\HeaderA{hf.bt}{hf.bt}{hf.bt}
\keyword{manip}{hf.bt}
\begin{Description}\relax
Denoises a Gaussian contaminated vector using a version of the wavelet-based ``greedy tree" algorithm by Baraniuk, see the reference in the thesis.
\end{Description}
\begin{Usage}
\begin{verbatim}
hf.bt(x, filter.number = 1, family = "DaubExPhase", min.level = 3, noise.level = NULL) 
\end{verbatim}
\end{Usage}
\begin{Arguments}
\begin{ldescription}
\item[\code{x}] The noisy vector, its length must be a power of 2.
\item[\code{filter.number}] The filter number of the analysing wavelet. Can be set to 1, 2, ..., 10 for \code{family == "DaubExPhase"}, or to 4, 5, ..., 10 for \code{family == "DaubLeAsymm"}.
\item[\code{family}] The family of wavelet bases from which the wavelet \code{filter.number} is chosen. Can be set to "DaubExPhase" or "DaubLeAsymm".
\item[\code{min.level}] The minimum level thresholded.
\item[\code{noise.level}] Standard deviation of the noise, can be set to a positive number or NULL; in the latter case it will be estimated using MAD.
\end{ldescription}
\end{Arguments}
\begin{Value}
Returns: 
item{est}{Denoised version of \code{x}.}
\end{Value}
\begin{Author}\relax
Piotr Fryzlewicz
\end{Author}

