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 state-space representation for irregularly observed time series data generated from a latent continuous-time Ornstein-Uhlenbeck process. Our Bayesian method adopts scientifically motivated hyper-prior distributions and a Metropolis-Hastings 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.
|Depends:||R (≥ 2.2.0), mnormt (≥ 1.5-1)|
|Author:||Hyungsuk Tak, Kaisey Mandel, David A. van Dyk, Vinay L. Kashyap, Xiao-Li Meng, and Aneta Siemiginowska|
|Maintainer:||Hyungsuk Tak <hyungsuk.tak at gmail.com>|
|CRAN checks:||timedelay results|
|Windows binaries:||r-devel: timedelay_1.0.6.zip, r-release: timedelay_1.0.6.zip, r-oldrel: timedelay_1.0.6.zip|
|OS X El Capitan binaries:||r-release: timedelay_1.0.6.tgz|
|OS X Mavericks binaries:||r-oldrel: timedelay_1.0.6.tgz|
|Old sources:||timedelay archive|
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