Auto-generate citations and references.

git-svn-id: https://svn.r-project.org/R/trunk@88553 00db46b3-68df-0310-9c12-caf00c1e9a41

Author: hornik

src/library/stats/man/ARMAacf.Rd

@@ -1,6 +1,6 @@
 % File src/library/stats/man/ARMAacf.Rd
 % Part of the R package, https://www.R-project.org
-% Copyright 1995-2015 R Core Team
+% Copyright 1995-2025 R Core Team
 % Distributed under GPL 2 or later
 
 \name{ARMAacf}
@@ -23,7 +23,7 @@ ARMAacf(ar = numeric(), ma = numeric(), lag.max = r, pacf = FALSE)
 }
 \details{
   The methods used follow
-  \bibcite{Brockwell & Davis (1991, section 3.3)}.  Their
+  \bibcitet{|R:Brockwell+Davis:1991|section 3.3}.  Their
   equations (3.3.8) are solved for the autocovariances at lags
   \eqn{0, \dots, \max(p, q+1)}{0, \dots, max(p, q+1)},
   and the remaining autocorrelations are given by a recursive filter.
@@ -33,8 +33,7 @@ ARMAacf(ar = numeric(), ma = numeric(), lag.max = r, pacf = FALSE)
 }
 
 \references{
-  Brockwell, P. J. and Davis, R. A. (1991) \emph{Time Series: Theory and
-    Methods}, Second Edition.  Springer.
+  \bibshow{*}
 }
 \seealso{\code{\link{arima}}, \code{\link{ARMAtoMA}},
   \code{\link{acf2AR}} for inverting part of \code{ARMAacf}; further

src/library/stats/man/Beta.Rd

@@ -1,6 +1,6 @@
 % File src/library/stats/man/Beta.Rd
 % Part of the R package, https://www.R-project.org
-% Copyright 1995-2023 R Core Team
+% Copyright 1995-2025 R Core Team
 % Distributed under GPL 2 or later
 
 \name{Beta}
@@ -51,7 +51,7 @@ rbeta(n, shape1, shape2, ncp = 0)
   \code{[dpqr]beta()} functions are defined correspondingly.
 
   \code{pbeta} is closely related to the incomplete beta function.  As
-  defined by \bibcite{Abramowitz and Stegun 6.6.1}
+  defined by \bibcitet{|R:Abramowitz+Stegun:1972|section 6.6.1}
   \deqn{B_x(a,b) = \int_0^x t^{a-1} (1-t)^{b-1} dt,}{
     B_x(a,b) = integral_0^x t^(a-1) (1-t)^(b-1) dt,}
   and 6.6.2 \eqn{I_x(a,b) = B_x(a,b) / B(a,b)} where
@@ -154,13 +154,9 @@ rbeta(n, shape1, shape2, ncp = 0)
  }
 }
 \references{
-  Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988)
-  \emph{The New S Language}.
-  Wadsworth & Brooks/Cole.
-
-  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}
 
   Johnson, N. L., Kotz, S. and Balakrishnan, N. (1995)
   \emph{Continuous Univariate Distributions}, volume 2, especially

src/library/stats/man/Fdist.Rd

@@ -1,6 +1,6 @@
 % File src/library/stats/man/Fdist.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{FDist}
@@ -62,7 +62,8 @@ rf(n, df1, df2, ncp)
   for \eqn{x > 0}.
 
   The F distribution's cumulative distribution function (\abbr{cdf}),
-  \eqn{F_{\nu_1,\nu_2}}{F_{n1,n2}} fulfills (\bibcite{Abramowitz & Stegun 26.6.2, p.946})
+  \eqn{F_{\nu_1,\nu_2}}{F_{n1,n2}} fulfills
+  \bibcitep{|R:Abramowitz+Stegun:1972|section 26.6.2\\\\\\, page 946}
   \eqn{F_{\nu_1,\nu_2}(qF) = 1 - I_x(\nu_2/2, \nu_1/2) = I_{1-x}(\nu_1/2, \nu_2/2),}{%
        F_{n1,n2}(qF)   = 1 - I_x(n2/2, n1/2)   = I_{1-x}(n1/2, n2/2),}  where
   \eqn{x := \frac{\nu_2}{\nu_2 + \nu_1*qF}}{x := n2/(n2 + n1*qF)},  and
@@ -95,9 +96,7 @@ rf(n, df1, df2, ncp)
   else \emph{via} \code{\link{qbeta}}.
 }
 \references{
-  Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988)
-  \emph{The New S Language}.
-  Wadsworth & Brooks/Cole.
+  \bibshow{*, R:Becker+Chambers+Wilks:1988}
 
