made minor changes and corrections to some function documentation

Author: acp29

DESCRIPTION

@@ -1,6 +1,6 @@
 name: statistics-resampling
-version: 5.5.13
-date: 2024-05-17
+version: 5.5.14
+date: 2024-05-19
 author: Andrew Penn <andy.c.penn@gmail.com>
 maintainer: Andrew Penn <andy.c.penn@gmail.com>
 title: A statistics package with a variety of resampling tools

inst/bootint.m

@@ -1,23 +1,25 @@
-% Computes simple percentile confidence interval(s) from a vector (or row-major
-% matrix) of bootstrap statistics.
+% Computes simple percentile confidence interval(s) directly from a vector (or
+% row-major matrix) of bootstrap statistics.
 %
-% -- Function File: CI = bootint (Y)
-% -- Function File: CI = bootint (Y, PROB)
+% -- Function File: CI = bootint (BOOTSTAT)
+% -- Function File: CI = bootint (BOOTSTAT, PROB)
 %
-%     'CI = bootint (Y)' computes 95% percentile confidence intervals from
-%     the vector, or rows* of the matrix, Y, where Y contains bootstrap
-%     statistics, such as those generated using the `bootstrp` function.
-%     Depending on the application, bootstrap confidence intervals with better
-%     coverage and accuracy can be computed using the various dedicated
-%     bootstrap functions from the statistics-resampling package.
+%     'CI = bootint (BOOTSTAT)' computes 95% percentile confidence intervals
+%     directly from the vector, or rows* of the matrix in BOOTSTAT, where
+%     BOOTSTAT contains bootstrap statistics such as those generated using
+%     the `bootstrp` function. Depending on the application, bootstrap
+%     confidence intervals with better coverage and accuracy can be computed
+%     using the various dedicated bootstrap functions from the statistics-
+%     resampling package.
 %
 %        * The matrix should have dimensions P * NBOOT, where P corresponds to
 %          the number of parameter estimates and NBOOT corresponds to the number
 %          of bootstrap samples.
 %
-%     'CI = bootint (Y, PROB)' returns confidence intervals, where PROB is
-%     numeric and sets the lower and upper bounds of the confidence interval(s).
-%     The value(s) of PROB must be between 0 and 1. PROB can either be:
+%     'CI = bootint (BOOTSTAT, PROB)' returns confidence intervals, where
+%     PROB is numeric and sets the lower and upper bounds of the confidence
+%     interval(s). The value(s) of PROB must be between 0 and 1. PROB can
+%     either be:
 %       <> scalar: To set the central mass of normal confidence intervals
 %                  to 100*PROB%
 %       <> vector: A pair of probabilities defining the lower and upper

inst/bootknife.m

@@ -790,7 +790,7 @@
             ci(j, :) = parsubfun.kdeinv (l(j, :), bootstat(j, :), ...
                                    se(j) * sqrt (1 / (n - K)), 1 - 1 / (n - K));
           else
-            error ('Invoke linear interpolation method.')
+            error ('bootknife: Invoke linear interpolation method.')
           end
         catch
           % Linear interpolation (legacy) when LOO is false and for corner cases

inst/credint.m

@@ -1,23 +1,24 @@
-% Computes credible interval(s) from a vector (or row-major matrix) of the
-% posterior(s) obtained by bayesian bootstrap.
+% Computes credible interval(s) directly from a vector (or row-major matrix) of
+% the posterior(s) obtained by bayesian bootstrap.
 %
-% -- Function File: CI = credint (Y)
-% -- Function File: CI = credint (Y, PROB)
+% -- Function File: CI = credint (BOOTSTAT)
+% -- Function File: CI = credint (BOOTSTAT, PROB)
 %
-%     'CI = credint (Y)' computes 95% credible intervals from the vector, or
-%     rows* of the matrix, Y, where Y contains posterior (or Bayesian bootstrap)
-%     statistics, such as those generated using the `bootbayes` function. The
-%     credible intervals are shortest probability intervals (SPI), which
-%     represent a more computationally stable version of the highest posterior
-%     density interval [1,2].
+%     'CI = credint (BOOTSTAT)' computes 95% credible intervals directly from
+%     the vector, or rows* of the matrix in BOOTSTAT, where BOOTSTAT contains
+%     posterior (or Bayesian bootstrap) statistics, such as those generated
+%     using the `bootbayes` function (or the `bootlm` function with the method
+%     set to 'bayesian'). The credible intervals are shortest probability
+%     intervals (SPI), which represent a more computationally stable version
+%     of the highest posterior density interval [1,2].
 %
 %        * The matrix should have dimensions P * NBOOT, where P corresponds to
 %          the number of parameter estimates and NBOOT corresponds to the number
 %          of posterior (or Bayesian bootstrap) samples.
 %
-%     'CI = credint (Y, PROB)' returns credible intervals, where PROB is numeric
-%     and sets the lower and upper bounds of the credible interval(s). The
-%     value(s) of PROB must be between 0 and 1. PROB can either be:
+%     'CI = credint (BOOTSTAT, PROB)' returns credible intervals, where PROB is
+%     numeric and sets the lower and upper bounds of the credible interval(s).
+%     The value(s) of PROB must be between 0 and 1. PROB can either be:
 %       <> scalar: To set the central mass of shortest probability intervals
 %                  to 100*PROB%
 %       <> vector: A pair of probabilities defining the lower and upper

inst/smoothmedian.m

@@ -8,10 +8,9 @@
 %     matrix, compute the univariate smoothed median value for each column and
 %     return them in a row vector.  If the optional argument DIM is given,
 %     operate along this dimension. Arrays of more than two dimensions are not
-%     NaN values currently supported. The MEX file versions of this function
-%     ignore (omit) whereas the m-file includes NaN in it's calculations. Use
-%     the 'which' command to establish which version of the function is being
-%     used.
+%     currently supported. The MEX file versions of this function ignore (omit)
+%     NaN values whereas the m-file includes NaN in it's calculations. Use the
+%     'which' command to establish which version of the function is being used.
 %
 %     The smoothed median is a slightly smoothed version of the ordinary median
 %     and is an M-estimator that is both robust and efficient: