@c DO NOT EDIT!  Generated automatically by munge-texi.

@c Copyright (C) 1996, 1997 John W. Eaton
@c This is part of the Octave manual.
@c For copying conditions, see the file gpl.texi.

@node Signal Processing
@chapter Signal Processing

I hope that someday Octave will include more signal processing
functions.  If you would like to help improve Octave in this area,
please contact @email{bug-octave@@bevo.che.wisc.edu}.

@anchor{doc-detrend}
@deftypefn {Function File} {} detrend (@var{x}, @var{p})
If @var{x} is a vector, @code{detrend (@var{x}, @var{p})} removes the
best fit of a polynomial of order @var{p} from the data @var{x}.

If @var{x} is a matrix, @code{detrend (@var{x}, @var{p})} does the same
for each column in @var{x}.

The second argument is optional.  If it is not specified, a value of 1
is assumed.  This corresponds to removing a linear trend.
@end deftypefn


@anchor{doc-fft}
@deftypefn {Loadable Function} {} fft (@var{a}, @var{n}, @var{dim})
Compute the FFT of @var{a} using subroutines from
@sc{Fftw}. The FFT is calculated along the first non-singleton dimension of the
array. Thus if @var{a} is a matrix, @code{fft (@var{a})} computes the
FFT for each column of @var{a}.

If called with two arguments, @var{n} is expected to be an integer
specifying the number of elements of @var{a} to use, or an empty
matrix to specify that its value should be ignored. If @var{n} is
larger than the dimension along which the FFT is calculated, then
@var{a} is resized and padded with zeros. Otherwise, if@var{n} is
smaller than the dimension along which the FFT is calculated, then
@var{a} is truncated.

If called with three agruments, @var{dim} is an integer specifying the
dimension of the matrix along which the FFT is performed
@end deftypefn
@seealso {ifft, fft2, fftn, fftw_wisdom}


@anchor{doc-ifft}
@deftypefn {Loadable Function} {} ifft (@var{a}, @var{n}, @var{dim})
Compute the inverse FFT of @var{a} using subroutines from
@sc{Fftw}. The inverse FFT is calculated along the first non-singleton dimension
of the array. Thus if @var{a} is a matrix, @code{fft (@var{a})} computes
the inverse FFT for each column of @var{a}.

If called with two arguments, @var{n} is expected to be an integer
specifying the number of elements of @var{a} to use, or an empty
matrix to specify that its value should be ignored. If @var{n} is
larger than the dimension along which the inverse FFT is calculated, then
@var{a} is resized and padded with zeros. Otherwise, if@var{n} is
smaller than the dimension along which the inverse FFT is calculated,
then @var{a} is truncated.

If called with three agruments, @var{dim} is an integer specifying the
dimension of the matrix along which the inverse FFT is performed
@end deftypefn
@seealso {fft, ifft2, ifftn, fftw_wisdom}


@anchor{doc-fft2}
@deftypefn {Loadable Function} {} fft2 (@var{a}, @var{n}, @var{m})
Compute the two dimensional FFT of @var{a} using subroutines from
@sc{Fftw}. The optional arguments @var{n} and @var{m} may be used specify the
number of rows and columns of @var{a} to use.  If either of these is
larger than the size of @var{a}, @var{a} is resized and padded with
zeros.

If @var{a} is a multi-dimensional matrix, each two-dimensional sub-matrix
of @var{a} is treated seperately
@end deftypefn
@seealso {ifft2, fft, fftn fftw_wisdom}


@anchor{doc-ifft2}
@deftypefn {Loadable Function} {} fft2 (@var{a}, @var{n}, @var{m})
Compute the inverse two dimensional FFT of @var{a} using subroutines from
@sc{Fftw}. The optional arguments @var{n} and @var{m} may be used specify the
number of rows and columns of @var{a} to use.  If either of these is
larger than the size of @var{a}, @var{a} is resized and padded with
zeros.

