We provide a toolbox to estimate the time delay between the brightness time series of gravitationally lensed quasar images via Bayesian and profile likelihood approaches. The model is based on a statespace representation for irregularly observed time series data generated from a latent continuoustime OrnsteinUhlenbeck process. Our Bayesian method adopts scientifically motivated hyperprior distributions and a MetropolisHastings within Gibbs sampler, producing posterior samples of the model parameters that include the time delay. A profile likelihood of the time delay is a simple approximation to the marginal posterior distribution of the time delay. Both Bayesian and profile likelihood approaches complement each other, producing almost identical results; the Bayesian way is more principled but the profile likelihood is easier to implement.
Package details 


Author  Hyungsuk Tak, Kaisey Mandel, David A. van Dyk, Vinay L. Kashyap, XiaoLi Meng, and Aneta Siemiginowska 
Maintainer  Hyungsuk Tak <[email protected]> 
License  GPL2 
Version  1.0.8 
Package repository  View on CRAN 
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