```
# DISCLAIMER: All TI table (e.g., titable) and reconstruction (e.g.,
# reverse.gwave) functions and their respective Rcpp counterparts
# are ported into R from MATLAB functions BMSMShrink and TISumProd
# as part of the BMSM project maintained by Eric Kolaczyk.
# @title Interleave two vectors.
# @param x A vector.
# @param y A vector of the same length as y.
# @return A vector of length twice that of x (or y).
interleave = function (x, y)
as.vector(rbind(x,y))
# @title Shift a vector one unit to the right.
# @param x A vector.
# @return A vector of the same length as that of x.
rshift = function (x) {
L = length(x)
return(c(x[L],x[-L]))
}
# @title Shift a vector one unit to the left.
# @param x A vector.
# @return A vector of the same length as that of x.
lshift = function (x)
c(x[-1],x[1])
# @description Produces two TI tables. One table contains the
# difference between adjacent pairs of data in the same resolution,
# and the other table contains the sum.
# @param sig a signal of length a power of 2
titable = function (sig) {
n = length(sig)
J = log2(n)
dmat = matrix(0, nrow = J + 1, ncol = n)
ddmat = matrix(0, nrow = J + 1, ncol = n)
dmat[1, ] = sig
ddmat[1, ] = sig
for (D in 0:(J - 1)) {
nD = 2^(J - D)
nDo2 = nD/2
twonD = 2 * nD
for (l in 0:(2^D - 1)) {
ind = (l * nD + 1):((l + 1) * nD)
x = dmat[D + 1, ind]
lsumx = x[seq(from = 1, to = nD - 1, by = 2)] +
x[seq(from = 2, to = nD, by = 2)]
ldiffx = x[seq(from = 1, to = nD - 1, by = 2)] -
x[seq(from = 2, to = nD, by = 2)]
rx = rshift(x)
rsumx = rx[seq(from = 1, to = nD - 1, by = 2)] +
rx[seq(from = 2, to = nD, by = 2)]
rdiffx = rx[seq(from = 1, to = nD - 1, by = 2)] -
rx[seq(from = 2, to = nD, by = 2)]
dmat[D + 2, ind] = c(lsumx, rsumx)
ddmat[D + 2, ind] = c(ldiffx, rdiffx)
}
}
return(list(sumtable = dmat, difftable = ddmat))
}
# @description Produces a TI table containing the log difference
# between adjacent pairs of data in the same resolution.
# @param sig A signal of length a power of 2.
# @return A TI table in the form of a matrix.
tirtable = function (sig) {
n = length(sig)
J = log2(n)
dmat = matrix(0, nrow = J + 1, ncol = n)
ddmat = matrix(0, nrow = J + 1, ncol = n)
dmat[1, ] = sig
ddmat[1, ] = sig
for (D in 0:(J - 1)) {
nD = 2^(J - D)
nDo2 = nD/2
twonD = 2 * nD
for (l in 0:(2^D - 1)) {
ind = (l * nD + 1):((l + 1) * nD)
x = dmat[D + 1, ind]
lsumx = x[seq(from = 1, to = nD - 1, by = 2)] +
x[seq(from = 2, to = nD, by = 2)]
ldiffx = log(x[seq(from = 1, to = nD - 1, by = 2)]) -
log(x[seq(from = 2, to = nD, by = 2)])
rx = rshift(x)
rsumx = rx[seq(from = 1, to = nD - 1, by = 2)] +
rx[seq(from = 2, to = nD, by = 2)]
rdiffx = log(rx[seq(from = 1, to = nD - 1, by = 2)]) -
log(rx[seq(from = 2, to = nD, by = 2)])
dmat[D + 2, ind] = c(lsumx, rsumx)
ddmat[D + 2, ind] = c(ldiffx, rdiffx)
}
}
return(ddmat)
}
# @title Reverse wavelet transform a set of wavelet coefficients in
# TItable format for Gaussian data.
# @param lp A J by n matrix of estimated wavelet coefficients.
# @param lq A J by n matrix of complementary wavelet coefficients.
# @param est An n-vector. Usually a constant vector with each element
# equal to the estimated total mean.
# @return Reconstructed signal in the original data space.
reverse.gwave = function (est, lp, lq = NULL) {
if (is.null(lq))
lq = -lp
if (length(est) == 1)
est = rep(est, ncol(lp))
J = nrow(lp)
for (D in J:1) {
nD = 2^(J - D + 1)
nDo2 = nD/2
for (l in 0:(2^(D - 1) - 1)) {
ind = (l * nD + 1):((l + 1) * nD)
estvec = est[ind]/2
lpvec = lp[D, ind]
lqvec = lq[D, ind]
estl = estvec[1:nDo2]
lpl = lpvec[1:nDo2]
lql = lqvec[1:nDo2]
nestl = interleave(estl + lpl, estl + lql)
estr = estvec[(nDo2 + 1):nD]
lpr = lpvec[(nDo2 + 1):nD]
lqr = lqvec[(nDo2 + 1):nD]
nestr = interleave(estr + lpr, estr + lqr)
nestr = lshift(nestr)
est[ind] = 0.5 * (nestl + nestr)
}
}
return(est)
}
# @description Reverse wavelet transform a set of posterior variances
# for wavelet coefficients in TItable format, for Gaussian data.
# @param lp A J by n matrix of estimated variances.
# @param lq A J by n matrix of complementary variances (=lp).
# @param est Nn n-vector. Usually 0.
# @return Reconstructed posterior variance in the original data space.
reverse.gvwave = function (est, lp, lq = NULL) {
if (is.null(lq))
lq = -lp
if (length(est) == 1)
est = rep(est, ncol(lp))
J = nrow(lp)
for (D in J:1) {
nD = 2^(J - D + 1)
nDo2 = nD/2
for (l in 0:(2^(D - 1) - 1)) {
ind = (l * nD + 1):((l + 1) * nD)
estvec = est[ind]/4
lpvec = lp[D, ind]
lqvec = lq[D, ind]
estl = estvec[1:nDo2]
lpl = lpvec[1:nDo2]
lql = lqvec[1:nDo2]
nestl = interleave(estl + lpl, estl + lql)
estr = estvec[(nDo2 + 1):nD]
lpr = lpvec[(nDo2 + 1):nD]
lqr = lqvec[(nDo2 + 1):nD]
nestr = interleave(estr + lpr, estr + lqr)
nestr = lshift(nestr)
est[ind] = 0.5 * (nestl + nestr)
}
}
return(est)
}
```

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