If @var{a} is a multi-dimensional matrix, each two-dimensional sub-matrix
of @var{a} is treated seperately
@end deftypefn
@seealso {fft2, ifft, ifftn, fftw_wisdom}


@anchor{doc-fftn}
@deftypefn {Loadable Function} {} fftn (@var{a}, @var{size})
Compute the N dimensional FFT of @var{a} using subroutines from
@sc{Fftw}. The optional vector argument @var{size} may be used specify the
dimensions of the array to be used. If an element of @var{size} is
smaller than the corresponding dimension, then the dimension is
truncated prior to performing the FFT. Otherwise if an element
of @var{size} is larger than the corresponding dimension @var{a}
is resized and padded with zeros.
@end deftypefn
@seealso {ifftn, fft, fft2, fftw_wisdom}


@anchor{doc-ifftn}
@deftypefn {Loadable Function} {} ifftn (@var{a}, @var{size})
Compute the invesre N dimensional FFT of @var{a} using subroutines from
@sc{Fftw}. The optional vector argument @var{size} may be used specify the
dimensions of the array to be used. If an element of @var{size} is
smaller than the corresponding dimension, then the dimension is
truncated prior to performing the inverse FFT. Otherwise if an element
of @var{size} is larger than the corresponding dimension @var{a}
is resized and padded with zeros.
@end deftypefn
@seealso {fftn, ifft, ifft2, fftw_wisdom}


@anchor{doc-fftconv}
@deftypefn {Function File} {} fftconv (@var{a}, @var{b}, @var{n})
Return the convolution of the vectors @var{a} and @var{b}, as a vector
with length equal to the @code{length (a) + length (b) - 1}.  If @var{a}
and @var{b} are the coefficient vectors of two polynomials, the returned
value is the coefficient vector of the product polynomial.

The computation uses the FFT by calling the function @code{fftfilt}.  If
the optional argument @var{n} is specified, an N-point FFT is used.
@end deftypefn


@anchor{doc-fftfilt}
@deftypefn {Function File} {} fftfilt (@var{b}, @var{x}, @var{n})

With two arguments, @code{fftfilt} filters @var{x} with the FIR filter
@var{b} using the FFT.

Given the optional third argument, @var{n}, @code{fftfilt} uses the
overlap-add method to filter @var{x} with @var{b} using an N-point FFT.

If @var{x} is a matrix, filter each column of the matrix.
@end deftypefn


@anchor{doc-filter}
@deftypefn {Loadable Function} {y =} filter (@var{b}, @var{a}, @var{x})
@deftypefnx {Loadable Function} {[@var{y}, @var{sf}] =} filter (@var{b}, @var{a}, @var{x}, @var{si})
Return the solution to the following linear, time-invariant difference
equation:
@iftex
@tex
$$
\sum_{k=0}^N a_{k+1} y_{n-k} = \sum_{k=0}^M b_{k+1} x_{n-k}, \qquad
 1 \le n \le P
$$
@end tex
@end iftex
@ifinfo

@smallexample
   N                   M
  SUM a(k+1) y(n-k) = SUM b(k+1) x(n-k)      for 1<=n<=length(x)
  k=0                 k=0
@end smallexample
@end ifinfo

@noindent
where
@ifinfo
 N=length(a)-1 and M=length(b)-1.
@end ifinfo
@iftex
@tex
 $a \in \Re^{N-1}$, $b \in \Re^{M-1}$, and $x \in \Re^P$.
@end tex
@end iftex
An equivalent form of this equation is:
@iftex
@tex
$$
y_n = -\sum_{k=1}^N c_{k+1} y_{n-k} + \sum_{k=0}^M d_{k+1} x_{n-k}, \qquad
 1 \le n \le P
$$
@end tex
@end iftex
@ifinfo

@smallexample
            N                   M
  y(n) = - SUM c(k+1) y(n-k) + SUM d(k+1) x(n-k)  for 1<=n<=length(x)
           k=1                 k=0
@end smallexample
@end ifinfo

@noindent
where
@ifinfo
 c = a/a(1) and d = b/a(1).
@end ifinfo
@iftex
@tex
$c = a/a_1$ and $d = b/a_1$.
@end tex
@end iftex

If the fourth argument @var{si} is provided, it is taken as the
initial state of the system and the final state is returned as
@var{sf}.  The state vector is a column vector whose length is
equal to the length of the longest coefficient vector minus one.
If @var{si} is not supplied, the initial state vector is set to all
zeros.

