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. A new functionality is added in version 1.0.9 for estimating the time delay between doublylensed light curves observed in two bands. See also Tak et al. (2017) <doi:10.1214/17AOAS1027>, Tak et al. (2018) <doi:10.1080/10618600.2017.1415911>, Hu and Tak (2020) <arXiv:2005.08049>.
Package details 


Author  Hyungsuk Tak, Kaisey Mandel, David A. van Dyk, Vinay L. Kashyap, XiaoLi Meng, Aneta Siemiginowska, and Zhirui Hu 
Maintainer  Hyungsuk Tak <hyungsuk.tak@gmail.com> 
License  GPL2 
Version  1.0.11 
Package repository  View on CRAN 
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