changeset 9211:f0c3d3fc4903

Simplify Texinfo documentation in .m scripts by removing redundant @iftex calls
author Rik <rdrider0-list@yahoo.com>
date Sun, 17 May 2009 14:39:39 -0700
parents a7a9eecc07b5
children 6feb27c38da1
files scripts/ChangeLog scripts/general/diff.m scripts/general/logspace.m scripts/linear-algebra/commutation_matrix.m scripts/linear-algebra/duplication_matrix.m scripts/linear-algebra/expm.m scripts/linear-algebra/planerot.m scripts/miscellaneous/bincoeff.m scripts/miscellaneous/namelengthmax.m scripts/optimization/glpk.m scripts/optimization/qp.m scripts/optimization/sqp.m scripts/plot/peaks.m scripts/plot/subplot.m scripts/polynomial/compan.m scripts/polynomial/pchip.m scripts/polynomial/polyout.m scripts/polynomial/residue.m scripts/polynomial/roots.m scripts/polynomial/spline.m scripts/signal/freqz.m scripts/signal/sinc.m scripts/specfun/beta.m scripts/specfun/betaln.m scripts/special-matrix/hankel.m scripts/special-matrix/hilb.m scripts/special-matrix/sylvester_matrix.m scripts/special-matrix/toeplitz.m scripts/special-matrix/vander.m scripts/statistics/base/cloglog.m scripts/statistics/base/cor.m scripts/statistics/base/corrcoef.m scripts/statistics/base/cov.m scripts/statistics/base/gls.m scripts/statistics/base/kendall.m scripts/statistics/base/kurtosis.m scripts/statistics/base/logit.m scripts/statistics/base/mean.m scripts/statistics/base/median.m scripts/statistics/base/ols.m scripts/statistics/base/skewness.m scripts/statistics/base/std.m scripts/statistics/distributions/kolmogorov_smirnov_cdf.m scripts/statistics/distributions/wblcdf.m scripts/statistics/distributions/wblpdf.m scripts/statistics/tests/hotelling_test_2.m
diffstat 46 files changed, 5 insertions(+), 166 deletions(-) [+]
line wrap: on
line diff
--- a/scripts/ChangeLog
+++ b/scripts/ChangeLog
@@ -1,3 +1,8 @@
+2009-05-17  Rik Wehbring  <rdrider0-list@yahoo.com>
+
+	* *.m: Simplify Texinfo documentation in .m scripts by removing 
+	redundant @iftex calls
+
 2009-05-17  Jaroslav Hajek  <highegg@gmail.com>
 
 	* optimization/fminunc.m: Improve TR updating strategy.