In terms of the z-transform, y is the result of passing the discrete-
time signal x through a system characterized by the following rational
system function:
@iftex
@tex
$$
H(z) = {\displaystyle\sum_{k=0}^M d_{k+1} z^{-k}
        \over 1 + \displaystyle\sum_{k+1}^N c_{k+1} z^{-k}}
$$
@end tex
@end iftex
@ifinfo

@example
             M
            SUM d(k+1) z^(-k)
            k=0
  H(z) = ----------------------
               N
          1 + SUM c(k+1) z(-k)
              k=1
@end example
@end ifinfo
@end deftypefn


@anchor{doc-freqz}
@deftypefn {Function File} {[@var{h}, @var{w}] =} freqz (@var{b}, @var{a}, @var{n}, "whole")
Return the complex frequency response @var{h} of the rational IIR filter
whose numerator and denominator coefficients are @var{b} and @var{a},
respectively.  The response is evaluated at @var{n} angular frequencies
between 0 and
@ifinfo
 2*pi.
@end ifinfo
@iftex
@tex
 $2\pi$.
@end tex
@end iftex

@noindent
The output value @var{w} is a vector of the frequencies.

If the fourth argument is omitted, the response is evaluated at
frequencies between 0 and
@ifinfo
 pi.
@end ifinfo
@iftex
@tex
 $\pi$.
@end tex
@end iftex

If @var{n} is omitted, a value of 512 is assumed.

If @var{a} is omitted, the denominator is assumed to be 1 (this
corresponds to a simple FIR filter).

For fastest computation, @var{n} should factor into a small number of
small primes.

@deftypefnx {Function File} {@var{h} =} freqz (@var{b}, @var{a}, @var{w})
Evaluate the response at the specific frequencies in the vector @var{w}.
The values for @var{w} are measured in radians.

@deftypefnx {Function File} {[@dots{}] =} freqz (@dots{}, @var{Fs})
Return frequencies in Hz instead of radians assuming a sampling rate
@var{Fs}.  If you are evaluating the response at specific frequencies 
@var{w}, those frequencies should be requested in Hz rather than radians.

@deftypefnx {Function File} {} freqz (@dots{})
Plot the pass band, stop band and phase response of @var{h} rather
than returning them.
@end deftypefn


@anchor{doc-freqz_plot}
@deftypefn {Function File} freqz_plot (@var{w}, @var{h})
Plot the pass band, stop band and phase response of @var{h}.
@end deftypefn


@anchor{doc-sinc}
@deftypefn {Function File} {} sinc (@var{x})
Return
@iftex
@tex
$ \sin (\pi x)/(\pi x)$.
@end tex
@end iftex
@ifinfo
 sin(pi*x)/(pi*x).
@end ifinfo
@end deftypefn


@anchor{doc-unwrap}
@deftypefn {Function File} {@var{b} =} unwrap (@var{a}, @var{tol}, @var{dim})

Unwrap radian phases by adding multiples of 2*pi as appropriate to
remove jumps greater than @var{tol}.  @var{tol} defaults to pi.

Unwrap will unwrap along the columns of @var{a} unless the row
dimension of @var{a} is 1 or @var{dim} is given with a
value of 1, when it will unwrap along the row(s).
@end deftypefn


@c XXX FIXME XXX -- someone needs to organize these...

@anchor{doc-arch_fit}
@deftypefn {Function File} {[@var{a}, @var{b}] =} arch_fit (@var{y}, @var{x}, @var{p}, @var{iter}, @var{gamma}, @var{a0}, @var{b0})
Fit an ARCH regression model to the time series @var{y} using the
scoring algorithm in Engle's original ARCH paper.  The model is

@example
y(t) = b(1) * x(t,1) + ... + b(k) * x(t,k) + e(t),
h(t) = a(1) + a(2) * e(t-1)^2 + ... + a(p+1) * e(t-p)^2
@end example

@noindent
in which @math{e(t)} is @math{N(0, h(t))}, given a time-series vector
@var{y} up to time @math{t-1} and a matrix of (ordinary) regressors
@var{x} up to @math{t}.  The order of the regression of the residual
variance is specified by @var{p}.

