smashr: smashr: Smoothing using Adaptive SHrinkage in R

In zrxing/smash: Smoothing by Adaptive Shrinkage in R

smashr: Smoothing using Adaptive SHrinkage in R

Description

This package performs nonparametric regression on univariate Poisson or Gaussian data using multi-scale methods. For the Poisson case, the data x is a vector, with x_j \sim Poi(\mu_j) where the mean vector \mu is to be estimated. For the Gaussian case, the data x are a vector with x_j \sim N(\mu_j, \sigma^2_j). Where the mean vector \mu and variance vector \sigma^2 are to be estimated. The primary assumption is that \mu is spatially structured, so \mu_j - \mu_{j+1} will often be small (that is, roughly, \mu is smooth). Also \sigma is spatially structured in the Gaussian case (or, optionally, \sigma is constant, not depending on j).

Details

The function smash provides a minimal interface to perform simple smoothing. It is actually a wrapper to smash.gaus and smash.poiss which provide more options for advanced use. The only required input is a vector of length 2^J for some integer J. Other options include the possibility of returning the posterior variances, specifying a wavelet basis (default is Haar, which performs well in general due to the fact that smash uses the translation-invariant transform)

Author(s)

Matthew Stephens and Zhengrong Xing

zrxing/smash documentation built on July 12, 2024, 6:31 a.m.