--- a/scripts/general/diff.m
+++ b/scripts/general/diff.m
@@ -21,11 +21,9 @@
 ## @deftypefn {Function File} {} diff (@var{x}, @var{k}, @var{dim})
 ## If @var{x} is a vector of length @var{n}, @code{diff (@var{x})} is the
 ## vector of first differences
-## @iftex
 ## @tex
 ##  $x_2 - x_1, \ldots{}, x_n - x_{n-1}$.
 ## @end tex
-## @end iftex
 ## @ifnottex
 ##  @var{x}(2) - @var{x}(1), @dots{}, @var{x}(n) - @var{x}(n-1).
 ## @end ifnottex
--- a/scripts/general/logspace.m
+++ b/scripts/general/logspace.m
@@ -21,39 +21,31 @@
 ## @deftypefn {Function File} {} logspace (@var{base}, @var{limit}, @var{n})
 ## Similar to @code{linspace} except that the values are logarithmically
 ## spaced from
-## @iftex
 ## @tex
 ## $10^{base}$ to $10^{limit}$.
 ## @end tex
-## @end iftex
 ## @ifnottex
 ## 10^base to 10^limit.
 ## @end ifnottex
 ##
 ## If @var{limit} is equal to
-## @iftex
 ## @tex
 ## $\pi$,
 ## @end tex
-## @end iftex
 ## @ifnottex
 ## pi,
 ## @end ifnottex
 ## the points are between
-## @iftex
 ## @tex
 ## $10^{base}$ and $\pi$,
 ## @end tex
-## @end iftex
 ## @ifnottex
 ## 10^base and pi,
 ## @end ifnottex
 ## @emph{not}
-## @iftex
 ## @tex
 ## $10^{base}$ and $10^{\pi}$,
 ## @end tex
-## @end iftex
 ## @ifnottex
 ## 10^base and 10^pi,
 ## @end ifnottex
--- a/scripts/linear-algebra/commutation_matrix.m
+++ b/scripts/linear-algebra/commutation_matrix.m
@@ -20,57 +20,45 @@
 ## -*- texinfo -*-
 ## @deftypefn {Function File} {} commutation_matrix (@var{m}, @var{n})
 ## Return the commutation matrix
-## @iftex
 ## @tex
 ##  $K_{m,n}$
 ## @end tex
-## @end iftex
 ## @ifnottex
 ##  K(m,n)
 ## @end ifnottex
 ##  which is the unique
-## @iftex
 ## @tex
 ##  $m n \times m n$
 ## @end tex
-## @end iftex
 ## @ifnottex
 ##  @var{m}*@var{n} by @var{m}*@var{n}
 ## @end ifnottex
 ##  matrix such that
-## @iftex
 ## @tex
 ##  $K_{m,n} \cdot {\rm vec} (A) = {\rm vec} (A^T)$
 ## @end tex
-## @end iftex
 ## @ifnottex
 ##  @math{K(m,n) * vec(A) = vec(A')}
 ## @end ifnottex
 ##  for all
-## @iftex
 ## @tex
 ##  $m\times n$
 ## @end tex
-## @end iftex
 ## @ifnottex
 ##  @math{m} by @math{n}
 ## @end ifnottex
 ##  matrices
-## @iftex
 ## @tex
 ##  $A$.
 ## @end tex
-## @end iftex
 ## @ifnottex
 ##  @math{A}.
 ## @end ifnottex
 ##
 ## If only one argument @var{m} is given,
-## @iftex
 ## @tex
 ##  $K_{m,m}$
 ## @end tex
-## @end iftex
 ## @ifnottex
 ##  @math{K(m,m)}
 ## @end ifnottex
--- a/scripts/linear-algebra/duplication_matrix.m
+++ b/scripts/linear-algebra/duplication_matrix.m
@@ -20,47 +20,37 @@
 ## -*- texinfo -*-
 ## @deftypefn {Function File} {} duplication_matrix (@var{n})
 ## Return the duplication matrix
-## @iftex
 ## @tex
 ##  $D_n$
 ## @end tex
-## @end iftex
 ## @ifnottex
 ##  @math{Dn}
 ## @end ifnottex
 ##  which is the unique
-## @iftex
 ## @tex
 ##  $n^2 \times n(n+1)/2$
 ## @end tex
-## @end iftex
 ## @ifnottex
 ##  @math{n^2} by @math{n*(n+1)/2}
 ## @end ifnottex
 ##  matrix such that
-## @iftex
 ## @tex
 ##  $D_n * {\rm vech} (A) = {\rm vec} (A)$
 ## @end tex
-## @end iftex
 ## @ifnottex
 ##  @math{Dn vech (A) = vec (A)}
 ## @end ifnottex
 ##  for all symmetric
-## @iftex
 ## @tex
 ##  $n \times n$
 ## @end tex
-## @end iftex
 ## @ifnottex
 ##  @math{n} by @math{n}
 ## @end ifnottex
 ##  matrices
-## @iftex
 ## @tex
 ##  $A$.
 ## @end tex
-## @end iftex
 ## @ifnottex
 ##  @math{A}.
 ## @end ifnottex
--- a/scripts/linear-algebra/expm.m
+++ b/scripts/linear-algebra/expm.m
@@ -20,13 +20,11 @@
 ## @deftypefn {Function File} {} expm (@var{a})
 ## Return the exponential of a matrix, defined as the infinite Taylor
 ## series
-## @iftex
 ## @tex
 ## $$
 ##  \exp (A) = I + A + {A^2 \over 2!} + {A^3 \over 3!} + \cdots
 ## $$
 ## @end tex
-## @end iftex
 ## @ifnottex
 ## 
 ## @example
@@ -38,30 +36,24 @@
 ## exponential; see Moler and Van Loan, @cite{Nineteen Dubious Ways to
 ## Compute the Exponential of a Matrix}, SIAM Review, 1978.  This routine
 ## uses Ward's diagonal
-## @iftex
 ## @tex
 ## Pad\'e
 ## @end tex
-## @end iftex
 ## @ifnottex
 ## Pade'
 ## @end ifnottex
 ## approximation method with three step preconditioning (SIAM Journal on
 ## Numerical Analysis, 1977).  Diagonal
-## @iftex
 ## @tex
 ## Pad\'e
 ## @end tex
-## @end iftex
 ## @ifnottex
 ## Pade'
 ## @end ifnottex
 ##  approximations are rational polynomials of matrices
-## @iftex
 ## @tex
 ## $D_q(a)^{-1}N_q(a)$
 ## @end tex
-## @end iftex
 ## @ifnottex
 ## 
 ## @example
@@ -73,30 +65,24 @@
 ## 
 ## @end ifnottex
 ##  whose Taylor series matches the first
-## @iftex
 ## @tex
 ## $2 q + 1 $
 ## @end tex
-## @end iftex