   Johnson, N. L., Kotz, S. and Balakrishnan, N. (1995)
   \emph{Continuous Univariate Distributions}, volume 2, chapters 27 and 30.

src/library/stats/man/KalmanLike.Rd

@@ -1,6 +1,6 @@
 % File src/library/stats/man/KalmanLike.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{KalmanLike}
@@ -86,7 +86,7 @@ makeARIMA(phi, theta, Delta, kappa = 1e6,
   It computes the covariance matrix of
   \eqn{(X_{t-1},...,X_{t-p},Z_t,...,Z_{t-q})}
   by the method of difference equations
-  (page 93 of \bibcite{Brockwell and Davis (1991)}),
+  (page 93 of \bibcitet{R:Brockwell+Davis:1991}),
   apparently suggested by a referee of Gardner \abbr{et al.}\sspace(see p.314 of
   their paper).
 }
@@ -122,20 +122,13 @@ makeARIMA(phi, theta, Delta, kappa = 1e6,
 }
 
 \references{
-  Brockwell, P. J. and Davis, R. A. (1991).
-  \emph{Time Series: Theory and Methods}, second edition.
-  Springer.
+  \bibshow{*,
+    R:Gardner+Harvey+Phillips:1980}
   
   Durbin, J. and Koopman, S. J. (2001).
   \emph{Time Series Analysis by State Space Methods}.
   Oxford University Press.
 
-  Gardner, G, Harvey, A. C. and Phillips, G. D. A. (1980).
-  Algorithm AS 154: An algorithm for exact maximum likelihood estimation
-  of  autoregressive-moving average models by means of Kalman filtering.
-  \emph{Applied Statistics}, \bold{29}, 311--322.
-  \doi{10.2307/2346910}.
-
   R bug report PR#14682 (2011-2013)
   \url{https://bugs.r-project.org/show_bug.cgi?id=14682}.
 }

src/library/stats/man/StructTS.Rd

@@ -1,6 +1,6 @@
 % File src/library/stats/man/StructTS.Rd
 % Part of the R package, https://www.R-project.org
-% Copyright 1995-2009 R Core Team
+% Copyright 1995-2025 R Core Team
 % Distributed under GPL 2 or later
 
 \name{StructTS}
@@ -98,11 +98,17 @@ StructTS(x, type = c("level", "trend", "BSM"), init = NULL,
     initial state of the filter, \code{model} its final state.}
   \item{xtsp}{the \code{tsp} attributes of \code{x}.}
 }
+\note{
+  Optimization of structural models is a lot harder than many of the
+  references admit.  For example, the \code{\link{AirPassengers}} data
+  are considered in \bibcitet{R:Brockwell+Davis:1996}: their solution appears to
+  be a local maximum, but nowhere near as good a fit as that produced by
+  \code{StructTS}.  It is quite common to find fits with one or more
+  variances zero, and this can include \eqn{\sigma^2_\epsilon}{sigma^2_eps}.
+}
 \references{
-  Brockwell, P. J. & Davis, R. A. (1996).
-  \emph{Introduction to Time Series and Forecasting}.
-  Springer, New York.
-  Sections 8.2 and 8.5.
+  \bibinfo{R:Brockwell+Davis:1996}{footer}{Sections 8.2 and 8.5.}
+  \bibshow{*}
 
   Durbin, J. and Koopman, S. J. (2001) \emph{Time Series Analysis by
     State Space Methods.}  Oxford University Press.
@@ -114,14 +120,6 @@ StructTS(x, type = c("level", "trend", "BSM"), init = NULL,
   Harvey, A. C. (1993) \emph{Time Series Models}.
   2nd Edition, Harvester Wheatsheaf.
 }
-\note{
-  Optimization of structural models is a lot harder than many of the
-  references admit.  For example, the \code{\link{AirPassengers}} data
-  are considered in \bibcite{Brockwell & Davis (1996)}: their solution appears to
-  be a local maximum, but nowhere near as good a fit as that produced by
-  \code{StructTS}.  It is quite common to find fits with one or more
-  variances zero, and this can include \eqn{\sigma^2_\epsilon}{sigma^2_eps}.
-}
 
 \seealso{
   \code{\link{KalmanLike}}, \code{\link{tsSmooth}};

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

@@ -1,6 +1,6 @@
 % File src/library/stats/man/ansari.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{ansari.test}
@@ -70,8 +70,8 @@ ansari.test(x, \dots)
   approximations.
 