If invoked as @code{arch_fit (@var{y}, @var{k}, @var{p})} with a
positive integer @var{k}, fit an ARCH(@var{k}, @var{p}) process,
i.e., do the above with the @math{t}-th row of @var{x} given by

@example
[1, y(t-1), ..., y(t-k)]
@end example

Optionally, one can specify the number of iterations @var{iter}, the
updating factor @var{gamma}, and initial values @math{a0} and
@math{b0} for the scoring algorithm.
@end deftypefn


@anchor{doc-arch_rnd}
@deftypefn {Function File} {} arch_rnd (@var{a}, @var{b}, @var{t})
Simulate an ARCH sequence of length @var{t} with AR
coefficients @var{b} and CH coefficients @var{a}.  I.e., the result
@math{y(t)} follows the model

@example
y(t) = b(1) + b(2) * y(t-1) + @dots{} + b(lb) * y(t-lb+1) + e(t),
@end example

@noindent
where @math{e(t)}, given @var{y} up to time @math{t-1}, is
@math{N(0, h(t))}, with

@example
h(t) = a(1) + a(2) * e(t-1)^2 + @dots{} + a(la) * e(t-la+1)^2
@end example
@end deftypefn


@anchor{doc-arch_test}
@deftypefn {Function File} {[@var{pval}, @var{lm}] =} arch_test (@var{y}, @var{x}, @var{p})
For a linear regression model

@example
y = x * b + e
@end example

@noindent
perform a Lagrange Multiplier (LM) test of the null hypothesis of no
conditional heteroscedascity against the alternative of CH(@var{p}).

I.e., the model is

@example
y(t) = b(1) * x(t,1) + @dots{} + b(k) * x(t,k) + e(t),
@end example

@noindent
given @var{y} up to @math{t-1} and @var{x} up to @math{t},
@math{e}(t) is @math{N(0, h(t))} with

@example
h(t) = v + a(1) * e(t-1)^2 + @dots{} + a(p) * e(t-p)^2,
@end example

@noindent
and the null is @math{a(1)} == @dots{} == @math{a(p)} == 0.

If the second argument is a scalar integer, @math{k}, perform the same
test in a linear autoregression model of order @math{k}, i.e., with

@example
[1, y(t-1), @dots{}, y(t-@var{k})]
@end example

@noindent
as the @math{t}-th row of @var{x}.

Under the null, LM approximately has a chisquare distribution with
@var{p} degrees of freedom and @var{pval} is the @math{p}-value (1
minus the CDF of this distribution at LM) of the test.

If no output argument is given, the @math{p}-value is displayed.
@end deftypefn


@anchor{doc-arma_rnd}
@deftypefn {Function File} {} arma_rnd (@var{a}, @var{b}, @var{v}, @var{t}, @var{n})
Return a simulation of the ARMA model

@example
x(n) = a(1) * x(n-1) + ... + a(k) * x(n-k)
     + e(n) + b(1) * e(n-1) + ... + b(l) * e(n-l)
@end example

@noindent
in which @var{k} is the length of vector @var{a}, @var{l} is the
length of vector @var{b} and @var{e} is gaussian white noise with
variance @var{v}.  The function returns a vector of length @var{t}.

The optional parameter @var{n} gives the number of dummy
@var{x}(@var{i}) used for initialization, i.e., a sequence of length
@var{t}+@var{n} is generated and @var{x}(@var{n}+1:@var{t}+@var{n})
is returned.  If @var{n} is omitted, @var{n} = 100 is used. 
@end deftypefn


@anchor{doc-autocor}
@deftypefn {Function File} {} autocor (@var{x}, @var{h})
Return the autocorrelations from lag 0 to @var{h} of vector @var{x}.
If @var{h} is omitted, all autocorrelations are computed.
If @var{x} is a matrix, the autocorrelations of each column are
computed.
@end deftypefn


@anchor{doc-autocov}
@deftypefn {Function File} {} autocov (@var{x}, @var{h})
Return the autocovariances from lag 0 to @var{h} of vector @var{x}.
If @var{h} is omitted, all autocovariances are computed.
If @var{x} is a matrix, the autocovariances of each column are
computed.
@end deftypefn


@anchor{doc-autoreg_matrix}
@deftypefn {Function File} {} autoreg_matrix (@var{y}, @var{k})
Given a time series (vector) @var{y}, return a matrix with ones in the
first column and the first @var{k} lagged values of @var{y} in the
other columns.  I.e., for @var{t} > @var{k}, @code{[1,
@var{y}(@var{t}-1), ..., @var{y}(@var{t}-@var{k})]} is the t-th row
of the result.  The resulting matrix may be used as a regressor matrix
in autoregressions.
@end deftypefn


@anchor{doc-bartlett}
@deftypefn {Function File} {} bartlett (@var{m})
Return the filter coefficients of a Bartlett (triangular) window of
length @var{m}.