 ## @ifnottex
 ## @code{2q+1}
 ## @end ifnottex
 ## terms of the Taylor series above; direct evaluation of the Taylor series
 ## (with the same preconditioning steps) may be desirable in lieu of the
-## @iftex
 ## @tex
 ## Pad\'e
 ## @end tex
-## @end iftex
 ## @ifnottex
 ## Pade'
 ## @end ifnottex
 ## approximation when
-## @iftex
 ## @tex
 ## $D_q(a)$
 ## @end tex
-## @end iftex
 ## @ifnottex
 ## @code{Dq(a)}
 ## @end ifnottex
--- a/scripts/linear-algebra/planerot.m
+++ b/scripts/linear-algebra/planerot.m
@@ -19,11 +19,9 @@
 ## -*- texinfo -*-
 ## @deftypefn {Function File} {[@var{g}, @var{y}] =} planerot (@var{x})
 ## Given a two-element column vector, returns the
-## @iftex
 ## @tex
 ## $2 \times 2$ orthogonal matrix
 ## @end tex
-## @end iftex
 ## @ifnottex
 ## 2 by 2 orthogonal matrix
 ## @end ifnottex
--- a/scripts/miscellaneous/bincoeff.m
+++ b/scripts/miscellaneous/bincoeff.m
@@ -20,13 +20,11 @@
 ## -*- texinfo -*-
 ## @deftypefn {Mapping Function} {} bincoeff (@var{n}, @var{k})
 ## Return the binomial coefficient of @var{n} and @var{k}, defined as
-## @iftex
 ## @tex
 ## $$
 ##  {n \choose k} = {n (n-1) (n-2) \cdots (n-k+1) \over k!}
 ## $$
 ## @end tex
-## @end iftex
 ## @ifnottex
 ##
 ## @example
--- a/scripts/miscellaneous/namelengthmax.m
+++ b/scripts/miscellaneous/namelengthmax.m
@@ -20,11 +20,9 @@
 ## @deftypefn {Function File} {} namelengthmax ()
 ## Returns the @sc{matlab} compatible maximum variable name length.  Octave is
 ## capable of storing strings up to 
-## @iftex
 ## @tex
 ## $2^{31} - 1$
 ## @end tex
-## @end iftex
 ## @ifnottex
 ## @code{2 ^ 31 - 1}
 ## @end ifnottex
--- a/scripts/optimization/glpk.m
+++ b/scripts/optimization/glpk.m
@@ -21,13 +21,11 @@
 ## Solve a linear program using the GNU GLPK library.  Given three
 ## arguments, @code{glpk} solves the following standard LP:
 ## 
-## @iftex
 ## @tex
 ## $$
 ##   \min_x C^T x
 ## $$
 ## @end tex
-## @end iftex
 ## @ifnottex
 ## @example
 ## min C'*x
@@ -36,13 +34,11 @@
 ## 
 ## subject to
 ## 
-## @iftex
 ## @tex
 ## $$
 ##   Ax = b \qquad x \geq 0
 ## $$
 ## @end tex
-## @end iftex
 ## @ifnottex
 ## @example
 ## @group
@@ -54,13 +50,11 @@
 ## 
 ## but may also solve problems of the form
 ## 
-## @iftex
 ## @tex
 ## $$
 ##   [ \min_x | \max_x ] C^T x
 ## $$
 ## @end tex
-## @end iftex
 ## @ifnottex
 ## @example
 ## [ min | max ] C'*x
@@ -69,13 +63,11 @@
 ## 
 ## subject to
 ## 
-## @iftex
 ## @tex
 ## $$
 ##  Ax [ = | \leq | \geq ] b \qquad LB \leq x \leq UB
 ## $$
 ## @end tex
-## @end iftex
 ## @ifnottex
 ## @example
 ## @group
--- a/scripts/optimization/qp.m
+++ b/scripts/optimization/qp.m
@@ -20,13 +20,11 @@
 ## -*- texinfo -*-
 ## @deftypefn {Function File} {[@var{x}, @var{obj}, @var{info}, @var{lambda}] =} qp (@var{x0}, @var{H}, @var{q}, @var{A}, @var{b}, @var{lb}, @var{ub}, @var{A_lb}, @var{A_in}, @var{A_ub})
 ## Solve the quadratic program
-## @iftex
 ## @tex
 ## $$
 ##  \min_x {1 \over 2} x^T H x + x^T q
 ## $$
 ## @end tex
-## @end iftex
 ## @ifnottex
 ##
 ## @example
@@ -38,13 +36,11 @@
 ##
 ## @end ifnottex
 ## subject to
-## @iftex
 ## @tex
 ## $$
 ##  Ax = b \qquad lb \leq x \leq ub \qquad A_{lb} \leq A_{in} \leq A_{ub}
 ## $$
 ## @end tex
-## @end iftex
 ## @ifnottex
 ##
 ## @example
--- a/scripts/optimization/sqp.m
+++ b/scripts/optimization/sqp.m
@@ -19,13 +19,11 @@
 ## -*- texinfo -*-
 ## @deftypefn {Function File} {[@var{x}, @var{obj}, @var{info}, @var{iter}, @var{nf}, @var{lambda}] =} sqp (@var{x}, @var{phi}, @var{g}, @var{h}, @var{lb}, @var{ub}, @var{maxiter}, @var{tolerance})
 ## Solve the nonlinear program
-## @iftex
 ## @tex
 ## $$
 ## \min_x \phi (x)
 ## $$
 ## @end tex
-## @end iftex
 ## @ifnottex
 ##
 ## @example
@@ -37,13 +35,11 @@
 ##
 ## @end ifnottex
 ## subject to
-## @iftex
 ## @tex
 ## $$
 ##  g(x) = 0 \qquad h(x) \geq 0 \qquad lb \leq x \leq ub
 ## $$
 ## @end tex
-## @end iftex
 ## @ifnottex
 ##
 ## @example
@@ -119,7 +115,6 @@
 ## function and the second should point to a function that computes the
 ## gradient of the constraint function:
 ##
-## @iftex
 ## @tex
 ## $$
 ##  \Bigg( {\partial f(x) \over \partial x_1}, 
@@ -127,7 +122,6 @@
 ##         {\partial f(x) \over \partial x_N} \Bigg)^T
 ## $$
 ## @end tex
-## @end iftex
 ## @ifnottex
 ## @example
 ## @group
--- a/scripts/plot/peaks.m
+++ b/scripts/plot/peaks.m
@@ -25,11 +25,9 @@
 ## Generate a function with lots of local maxima and minima.  The function
 ## has the form
 ##
-## @iftex
 ## @tex
 ## $f(x,y) = 3 (1 - x) ^ 2 e ^ {\left(-x^2 - (y+1)^2\right)} - 10 \left({x \over 5} - x^3 - y^5)\right) - {1 \over 3} e^{\left(-(x+1)^2 - y^2\right)}$