   Note that mid-ranks are used in the case of ties rather than average
-  scores as employed in \bibcite{Hollander & Wolfe (1973)}.
-  See, e.g., \bibcite{Hajek, Sidak and Sen (1999), pages 131ff}, for
+  scores as employed in \bibcitet{R:Hollander+Wolfe:1973}.
+  See, e.g., \bibcitet{|R:Hajek+Sidak+Sen:1999|pages 131ff}, for
   more information.
 }
 \value{
@@ -98,20 +98,8 @@ ansari.test(x, \dots)
   for \eqn{s^2} in the F test.
 }
 \references{
-  David F. Bauer (1972).
-  Constructing confidence sets using rank statistics.
-  \emph{Journal of the American Statistical Association},
-  \bold{67}, 687--690.
-  \doi{10.1080/01621459.1972.10481279}.
-
-  Jaroslav Hajek, Zbynek Sidak and Pranab K. Sen (1999).
-  \emph{Theory of Rank Tests}.
-  San Diego, London: Academic Press.
-
-  Myles Hollander and Douglas A. Wolfe (1973).
-  \emph{Nonparametric Statistical Methods}.
-  New York: John Wiley & Sons.
-  Pages 83--92.
+  \bibinfo{R:Hollander+Wolfe:1973}{footer}{Pages 83--92.}
+  \bibshow{*, R:Bauer:1972}
 }
 \seealso{
   \code{\link{fligner.test}} for a rank-based (nonparametric)

src/library/stats/man/ar.Rd

@@ -1,6 +1,6 @@
 % File src/library/stats/man/ar.Rd
 % Part of the R package, https://www.R-project.org
-% Copyright 1995-2024 R Core Team
+% Copyright 1995-2025 R Core Team
 % Distributed under GPL 2 or later
 
 \name{ar}
@@ -108,11 +108,11 @@ ar.mle(x, aic = TRUE, order.max = NULL, na.action = na.fail,
   \code{ar.burg} allows two methods to estimate the innovations
   variance and hence AIC. Method 1 is to use the update given by
   the \I{Levinson}-\I{Durbin} recursion
-  (\bibcite{Brockwell and Davis, 1991, (8.2.6) on page 242}),
+  \bibcitep{|R:Brockwell+Davis:1991|(8.2.6) on page 242},
   and follows S-PLUS.  Method 2 is the mean of the sum
   of squares of the forward and backward prediction errors
-  (as in \bibcite{Brockwell and Davis, 1996, page 145}).
-  \bibcite{Percival and Walden (1998)} discuss both.
+  (as in \bibcitet{|R:Brockwell+Davis:1996|page 145}).
+  \bibcitet{R:Percival+Walden:1998} discuss both.
   In the multivariate case the estimated
   coefficients will depend (slightly) on the variance estimation method.
 
@@ -178,25 +178,9 @@ ar.mle(x, aic = TRUE, order.max = NULL, na.action = na.fail,
 }
 
 \references{
-  Brockwell, P. J. and Davis, R. A. (1991).
-  \emph{Time Series and Forecasting Methods}, second edition.
-  Springer, New York.
-  Section 11.4.
-
-  Brockwell, P. J. and Davis, R. A. (1996).
-  \emph{Introduction to Time Series and Forecasting}.
-  Springer, New York.
-  Sections 5.1 and 7.6.
-
-  Percival, D. P. and Walden, A. T. (1998).
-  \emph{Spectral Analysis for Physical Applications}.
-  Cambridge University Press.
-
-  Whittle, P. (1963).
-  On the fitting of multivariate autoregressions and the approximate
-  canonical factorization of a spectral density matrix.
-  \emph{Biometrika}, \bold{40}, 129--134.
-  \doi{10.2307/2333753}.
+  \bibinfo{R:Brockwell+Davis:1991}{footer}{Section 11.4.}
+  \bibinfo{R:Brockwell+Davis:1996}{footer}{Sections 5.1 and 7.6.}    
+  \bibshow{*, R:Whittle:1963}
 }
 