For a definition of the Bartlett window, see e.g. A. V. Oppenheim &
R. W. Schafer, "Discrete-Time Signal Processing".
@end deftypefn


@anchor{doc-blackman}
@deftypefn {Function File} {} blackman (@var{m})
Return the filter coefficients of a Blackman window of length @var{m}.

For a definition of the  Blackman window, see e.g. A. V. Oppenheim &
R. W. Schafer, "Discrete-Time Signal Processing".
@end deftypefn


@anchor{doc-diffpara}
@deftypefn {Function File} {[@var{d}, @var{dd}]} = diffpara (@var{x}, @var{a}, @var{b})
Return the estimator @var{d} for the differencing parameter of an
integrated time series.

The frequencies from @math{[2*pi*a/t, 2*pi*b/T]} are used for the
estimation.  If @var{b} is omitted, the interval
@math{[2*pi/T, 2*pi*a/T]} is used.  If both @var{b} and @var{a} are
omitted then @math{a = 0.5 * sqrt (T)} and @math{b = 1.5 * sqrt (T)}
is used, where @math{T} is the sample size.  If @var{x} is a matrix,
the differencing parameter of each column is estimated.

The estimators for all frequencies in the intervals
described above is returned in @var{dd}.  The value of @var{d} is
simply the mean of @var{dd}.

Reference: Brockwell, Peter J. & Davis, Richard A. Time Series:
Theory and Methods Springer 1987.
@end deftypefn


@anchor{doc-durbinlevinson}
@deftypefn {Function File} {} durbinlevinson (@var{c}, @var{oldphi}, @var{oldv})
Perform one step of the Durbin-Levinson algorithm.

The vector @var{c} specifies the autocovariances @code{[gamma_0, ...,
gamma_t]} from lag 0 to @var{t}, @var{oldphi} specifies the
coefficients based on @var{c}(@var{t}-1) and @var{oldv} specifies the
corresponding error.

If @var{oldphi} and @var{oldv} are omitted, all steps from 1 to
@var{t} of the algorithm are performed.
@end deftypefn


@anchor{doc-fftshift}
@deftypefn {Function File} {} fftshift (@var{v})
Perform a shift of the vector @var{v}, for use with the @code{fft}
and @code{ifft} functions, in order the move the frequency 0 to the
center of the vector or matrix.

If @var{v} is a vector of @math{N} elements corresponding to @math{N}
time samples spaced of @math{Dt} each, then @code{fftshift (fft
(@var{v}))} corresponds to frequencies

@example
f = ((1:N) - ceil(N/2)) / N / Dt
@end example

If @var{v} is a matrix, the same holds for rows and columns.
@end deftypefn


@anchor{doc-fractdiff}
@deftypefn {Function File} {} fractdiff (@var{x}, @var{d})
Compute the fractional differences @math{(1-L)^d x} where @math{L}
denotes the lag-operator and @math{d} is greater than -1.
@end deftypefn


@anchor{doc-hamming}
@deftypefn {Function File} {} hamming (@var{m})
Return the filter coefficients of a Hamming window of length @var{m}.

For a definition of the Hamming window, see e.g. A. V. Oppenheim &
R. W. Schafer, "Discrete-Time Signal Processing".
@end deftypefn


@anchor{doc-hanning}
@deftypefn {Function File} {} hanning (@var{m})
Return the filter coefficients of a Hanning window of length @var{m}.

For a definition of this window type, see e.g. A. V. Oppenheim &
R. W. Schafer, "Discrete-Time Signal Processing".
@end deftypefn


@anchor{doc-hurst}
@deftypefn {Function File} {} hurst (@var{x})
Estimate the Hurst parameter of sample @var{x} via the rescaled range
statistic.  If @var{x} is a matrix, the parameter is estimated for
every single column.
@end deftypefn


@anchor{doc-periodogram}
@deftypefn {Function File} {} periodogram (@var{x})
For a data matrix @var{x} from a sample of size @var{n}, return the
periodogram.
@end deftypefn


@anchor{doc-rectangle_lw}
@deftypefn {Function File} {} rectangle_lw (@var{n}, @var{b})
Rectangular lag window. Subfunction used for spectral density
estimation.
@end deftypefn


@anchor{doc-rectangle_sw}
@deftypefn {Function File} {} rectangle_sw (@var{n}, @var{b})
Rectangular spectral window.  Subfunction used for spectral density
estimation.
@end deftypefn


@anchor{doc-sinetone}
@deftypefn {Function File} {} sinetone (@var{freq}, @var{rate}, @var{sec}, @var{ampl})
Return a sinetone of frequency @var{freq} with length of @var{sec}
seconds at sampling rate @var{rate} and with amplitude @var{ampl}.
The arguments @var{freq} and @var{ampl} may be vectors of common size.