 ## @end tex
-## @end iftex
 ## @ifnottex
 ## @verbatim
 ## f(x,y) = 3*(1-x)^2*exp(-x^2 - (y+1)^2) ...
--- a/scripts/plot/subplot.m
+++ b/scripts/plot/subplot.m
@@ -32,7 +32,6 @@
 ##
 ## For example, a plot with 2 by 3 grid will have plot indices running as
 ## follows:
-## @iftex
 ## @tex
 ## \vskip 10pt
 ## \hfil\vbox{\offinterlineskip\hrule
@@ -42,7 +41,6 @@
 ## \hfil
 ## \vskip 10pt
 ## @end tex
-## @end iftex
 ## @ifnottex
 ## @display
 ## @example
--- a/scripts/polynomial/compan.m
+++ b/scripts/polynomial/compan.m
@@ -23,7 +23,6 @@
 ## vector @var{c}.
 ##
 ## The companion matrix is
-## @iftex
 ## @tex
 ## $$
 ## A = \left[\matrix{
@@ -34,7 +33,6 @@
 ##      0    &     0    & \cdots &     1    &         0}\right].
 ## $$
 ## @end tex
-## @end iftex
 ## @ifnottex
 ##
 ## @c Set example in small font to prevent overfull line
--- a/scripts/polynomial/pchip.m
+++ b/scripts/polynomial/pchip.m
@@ -30,21 +30,17 @@
 ## array.  In the case where @var{y} is a vector, it must have a length
 ## of @var{n}.  If @var{y} is an array, then the size of @var{y} must
 ## have the form
-## @iftex
 ## @tex
 ## $$[s_1, s_2, \cdots, s_k, n]$$
 ## @end tex
-## @end iftex
 ## @ifnottex
 ## @code{[@var{s1}, @var{s2}, @dots{}, @var{sk}, @var{n}]}
 ## @end ifnottex
 ## The array is then reshaped internally to a matrix where the leading
 ## dimension is given by 
-## @iftex
 ## @tex
 ## $$s_1 s_2 \cdots s_k$$
 ## @end tex
-## @end iftex
 ## @ifnottex
 ## @code{@var{s1} * @var{s2} * @dots{} * @var{sk}}
 ## @end ifnottex
--- a/scripts/polynomial/polyout.m
+++ b/scripts/polynomial/polyout.m
@@ -20,11 +20,9 @@
 ## -*- texinfo -*-
 ## @deftypefn {Function File} {} polyout (@var{c}, @var{x})
 ## Write formatted polynomial
-## @iftex
 ## @tex
 ## $$ c(x) = c_1 x^n + \ldots + c_n x + c_{n+1} $$
 ## @end tex
-## @end iftex
 ## @ifnottex
 ## @example
 ##    c(x) = c(1) * x^n + @dots{} + c(n) x + c(n+1)
--- a/scripts/polynomial/residue.m
+++ b/scripts/polynomial/residue.m
@@ -23,14 +23,12 @@
 ## Compute the partial fraction expansion for the quotient of the
 ## polynomials, @var{b} and @var{a}.
 ##
-## @iftex
 ## @tex
 ## $$
 ## {B(s)\over A(s)} = \sum_{m=1}^M {r_m\over (s-p_m)^e_m}
 ##   + \sum_{i=1}^N k_i s^{N-i}.
 ## $$
 ## @end tex
-## @end iftex
 ## @ifnottex
 ##
 ## @example
@@ -64,13 +62,11 @@
 ##
 ## @noindent
 ## which represents the following partial fraction expansion
-## @iftex
 ## @tex
 ## $$
 ## {s^2+s+1\over s^3-5s^2+8s-4} = {-2\over s-2} + {7\over (s-2)^2} + {3\over s-1}
 ## $$
 ## @end tex
-## @end iftex
 ## @ifnottex
 ##
 ## @example
@@ -125,13 +121,11 @@
 ##
 ## @noindent
 ## which represents the following partial fraction expansion
-## @iftex
 ## @tex
 ## $$
 ## {-2\over s-2} + {7\over (s-2)^2} + {3\over s-1} + s = {s^4-5s^3+9s^2-3s+1\over s^3-5s^2+8s-4}
 ## $$
 ## @end tex
-## @end iftex
 ## @ifnottex
 ##
 ## @example
--- a/scripts/polynomial/roots.m
+++ b/scripts/polynomial/roots.m
@@ -22,13 +22,11 @@
 ##
 ## For a vector @var{v} with @math{N} components, return
 ## the roots of the polynomial
-## @iftex
 ## @tex
 ## $$
 ## v_1 z^{N-1} + \cdots + v_{N-1} z + v_N.
 ## $$
 ## @end tex
-## @end iftex
 ## @ifnottex
 ##
 ## @example
@@ -38,11 +36,9 @@
 ##
 ## As an example, the following code finds the roots of the quadratic
 ## polynomial
-## @iftex
 ## @tex
 ## $$ p(x) = x^2 - 5. $$
 ## @end tex
-## @end iftex
