice McCarthy , Andrew Allen , Kouros Owzar , Zhiguo Li

Documentation:   PDF Manual  

GPL (>= 2) license

Imports Rcpp

Suggests knitr

Linking to Rcpp, BH

See at CRAN