Smoothing by Adaptive Shrinkage
Model fitting using weighted least squares or a GLM approach.
Reflect and extend a vector.
Reverse wavelet transform a set of probabilities in TItable format for...
Estimate homoskedastic standard deviation from nonparamatric regressio...
Estimate underlying mean function from noisy Gaussian data.
Estimate the underlying intensity for Poisson counts.
Estimate the underlying mean or intensity function from Gaussian or Po...
smashr: Smoothing using Adaptive SHrinkage in R
TI thresholding with heteroskedastic errors.
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>.