 ## @ifnottex
 ## @example
 ## p(x) = x^2 - 5.
@@ -57,20 +53,16 @@
 ## @end group
 ## @end example
 ## Note that the true result is
-## @iftex
 ## @tex
 ## $\pm \sqrt{5}$
 ## @end tex
-## @end iftex
 ## @ifnottex
 ## @math{+/- sqrt(5)}
 ## @end ifnottex
 ## which is roughly
-## @iftex
 ## @tex
 ## $\pm 2.2361$.
 ## @end tex
-## @end iftex
 ## @ifnottex
 ## @math{+/- 2.2361}.
 ## @end ifnottex
--- a/scripts/polynomial/spline.m
+++ b/scripts/polynomial/spline.m
@@ -39,30 +39,24 @@
 ## of the first derivative of the cubic spline at the end-points.
 ##
 ## If @var{y} is an array, then the size of @var{y} must have the form
-## @iftex
 ## @tex
 ## $$[s_1, s_2, \cdots, s_k, n]$$
 ## @end tex
-## @end iftex
 ## @ifnottex
 ## @code{[@var{s1}, @var{s2}, @dots{}, @var{sk}, @var{n}]}
 ## @end ifnottex
 ## or
-## @iftex
 ## @tex
 ## $$[s_1, s_2, \cdots, s_k, n + 2].$$
 ## @end tex
-## @end iftex
 ## @ifnottex
 ## @code{[@var{s1}, @var{s2}, @dots{}, @var{sk}, @var{n} + 2]}.
 ## @end ifnottex
 ## The array is then reshaped internally to a matrix where the leading
 ## dimension is given by 
-## @iftex
 ## @tex
 ## $$s_1 s_2 \cdots s_k$$
 ## @end tex
-## @end iftex
 ## @ifnottex
 ## @code{@var{s1} * @var{s2} * @dots{} * @var{sk}}
 ## @end ifnottex
--- a/scripts/signal/freqz.m
+++ b/scripts/signal/freqz.m
@@ -26,11 +26,9 @@
 ## @ifnottex
 ##  2*pi.
 ## @end ifnottex
-## @iftex
 ## @tex
 ##  $2\pi$.
 ## @end tex
-## @end iftex
 ##
 ## @noindent
 ## The output value @var{w} is a vector of the frequencies.
@@ -40,11 +38,9 @@
 ## @ifnottex
 ##  pi.
 ## @end ifnottex
-## @iftex
 ## @tex
 ##  $\pi$.
 ## @end tex
-## @end iftex
 ##
 ## If @var{n} is omitted, a value of 512 is assumed.
 ##
--- a/scripts/signal/sinc.m
+++ b/scripts/signal/sinc.m
@@ -20,11 +20,9 @@
 ## -*- texinfo -*-
 ## @deftypefn {Function File} {} sinc (@var{x})
 ## Return
-## @iftex
 ## @tex
 ## $ \sin (\pi x)/(\pi x)$.
 ## @end tex
-## @end iftex
 ## @ifnottex
 ##  sin(pi*x)/(pi*x).
 ## @end ifnottex
--- a/scripts/specfun/beta.m
+++ b/scripts/specfun/beta.m
@@ -20,13 +20,11 @@
 ## -*- texinfo -*-
 ## @deftypefn {Mapping Function} {} beta (@var{a}, @var{b})
 ## For real inputs, return the Beta function,
-## @iftex
 ## @tex
 ## $$
 ##  B (a, b) = {\Gamma (a) \Gamma (b) \over \Gamma (a + b)}.
 ## $$
 ## @end tex
-## @end iftex
 ## @ifnottex
 ##
 ## @example
--- a/scripts/specfun/betaln.m
+++ b/scripts/specfun/betaln.m
@@ -19,13 +19,11 @@
 ## -*- texinfo -*-
 ## @deftypefn {Mapping Function} {} betaln (@var{a}, @var{b})
 ## Return the log of the Beta function,
-## @iftex
 ## @tex
 ## $$
 ##  B (a, b) = \log {\Gamma (a) \Gamma (b) \over \Gamma (a + b)}.
 ## $$
 ## @end tex
-## @end iftex
 ## @ifnottex
 ##
 ## @example
--- a/scripts/special-matrix/hankel.m
+++ b/scripts/special-matrix/hankel.m
@@ -27,13 +27,11 @@
 ##
 ## A Hankel matrix formed from an m-vector @var{c}, and an n-vector
 ## @var{r}, has the elements
-## @iftex
 ## @tex
 ## $$
 ## H (i, j) = \cases{c_{i+j-1},&$i+j-1\le m$;\cr r_{i+j-m},&otherwise.\cr}
 ## $$
 ## @end tex
-## @end iftex
 ## @ifnottex
 ##
 ## @example
--- a/scripts/special-matrix/hilb.m
+++ b/scripts/special-matrix/hilb.m
@@ -20,22 +20,18 @@
 ## -*- texinfo -*-
 ## @deftypefn {Function File} {} hilb (@var{n})
 ## Return the Hilbert matrix of order @var{n}.  The
-## @iftex
