timedelay: Time Delay Estimation for Stochastic Time Series of
Gravitationally Lensed Quasars
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. A new functionality is added in version 1.0.9 for estimating the time delay between doubly-lensed light curves observed in two bands. See also Tak et al. (2017) <doi:10.1214/17-AOAS1027>, Tak et al. (2018) <doi:10.1080/10618600.2017.1415911>, Hu and Tak (2020) <doi:10.48550/arXiv.2005.08049>.
| Version: |
1.0.11 |
| Depends: |
R (≥ 3.5.0) |
| Imports: |
MASS (≥ 7.3-51.3), mvtnorm (≥ 1.0-11) |
| Published: |
2020-05-19 |
| DOI: |
10.32614/CRAN.package.timedelay |
| Author: |
Hyungsuk Tak, Kaisey Mandel, David A. van Dyk, Vinay L. Kashyap, Xiao-Li Meng, Aneta Siemiginowska, and Zhirui Hu |
| Maintainer: |
Hyungsuk Tak <hyungsuk.tak at gmail.com> |
| License: |
GPL-2 |
| NeedsCompilation: |
no |
| CRAN checks: |
timedelay results |
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