 \examples{

src/library/stats/man/bandwidth.Rd

@@ -1,6 +1,6 @@
 % File src/library/stats/man/bandwidth.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{bandwidth}
@@ -49,7 +49,8 @@ bw.SJ(x, nb = 1000, lower = 0.1 * hmax, upper = hmax,
   It defaults to 0.9 times the
   minimum of the standard deviation and the interquartile range divided by
   1.34 times the sample size to the negative one-fifth power
-  (= \I{Silverman}'s \sQuote{rule of thumb}, \bibcite{Silverman (1986, page 48, \abbr{eqn} (3.31))})
+  (= \I{Silverman}'s \sQuote{rule of thumb},
+  \bibcitet{|R:Silverman:1986|page 48\\\\\\, \\\\\\abbr{eqn} (3.31)})
   \emph{unless} the quartiles coincide when a positive result
   will be guaranteed.
 
@@ -59,7 +60,7 @@ bw.SJ(x, nb = 1000, lower = 0.1 * hmax, upper = hmax,
   \code{bw.ucv} and \code{bw.bcv} implement unbiased and
   biased cross-validation respectively.
 
-  \code{bw.SJ} implements the methods of \bibcite{Sheather & Jones (1991)}
+  \code{bw.SJ} implements the methods of \bibcitet{R:Sheather+Jones:1991}
   to select the bandwidth using pilot estimation of derivatives.\cr
   The algorithm for method \code{"ste"} solves an equation (via
   \code{\link{uniroot}}) and because of that, enlarges the interval
@@ -92,26 +93,12 @@ bw.SJ(x, nb = 1000, lower = 0.1 * hmax, upper = hmax,
   of \code{density} and so give answers four times as large.
 }
 \references{
+  \bibshow{*, R:Venables+Ripley:2002}
+  
   Scott, D. W. (1992)
   \emph{Multivariate Density Estimation: Theory, Practice, and
     Visualization.}
   New York: Wiley.
-
-  Sheather, S. J. and Jones, M. C. (1991).
-  A reliable data-based bandwidth selection method for kernel density
-  estimation.
-  \emph{Journal of the Royal Statistical Society Series B},
-  \bold{53}, 683--690.
-  \doi{10.1111/j.2517-6161.1991.tb01857.x}.
-  %% \url{https://www.jstor.org/stable/2345597}.
-
-  Silverman, B. W. (1986).
-  \emph{Density Estimation}.
-  London: Chapman and Hall.
-
-  Venables, W. N. and Ripley, B. D. (2002).
-  \emph{Modern Applied Statistics with S}.
-  Springer.
 }
 \examples{
 require(graphics)

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

@@ -1,6 +1,6 @@
 % File src/library/stats/man/binom.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{binom.test}
@@ -28,7 +28,7 @@ binom.test(x, n, p = 0.5,
 }
 \details{
   Confidence intervals are obtained by a procedure first given in
-  \bibcite{Clopper and Pearson (1934)}.
+  \bibcitet{R:Clopper+Pearson:1934}.
   This guarantees that the confidence level
   is at least \code{conf.level}, but in general does not give the
   shortest-length confidence intervals.
@@ -48,21 +48,13 @@ binom.test(x, n, p = 0.5,
   \item{data.name}{a character string giving the names of the data.}
 }
 \references{
-  Clopper, C. J. & Pearson, E. S. (1934).
-  The use of confidence or fiducial limits illustrated in the case of
-  the binomial.
-  \emph{Biometrika}, \bold{26}, 404--413.
-  \doi{10.2307/2331986}.
+  \bibinfo{R:Hollander+Wolfe:1973}{footer}{Pages 15--22.}
+  \bibshow{*, R:Hollander+Wolfe:1973}
 
   William J. Conover (1971),
   \emph{Practical nonparametric statistics}.
   New York: John Wiley & Sons.
   Pages 97--104.
-
-  Myles Hollander & Douglas A. Wolfe (1973),
-  \emph{Nonparametric Statistical Methods.}
-  New York: John Wiley & Sons.
-  Pages 15--22.
 }
 \seealso{
   \code{\link{prop.test}} for a general (approximate) test for equal or