From f8b9ec9666ef4c368f4b91d256673efdec0541db Mon Sep 17 00:00:00 2001 From: Ben Pfaff Date: Sun, 4 May 2014 10:30:05 -0700 Subject: [PATCH] doc: Comment out documentation for functions that are not implemented. Reported by John Darrington . --- doc/expressions.texi | 66 ++++++++++++++++++++++---------------------- 1 file changed, 33 insertions(+), 33 deletions(-) diff --git a/doc/expressions.texi b/doc/expressions.texi index 77908e35ed..184589cba2 100644 --- a/doc/expressions.texi +++ b/doc/expressions.texi @@ -1215,12 +1215,12 @@ Cauchy distribution with location parameter @var{a} and scale parameter @var{b}. Constraints: @var{b} > 0, 0 < @var{p} < 1. @end deftypefn -@deftypefn {Function} {} PDF.CHISQ (@var{x}, @var{df}) -@deftypefnx {Function} {} CDF.CHISQ (@var{x}, @var{df}) +@c @deftypefn {Function} {} PDF.CHISQ (@var{x}, @var{df}) +@deftypefn {Function} {} CDF.CHISQ (@var{x}, @var{df}) @deftypefnx {Function} {} SIG.CHISQ (@var{x}, @var{df}) @deftypefnx {Function} {} IDF.CHISQ (@var{p}, @var{df}) @deftypefnx {Function} {} RV.CHISQ (@var{df}) -@deftypefnx {Function} {} NPDF.CHISQ (@var{x}, @var{df}, @var{lambda}) +@c @deftypefnx {Function} {} NPDF.CHISQ (@var{x}, @var{df}, @var{lambda}) @deftypefnx {Function} {} NCDF.CHISQ (@var{x}, @var{df}, @var{lambda}) Chi-squared distribution with @var{df} degrees of freedom. The noncentral distribution takes an additional parameter @var{lambda}. @@ -1250,8 +1250,8 @@ and nonnegative power parameter @var{b}. Constraints: @var{a} > 0, @deftypefnx {Function} {} SIG.F (@var{x}, @var{df1}, @var{df2}) @deftypefnx {Function} {} IDF.F (@var{p}, @var{df1}, @var{df2}) @deftypefnx {Function} {} RV.F (@var{df1}, @var{df2}) -@deftypefnx {Function} {} NPDF.F (@var{x}, @var{df1}, @var{df2}, @var{lambda}) -@deftypefnx {Function} {} NCDF.F (@var{x}, @var{df1}, @var{df2}, @var{lambda}) +@c @deftypefnx {Function} {} NPDF.F (@var{x}, @var{df1}, @var{df2}, @var{lambda}) +@c @deftypefnx {Function} {} NCDF.F (@var{x}, @var{df1}, @var{df2}, @var{lambda}) F-distribution of two chi-squared deviates with @var{df1} and @var{df2} degrees of freedom. The noncentral distribution takes an additional parameter @var{lambda}. Constraints: @var{df1} > 0, @@ -1267,21 +1267,21 @@ Gamma distribution with shape parameter @var{a} and scale parameter @var{p} < 1. @end deftypefn -@deftypefn {Function} {} PDF.HALFNRM (@var{x}, @var{a}, @var{b}) -@deftypefnx {Function} {} CDF.HALFNRM (@var{x}, @var{a}, @var{b}) -@deftypefnx {Function} {} IDF.HALFNRM (@var{p}, @var{a}, @var{b}) -@deftypefnx {Function} {} RV.HALFNRM (@var{a}, @var{b}) -Half-normal distribution with location parameter @var{a} and shape -parameter @var{b}. Constraints: @var{b} > 0, 0 < @var{p} < 1. -@end deftypefn +@c @deftypefn {Function} {} PDF.HALFNRM (@var{x}, @var{a}, @var{b}) +@c @deftypefnx {Function} {} CDF.HALFNRM (@var{x}, @var{a}, @var{b}) +@c @deftypefnx {Function} {} IDF.HALFNRM (@var{p}, @var{a}, @var{b}) +@c @deftypefnx {Function} {} RV.HALFNRM (@var{a}, @var{b}) +@c Half-normal distribution with location parameter @var{a} and shape +@c parameter @var{b}. Constraints: @var{b} > 0, 0 < @var{p} < 1. +@c @end deftypefn -@deftypefn {Function} {} PDF.IGAUSS (@var{x}, @var{a}, @var{b}) -@deftypefnx {Function} {} CDF.IGAUSS (@var{x}, @var{a}, @var{b}) -@deftypefnx {Function} {} IDF.IGAUSS (@var{p}, @var{a}, @var{b}) -@deftypefnx {Function} {} RV.IGAUSS (@var{a}, @var{b}) -Inverse Gaussian distribution with parameters @var{a} and @var{b}. -Constraints: @var{a} > 0, @var{b} > 0, @var{x} > 0, 0 <= @var{p} < 1. -@end deftypefn +@c @deftypefn {Function} {} PDF.IGAUSS (@var{x}, @var{a}, @var{b}) +@c @deftypefnx {Function} {} CDF.IGAUSS (@var{x}, @var{a}, @var{b}) +@c @deftypefnx {Function} {} IDF.IGAUSS (@var{p}, @var{a}, @var{b}) +@c @deftypefnx {Function} {} RV.IGAUSS (@var{a}, @var{b}) +@c Inverse Gaussian distribution with parameters @var{a} and @var{b}. +@c Constraints: @var{a} > 0, @var{b} > 0, @var{x} > 0, 0 <= @var{p} < 1. +@c @end deftypefn @deftypefn {Function} {} PDF.LANDAU (@var{x}) @deftypefnx {Function} {} RV.LANDAU () @@ -1376,26 +1376,26 @@ parameter @var{sigma}. This distribution is a @pspp{} extension. Constraints: @var{a} > 0, @var{sigma} > 0, @var{x} > @var{a}. @end deftypefn -@deftypefn {Function} {} CDF.SMOD (@var{x}, @var{a}, @var{b}) -@deftypefnx {Function} {} IDF.SMOD (@var{p}, @var{a}, @var{b}) -Studentized maximum modulus distribution with parameters @var{a} and -@var{b}. Constraints: @var{a} > 0, @var{b} > 0, @var{x} > 0, 0 <= -@var{p} < 1. -@end deftypefn +@c @deftypefn {Function} {} CDF.SMOD (@var{x}, @var{a}, @var{b}) +@c @deftypefnx {Function} {} IDF.SMOD (@var{p}, @var{a}, @var{b}) +@c Studentized maximum modulus distribution with parameters @var{a} and +@c @var{b}. Constraints: @var{a} > 0, @var{b} > 0, @var{x} > 0, 0 <= +@c @var{p} < 1. +@c @end deftypefn -@deftypefn {Function} {} CDF.SRANGE (@var{x}, @var{a}, @var{b}) -@deftypefnx {Function} {} IDF.SRANGE (@var{p}, @var{a}, @var{b}) -Studentized range distribution with parameters @var{a} and @var{b}. -Constraints: @var{a} >= 1, @var{b} >= 1, @var{x} > 0, 0 <= @var{p} < -1. -@end deftypefn +@c @deftypefn {Function} {} CDF.SRANGE (@var{x}, @var{a}, @var{b}) +@c @deftypefnx {Function} {} IDF.SRANGE (@var{p}, @var{a}, @var{b}) +@c Studentized range distribution with parameters @var{a} and @var{b}. +@c Constraints: @var{a} >= 1, @var{b} >= 1, @var{x} > 0, 0 <= @var{p} < +@c 1. +@c @end deftypefn @deftypefn {Function} {} PDF.T (@var{x}, @var{df}) @deftypefnx {Function} {} CDF.T (@var{x}, @var{df}) @deftypefnx {Function} {} IDF.T (@var{p}, @var{df}) @deftypefnx {Function} {} RV.T (@var{df}) -@deftypefnx {Function} {} NPDF.T (@var{x}, @var{df}, @var{lambda}) -@deftypefnx {Function} {} NCDF.T (@var{x}, @var{df}, @var{lambda}) +@c @deftypefnx {Function} {} NPDF.T (@var{x}, @var{df}, @var{lambda}) +@c @deftypefnx {Function} {} NCDF.T (@var{x}, @var{df}, @var{lambda}) T-distribution with @var{df} degrees of freedom. The noncentral distribution takes an additional parameter @var{lambda}. Constraints: @var{df} > 0, 0 < @var{p} < 1. -- 2.30.2