smashr
Smoothing by Adaptive Shrinkage
Fast, wavelet-based Empirical Bayes shrinkage methods for signal denoising, including smoothing Poisson-distributed data and Gaussian-distributed data with possibly heteroskedastic error. The algorithms implement the methods described Z. Xing, P. Carbonetto & M. Stephens (2021) <https://jmlr.org/papers/v22/19-042.html>.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
1.3-12 |
rolling linux/jammy R-4.5 | smashr_1.3-12.tar.gz |
230.6 KiB |
1.3-12 |
rolling linux/noble R-4.5 | smashr_1.3-12.tar.gz |
232.6 KiB |
1.3-12 |
rolling source/ R- | smashr_1.3-12.tar.gz |
53.0 KiB |
1.3-12 |
latest linux/jammy R-4.5 | smashr_1.3-12.tar.gz |
230.6 KiB |
1.3-12 |
latest linux/noble R-4.5 | smashr_1.3-12.tar.gz |
232.6 KiB |
1.3-12 |
latest source/ R- | smashr_1.3-12.tar.gz |
53.0 KiB |
1.3-12 |
2026-04-26 source/ R- | smashr_1.3-12.tar.gz |
53.0 KiB |
1.3-12 |
2026-04-23 source/ R- | smashr_1.3-12.tar.gz |
53.0 KiB |
1.3-12 |
2026-04-09 windows/windows R-4.5 | smashr_1.3-12.zip |
555.8 KiB |