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  "Title": "Computes Credible Intervals for Bayesian Wavelet Shrinkage",
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  "Authors@R": "c(person(\"Stuart\", \"Barber\", role=c(\"aut\"),\nemail=\"stuart@maths.leeds.ac.uk\"),\nperson(\"Guy\", \"Nason\", role=c(\"cre\", \"ctb\"),\nemail=\"g.nason@imperial.ac.uk\"))",
  "Description": "Computes Bayesian wavelet shrinkage credible intervals for\nnonparametric regression. The method uses cumulants to derive\nBayesian credible intervals for wavelet regression estimates.\nThe first four cumulants of the posterior distribution of the\nestimates are expressed in terms of the observed data and\ninteger powers of the mother wavelet functions. These powers\nare closely approximated by linear combinations of wavelet\nscaling functions at an appropriate finer scale. Hence, a\nsuitable modification of the discrete wavelet transform allows\nthe posterior cumulants to be found efficiently for any data\nset. Johnson transformations then yield the credible intervals\nthemselves. Barber, S., Nason, G.P. and Silverman, B.W. (2002)\n<doi:10.1111/1467-9868.00332>.",
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