Auto-generate citations and references.

git-svn-id: https://svn.r-project.org/R/trunk@88557 00db46b3-68df-0310-9c12-caf00c1e9a41

Author: hornik

src/library/stats/man/GammaDist.Rd

@@ -94,9 +94,9 @@ rgamma(n, shape, rate = 1, scale = 1/rate)
   and \code{rate}: S had no \code{scale} parameter. It is an error
   to supply both \code{scale} and \code{rate}.
 
-  \code{pgamma} is closely related to the incomplete gamma function.  As
-  defined by \bibcite{Abramowitz and Stegun 6.5.1} (and by \sQuote{Numerical
-    Recipes}) this is
+  \code{pgamma} is closely related to the incomplete gamma function.
+  As defined by \bibcitet{|R:Abramowitz+Stegun:1972|section 6.5.1}
+  (and by \sQuote{Numerical Recipes}) this is
   \deqn{P(a,x) = \frac{1}{\Gamma(a)} \int_0^x t^{a-1} e^{-t} dt}{P(a,x) = 1/Gamma(a) integral_0^x t^(a-1) exp(-t) dt}
   \eqn{P(a, x)} is \code{pgamma(x, a)}.  Other authors (for example
   Karl Pearson in his 1922 tables) omit the normalizing factor,
@@ -125,12 +125,8 @@ rgamma(n, shape, rate = 1, scale = 1/rate)
   algorithm \sQuote{mainly by Morten Welinder}.
 
   \code{qgamma} is based on a C translation of
-
-  Best, D. J. and D. E. Roberts (1975).
-  Algorithm AS91. Percentage points of the chi-squared distribution.
-  \emph{Applied Statistics},  \bold{24}, 385--388.
-
-  plus a final Newton step to improve the approximation.
+  \bibcitet{R:Best+Roberts:1975} plus a final Newton step to improve the
+  approximation.
 
   \code{rgamma} for \code{shape >= 1} uses
 
@@ -145,18 +141,10 @@ rgamma(n, shape, rate = 1, scale = 1/rate)
   distributions. \emph{Computing}, \bold{12}, 223--246.
 }
 \references{
-  Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988).
-  \emph{The New S Language}.
-  Wadsworth & Brooks/Cole.
-
-  Shea, B. L. (1988).
-  Algorithm AS 239: Chi-squared and incomplete Gamma integral,
-  \emph{Applied Statistics (JRSS C)}, \bold{37}, 466--473.
-  \doi{10.2307/2347328}.
-
-  Abramowitz, M. and Stegun, I. A. (1972)
-  \emph{Handbook of Mathematical Functions.} New York: Dover.
-  Chapter 6: Gamma and Related Functions.
+  \bibinfo{R:Abramowitz+Stegun:1972}{footer}{Chapter 6: Gamma and Related Functions.}
+  \bibshow{*,
+    R:Becker+Chambers+Wilks:1988,
+    R:Shea:1988}
 
   NIST Digital Library of Mathematical Functions.
   \url{https://dlmf.nist.gov/}, section 8.2.

src/library/stats/man/fligner.test.Rd

@@ -1,6 +1,6 @@
 % File src/library/stats/man/fligner.test.Rd
 % Part of the R package, https://www.R-project.org
-% Copyright 1995-2018 R Core Team
+% Copyright 1995-2025 R Core Team
 % Distributed under GPL 2 or later
 
 \name{fligner.test}
@@ -52,7 +52,7 @@ fligner.test(x, \dots)
   The \I{Fligner}-\I{Killeen} (median) test has been determined in a simulation
   study as one of the many tests for homogeneity of variances which is
   most robust against departures from normality, see
-  \bibcite{Conover, Johnson & Johnson (1981)}.
+  \bibcitet{R:Conover+Johnson+Johnson:1981}.
   It is a \eqn{k}-sample simple linear rank which uses
   the ranks of the absolute values of the centered samples and weights
   \eqn{a(i) = \mathrm{qnorm}((1 + i/(n+1))/2)}{a(i) = qnorm((1 +
@@ -70,11 +70,7 @@ fligner.test(x, \dots)
   \item{data.name}{a character string giving the names of the data.}
 }
 \references{
-  William J. Conover, Mark E. Johnson and Myrle M. Johnson (1981).
-  A comparative study of tests for homogeneity of variances, with
-  applications to the outer continental shelf bidding data.
-  \emph{Technometrics}, \bold{23}, 351--361.
-  \doi{10.2307/1268225}.
+  \bibshow{*}
 }
 \seealso{
   \code{\link{ansari.test}} and \code{\link{mood.test}} for rank-based

src/library/stats/man/influence.measures.Rd

@@ -1,6 +1,6 @@
 % File src/library/stats/man/influence.measures.Rd
 % Part of the R package, https://www.R-project.org
-% Copyright 1995-2022 R Core Team
+% Copyright 1995-2025 R Core Team
 % Distributed under GPL 2 or later
 
 \name{influence.measures}
@@ -99,7 +99,7 @@ hat(x, intercept = TRUE)
 \description{
   This suite of functions can be used to compute some of the regression
   (leave-one-out deletion) diagnostics for linear and generalized linear
-  models discussed in \bibcite{Belsley, Kuh and Welsch (1980)},
+  models discussed in \bibcitet{R:Belsley+Kuh+Welsch:1980},
   \bibcite{Cook and Weisberg (1982)}, etc.
 }
 \details{
@@ -122,7 +122,7 @@ hat(x, intercept = TRUE)
   or matrices instead of vectors.
 
   Values for generalized linear models are approximations, as described
-  in Williams (1987) (except that Cook's distances are scaled as
+  in \bibcitet{R:Williams:1987} (except that Cook's distances are scaled as
   \eqn{F} rather than as chi-square values).  The approximations can be
   poor when some cases have large influence.
 
@@ -154,20 +154,12 @@ hat(x, intercept = TRUE)
   package.
 }
 \references{
-  Belsley, D. A., Kuh, E. and Welsch, R. E. (1980).
-  \emph{Regression Diagnostics}.
-  New York: Wiley.
-
+  \bibshow{*}
+  
   Cook, R. D. and Weisberg, S. (1982).
   \emph{Residuals and Influence in Regression}.
   London: Chapman and Hall.
 
-  Williams, D. A. (1987).
-  Generalized linear model diagnostics using the deviance and single
-  case deletions.
-  \emph{Applied Statistics}, \bold{36}, 181--191.
-  \doi{10.2307/2347550}.
-
   Fox, J. (1997).
   \emph{Applied Regression, Linear Models, and Related Methods}.
   Sage.