Defaults are @var{rate} = 8000, @var{sec} = 1 and @var{ampl} = 64.
@end deftypefn


@anchor{doc-sinewave}
@deftypefn {Function File} {} sinewave (@var{m}, @var{n}, @var{d})
Return an @var{m}-element vector with @var{i}-th element given by
@code{sin (2 * pi * (@var{i}+@var{d}-1) / @var{n})}.

The default value for @var{d} is 0 and the default value for @var{n}
is @var{m}.
@end deftypefn


@anchor{doc-spectral_adf}
@deftypefn {Function File} {} spectral_adf (@var{c}, @var{win}, @var{b})
Return the spectral density estimator given a vector of
autocovariances @var{c}, window name @var{win}, and bandwidth,
@var{b}.

The window name, e.g., @code{"triangle"} or @code{"rectangle"} is
used to search for a function called @code{@var{win}_sw}.

If @var{win} is omitted, the triangle window is used.  If @var{b} is
omitted, @code{1 / sqrt (length (@var{x}))} is used.
@end deftypefn


@anchor{doc-spectral_xdf}
@deftypefn {Function File} {} spectral_xdf (@var{x}, @var{win}, @var{b})
Return the spectral density estimator given a data vector @var{x},
window name @var{win}, and bandwidth, @var{b}.

The window name, e.g., @code{"triangle"} or @code{"rectangle"} is
used to search for a function called @code{@var{win}_sw}.

If @var{win} is omitted, the triangle window is used.  If @var{b} is
omitted, @code{1 / sqrt (length (@var{x}))} is used.
@end deftypefn


@anchor{doc-spencer}
@deftypefn {Function File} {} spencer (@var{x})
Return Spencer's 15 point moving average of every single column of
@var{x}.
@end deftypefn


@anchor{doc-stft}
@deftypefn {Function File} {[@var{y}, @var{c}]} = stft (@var{x}, @var{win_size}, @var{inc}, @var{num_coef}, @var{w_type})
Compute the short-term Fourier transform of the vector @var{x} with
@var{num_coef} coefficients by applying a window of @var{win_size} data
points and an increment of @var{inc} points.

Before computing the Fourier transform, one of the following windows
is applied:

@table @asis
@item hanning
w_type = 1
@item hamming
w_type = 2
@item rectangle
w_type = 3
@end table

The window names can be passed as strings or by the @var{w_type} number.

If not all arguments are specified, the following defaults are used:
@var{win_size} = 80, @var{inc} = 24, @var{num_coef} = 64, and
@var{w_type} = 1.

@code{@var{y} = stft (@var{x}, @dots{})} returns the absolute values
of the Fourier coefficients according to the @var{num_coef} positive
frequencies.

@code{[@var{y}, @var{c}] = stft (@code{x}, @dots{})} returns the
entire STFT-matrix @var{y} and a 3-element vector @var{c} containing
the window size, increment, and window type, which is needed by the
synthesis function.
@end deftypefn


@anchor{doc-synthesis}
@deftypefn {Function File} {} synthesis (@var{y}, @var{c})
Compute a signal from its short-time Fourier transform @var{y} and a
3-element vector @var{c} specifying window size, increment, and
window type.

The values @var{y} and @var{c} can be derived by

@example
[@var{y}, @var{c}] = stft (@var{x} , @dots{})
@end example
@end deftypefn


@anchor{doc-triangle_lw}
@deftypefn {Function File} {} triangle_lw (@var{n}, @var{b})
Triangular lag window. Subfunction used for spectral density
estimation.
@end deftypefn


@anchor{doc-triangle_sw}
@deftypefn {Function File} {} triangle_sw (@var{n}, @var{b})
Triangular spectral window.  Subfunction used for spectral density
estimation.
@end deftypefn


@anchor{doc-yulewalker}
@deftypefn {Function File} {[@var{a}, @var{v}] =} yulewalker (@var{c})
Fit an AR (p)-model with Yule-Walker estimates given a vector @var{c}
of autocovariances @code{[gamma_0, ..., gamma_p]}.

Returns the AR coefficients, @var{a}, and the variance of white
noise, @var{v}.
@end deftypefn