 ## @tex
 ## $i,\,j$
 ## @end tex
-## @end iftex
 ## @ifnottex
 ## i, j
 ## @end ifnottex
 ## element of a Hilbert matrix is defined as
-## @iftex
 ## @tex
 ## $$
 ## H (i, j) = {1 \over (i + j - 1)}
 ## $$
 ## @end tex
-## @end iftex
 ## @ifnottex
 ##
 ## @example
--- a/scripts/special-matrix/sylvester_matrix.m
+++ b/scripts/special-matrix/sylvester_matrix.m
@@ -20,11 +20,9 @@
 ## -*- texinfo -*-
 ## @deftypefn {Function File} {} sylvester_matrix (@var{k})
 ## Return the Sylvester matrix of order
-## @iftex
 ## @tex
 ## $n = 2^k$.
 ## @end tex
-## @end iftex
 ## @ifnottex
 ## n = 2^k.
 ## @end ifnottex
--- a/scripts/special-matrix/toeplitz.m
+++ b/scripts/special-matrix/toeplitz.m
@@ -27,7 +27,6 @@
 ## taken to be the same as the first column.
 ##
 ## A square Toeplitz matrix has the form:
-## @iftex
 ## @tex
 ## $$
 ## \left[\matrix{c_0    & r_1     & r_2      & \cdots & r_n\cr
@@ -37,7 +36,6 @@
 ##               c_n    & c_{n-1} & c_{n-2} & \ldots & c_0}\right]
 ## $$
 ## @end tex
-## @end iftex
 ## @ifnottex
 ##
 ## @example
--- a/scripts/special-matrix/vander.m
+++ b/scripts/special-matrix/vander.m
@@ -25,7 +25,6 @@
 ## otherwise, @var{n} is taken to be equal to the length of @var{c}.
 ##
 ## A Vandermonde matrix has the form:
-## @iftex
 ## @tex
 ## $$
 ## \left[\matrix{c_1^{n-1}  & \cdots & c_1^2  & c_1    & 1      \cr
@@ -34,7 +33,6 @@
 ##               c_n^{n-1}  & \cdots & c_n^2  & c_n    & 1      }\right]
 ## $$
 ## @end tex
-## @end iftex
 ## @ifnottex
 ##
 ## @example
--- a/scripts/statistics/base/cloglog.m
+++ b/scripts/statistics/base/cloglog.m
@@ -21,13 +21,11 @@
 ## @deftypefn {Function File} {} cloglog (@var{x})
 ## Return the complementary log-log function of @var{x}, defined as
 ##
-## @iftex
 ## @tex
 ## $$
 ## {\rm cloglog}(x) = - \log (- \log (x))
 ## $$
 ## @end tex
-## @end iftex
 ## @ifnottex
 ## @example
 ## cloglog(x) = - log (- log (@var{x}))
--- a/scripts/statistics/base/cor.m
+++ b/scripts/statistics/base/cor.m
@@ -25,13 +25,11 @@
 ## the correlation between the @var{i}-th variable in @var{x} and the
 ## @var{j}-th variable in @var{y}.
 ##
-## @iftex
 ## @tex
 ## $$
 ## {\rm corrcoef}(x,y) = {{\rm cov}(x,y) \over {\rm std}(x) {\rm std}(y)}
 ## $$
 ## @end tex
-## @end iftex
 ## @ifnottex
 ## @example
 ## corrcoef(x,y) = cov(x,y)/(std(x)*std(y))
--- a/scripts/statistics/base/corrcoef.m
+++ b/scripts/statistics/base/corrcoef.m
@@ -26,13 +26,11 @@
 ## @code{corrcoef (@var{x}, @var{y})} is the correlation between the
 ## @var{i}-th variable in @var{x} and the @var{j}-th variable in @var{y}.
 ##
-## @iftex
 ## @tex
 ## $$
 ## {\rm corrcoef}(x,y) = {{\rm cov}(x,y) \over {\rm std}(x) {\rm std}(y)}
 ## $$
 ## @end tex
-## @end iftex
 ## @ifnottex
 ## @example
 ## corrcoef(x,y) = cov(x,y)/(std(x)*std(y))
--- a/scripts/statistics/base/cov.m
+++ b/scripts/statistics/base/cov.m
@@ -25,14 +25,12 @@
 ## a variable, the (@var{i}, @var{j})-th entry of
 ## @code{cov (@var{x}, @var{y})} is the covariance between the @var{i}-th
 ## variable in @var{x} and the @var{j}-th variable in @var{y}.
-## @iftex
 ## @tex
 ## $$
 ## \sigma_{ij} = {1 \over N-1} \sum_{i=1}^N (x_i - \bar{x})(y_i - \bar{y})
 ## $$
 ## where $\bar{x}$ and $\bar{y}$ are the mean values of $x$ and $y$.
 ## @end tex
-## @end iftex
 ## If called with one argument, compute @code{cov (@var{x}, @var{x})}.
 ## @end deftypefn
 
--- a/scripts/statistics/base/gls.m
+++ b/scripts/statistics/base/gls.m
@@ -20,24 +20,20 @@
 ## -*- texinfo -*-
 ## @deftypefn {Function File} {[@var{beta}, @var{v}, @var{r}] =} gls (@var{y}, @var{x}, @var{o})
 ## Generalized least squares estimation for the multivariate model
-## @iftex
 ## @tex
 ## $y = x b + e$
 ## with $\bar{e} = 0$ and cov(vec($e$)) = $(s^2)o$,
 ## @end tex
-## @end iftex
 ## @ifnottex
 ## @math{y = x b + e} with @math{mean (e) = 0} and
 ## @math{cov (vec (e)) = (s^2) o},
 ## @end ifnottex
 ##  where
-## @iftex
 ## @tex
 ## $y$ is a $t \times p$ matrix, $x$ is a $t \times k$ matrix, $b$ is a $k
 ## \times p$ matrix, $e$ is a $t \times p$ matrix, and $o$ is a $tp \times
 ## tp$ matrix.
 ## @end tex
-## @end iftex
 ## @ifnottex
 ## @math{y} is a @math{t} by @math{p} matrix, @math{x} is a @math{t} by
 ## @math{k} matrix, @math{b} is a @math{k} by @math{p} matrix, @math{e}
--- a/scripts/statistics/base/kendall.m
+++ b/scripts/statistics/base/kendall.m
@@ -33,11 +33,9 @@
 ## differences of @var{x} and @var{y};  i.e., if both @var{x} and
 ## @var{y} have distinct entries, then
 ##
-## @iftex
 ## @tex
 ## $$ \tau = {1 \over n(n-1)} \sum_{i,j} {\rm sign}(q_i-q_j) {\rm sign}(r_i-r_j) $$
 ## @end tex
-## @end iftex
 ## @ifnottex
 ## @example
 ## @group
@@ -50,11 +48,9 @@
 ##
 ## @noindent
 ## in which the
-## @iftex
 ## @tex
 ## $q_i$ and $r_i$
 ## @end tex
-## @end iftex
 ## @ifnottex
 ## @var{q}(@var{i}) and @var{r}(@var{i})
 ## @end ifnottex
@@ -63,11 +59,9 @@
 ##
 ## If @var{x} and @var{y} are drawn from independent distributions,
 ## Kendall's @var{tau} is asymptotically normal with mean 0 and variance
-## @iftex
 ## @tex
 ## ${2 (2n+5) \over 9n(n-1)}$.
 ## @end tex
-## @end iftex
 ## @ifnottex
 ## @code{(2 * (2@var{n}+5)) / (9 * @var{n} * (@var{n}-1))}.
 ## @end ifnottex
--- a/scripts/statistics/base/kurtosis.m
+++ b/scripts/statistics/base/kurtosis.m
@@ -20,14 +20,12 @@
 ## -*- texinfo -*-
 ## @deftypefn {Function File} {} kurtosis (@var{x}, @var{dim})
 ## If @var{x} is a vector of length @math{N}, return the kurtosis
-## @iftex
 ## @tex
 ## $$
 ##  {\rm kurtosis} (x) = {1\over N \sigma(x)^4} \sum_{i=1}^N (x_i-\bar{x})^4 - 3
 ## $$
 ## where $\bar{x}$ is the mean value of $x$.
 ## @end tex
-## @end iftex
 ## @ifnottex
 ##
 ## @example
--- a/scripts/statistics/base/logit.m
+++ b/scripts/statistics/base/logit.m
@@ -20,13 +20,11 @@
 ## -*- texinfo -*-
 ## @deftypefn {Function File} {} logit (@var{p})
 ## For each component of @var{p}, return the logit of @var{p} defined as
-## @iftex
 ## @tex
 ## $$
 ## {\rm logit}(p) = \log\Big({p \over 1-p}\Big)
 ## $$
 ## @end tex
-## @end iftex
 ## @ifnottex
 ## @example
 ## logit(@var{p}) = log (@var{p} / (1-@var{p}))
--- a/scripts/statistics/base/mean.m
+++ b/scripts/statistics/base/mean.m
@@ -20,11 +20,9 @@
 ## -*- texinfo -*-
 ## @deftypefn {Function File} {} mean (@var{x}, @var{dim}, @var{opt})
 ## If @var{x} is a vector, compute the mean of the elements of @var{x}
-## @iftex
 ## @tex
 ## $$ {\rm mean}(x) = \bar{x} = {1\over N} \sum_{i=1}^N x_i $$
 ## @end tex
-## @end iftex
 ## @ifnottex
 ##
 ## @example
--- a/scripts/statistics/base/median.m
+++ b/scripts/statistics/base/median.m
@@ -22,7 +22,6 @@
 ## If @var{x} is a vector, compute the median value of the elements of
 ## @var{x}.  If the elements of @var{x} are sorted, the median is defined
 ## as
-## @iftex
 ## @tex
 ## $$
 ## {\rm median} (x) =
@@ -30,7 +29,6 @@
 ##           (x(N/2)+x(N/2+1))/2, & $N$ even.}
 ## $$
 ## @end tex
-## @end iftex
 ## @ifnottex
 ##
 ## @example
--- a/scripts/statistics/base/ols.m
+++ b/scripts/statistics/base/ols.m
@@ -20,24 +20,20 @@
 ## -*- texinfo -*-
 ## @deftypefn {Function File} {[@var{beta}, @var{sigma}, @var{r}] =} ols (@var{y}, @var{x})
 ## Ordinary least squares estimation for the multivariate model
-## @iftex
 ## @tex
 ## $y = x b + e$
 ## with
 ## $\bar{e} = 0$, and cov(vec($e$)) = kron ($s, I$)
 ## @end tex
-## @end iftex
 ## @ifnottex
 ## @math{y = x b + e} with
 ## @math{mean (e) = 0} and @math{cov (vec (e)) = kron (s, I)}.
 ## @end ifnottex
 ##  where
-## @iftex
 ## @tex
 ## $y$ is a $t \times p$ matrix, $x$ is a $t \times k$ matrix,
 ## $b$ is a $k \times p$ matrix, and $e$ is a $t \times p$ matrix.
 ## @end tex
-## @end iftex
 ## @ifnottex
 ## @math{y} is a @math{t} by @math{p} matrix, @math{x} is a @math{t} by
 ## @math{k} matrix, @math{b} is a @math{k} by @math{p} matrix, and
--- a/scripts/statistics/base/skewness.m
+++ b/scripts/statistics/base/skewness.m
@@ -20,14 +20,12 @@
 ## -*- texinfo -*-
 ## @deftypefn {Function File} {} skewness (@var{x}, @var{dim})
 ## If @var{x} is a vector of length @math{n}, return the skewness
-## @iftex
 ## @tex
 ## $$
 ## {\rm skewness} (x) = {1\over N \sigma(x)^3} \sum_{i=1}^N (x_i-\bar{x})^3
 ## $$
 ## where $\bar{x}$ is the mean value of $x$.
 ## @end tex
-## @end iftex
 ## @ifnottex
 ##
 ## @example
--- a/scripts/statistics/base/std.m
+++ b/scripts/statistics/base/std.m
@@ -23,14 +23,12 @@
 ## @deftypefnx {Function File} {} std (@var{x}, @var{opt}, @var{dim})
 ## If @var{x} is a vector, compute the standard deviation of the elements
 ## of @var{x}.
-## @iftex
 ## @tex
 ## $$
 ## {\rm std} (x) = \sigma (x) = \sqrt{{\sum_{i=1}^N (x_i - \bar{x})^2 \over N - 1}}
 ## $$
 ## where $\bar{x}$ is the mean value of $x$.
 ## @end tex
-## @end iftex
 ## @ifnottex
 ##
 ## @example
--- a/scripts/statistics/distributions/kolmogorov_smirnov_cdf.m
+++ b/scripts/statistics/distributions/kolmogorov_smirnov_cdf.m
@@ -20,11 +20,9 @@
 ## -*- texinfo -*-
 ## @deftypefn {Function File} {} kolmogorov_smirnov_cdf (@var{x}, @var{tol})
 ## Return the CDF at @var{x} of the Kolmogorov-Smirnov distribution,
-## @iftex
 ## @tex
 ## $$ Q(x) = \sum_{k=-\infty}^\infty (-1)^k \exp(-2 k^2 x^2) $$
 ## @end tex
-## @end iftex
 ## @ifnottex
 ## @example
 ## @group
--- a/scripts/statistics/distributions/wblcdf.m
+++ b/scripts/statistics/distributions/wblcdf.m
@@ -22,12 +22,10 @@
 ## Weibull distribution with shape parameter @var{scale} and scale
 ## parameter @var{shape}, which is
 ##
-## @iftex
 ## @tex
 ## $$ 1 - \exp(-(x/shape)^{scale}) $$
 ## for $x\geq 0$.
 ## @end tex
-## @end iftex
 ## @ifnottex
 ## @example
 ## 1 - exp(-(x/shape)^scale)
--- a/scripts/statistics/distributions/wblpdf.m
+++ b/scripts/statistics/distributions/wblpdf.m
@@ -22,11 +22,9 @@
 ## Weibull distribution with shape parameter @var{scale} and scale
 ## parameter @var{shape} which is given by
 ##
-## @iftex
 ## @tex
 ## $$  scale \cdot shape^{-scale} x^{scale-1} \exp(-(x/shape)^{scale}) $$
 ## @end tex
-## @end iftex
 ## @ifnottex
 ## @example
 ##    scale * shape^(-scale) * x^(scale-1) * exp(-(x/shape)^scale)
--- a/scripts/statistics/tests/hotelling_test_2.m
+++ b/scripts/statistics/tests/hotelling_test_2.m
@@ -26,13 +26,11 @@
 ##
 ## Hotelling's two-sample @math{T^2} is returned in @var{tsq}.  Under the null,
 ##
-## @iftex
 ## @tex
 ## $$
 ## {n_x+n_y-p-1) T^2 \over p(n_x+n_y-2)}
 ## $$
 ## @end tex
-## @end iftex
 ## @ifnottex
 ## @example
 ## (n_x+n_y-p-1) T^2 / (p(n_x+n_y-2))