remove some trailing whitespace

.github/workflows/macos.yml

@@ -33,7 +33,7 @@ jobs:
           echo "{~/bin}" >> $GITHUB_PATH
           which perl
           which cpanm
-        
+
       - name: perl -V
         run: perl -V
 
@@ -54,7 +54,6 @@ jobs:
       - name: Install Dynamic Dependencies
         run: |
           cpanm --notest --installdeps .
-      
+
       - name: Run Tests
         run: prove -l -j4
-

.github/workflows/windows.yml

@@ -29,10 +29,9 @@ jobs:
           echo "C:\strawberry\c\bin;C:\strawberry\perl\site\bin;C:\strawberry\perl\bin" | Out-File -FilePath $env:GITHUB_PATH -Encoding utf8 -Append
           ##echo $env:PATH
 
-    
       - name: perl -V
         run: perl -V
-        
+
       - name: Prepare for CPAN cache
         run: |
           perl -V > perlversion.txt
@@ -47,15 +46,14 @@ jobs:
           restore-keys: |
             ${{ runner.os }}-build-${{ hashFiles('perlversion.txt') }}
 
-          
+
       - name: Install task deps
         run: |
           cpanm --notest --no-man-pages PDL
           cpanm --notest --installdeps .
           cpanm --installdeps .
-  
+
       # Runs a set of commands using the runners shell
       - name: Run tests
         run: |
           prove -l t
-          

Changes

@@ -8,7 +8,7 @@ Revision history for Statistics-Descriptive-PDL
         - SampleWeighted:
           - Return undef for skewness and kurtosis
             when insufficient samples to apply correction.
-        - Avoid method call on scalar in weighted geometric mean 
+        - Avoid method call on scalar in weighted geometric mean
 
 0.15    2022-04-24
         - Fix weighted geometric_mean for larger data samples

lib/Statistics/Descriptive/PDL/SampleWeighted.pm

@@ -201,9 +201,9 @@ This module provides basic functions used in descriptive statistics
 using weighted values.  Inherits from L<Statistics::Descriptive::PDL::Weighted>,
 with the key difference that the weights are forced to be integers.
 
-Variance, skewness and kurtosis all use the unbiased calculations.  
+Variance, skewness and kurtosis all use the unbiased calculations.
 The median and percentiles are calculated using interpolation,
-analogous to the unweighted case where values are repeated by the weights.  
+analogous to the unweighted case where values are repeated by the weights.
 
 
 =head1 METHODS
@@ -212,7 +212,7 @@ analogous to the unweighted case where values are repeated by the weights.
 
 =item new
 
-Create a new statistics object.  Takes no arguments.  
+Create a new statistics object.  Takes no arguments.
 
 =item add_data (\%data)
 

lib/Statistics/Descriptive/PDL/Weighted.pm

@@ -440,7 +440,7 @@ Version 0.11
 =head1 DESCRIPTION
 
 This module provides basic functions used in descriptive statistics
-using weighted values.  
+using weighted values.
 
 
 =head1 METHODS
@@ -449,7 +449,7 @@ using weighted values.
 
 =item new
 
-Create a new statistics object.  Takes no arguments.  
+Create a new statistics object.  Takes no arguments.
 
 =item add_data (\%data)
 
@@ -480,7 +480,7 @@ In scalar context returns an array of arrays, i.e. C<[\@data,\@wts]>.
 Returns the data as a perl hash, with the data values as the hash keys and weights as the hash values.
 Deduplicates the data if needed, incrementing the weights as appropriate.
 
-Data values are stringified so there is obviously potential for loss of precision.  
+Data values are stringified so there is obviously potential for loss of precision.
 
 Returns a hash ref in scalar context.
 
@@ -504,16 +504,16 @@ Sum of the squared weights vector.  Each weight is squared and the sum of these
 =item Statistical methods
 
 Most of the methods should need no explanation here,
-except to note that the standard_deviation, skewness and kurtosis 
+except to note that the standard_deviation, skewness and kurtosis
 use the biased methods.  This is because one cannot guarantee the data are sample counts.
 The same applies to the median and percentiles.  The median uses a centre of mass calculation, and the
 percentiles using analogous approach.  This is because the weights are not guaranteed
 to be integers and so there is no sense interpolating.
 
 Use L<Statistics::Descriptive::PDL::SampleWeighted> when your weights are counts
-and you need the unbiased methods.  
+and you need the unbiased methods.
 
-The iqr is the inter-quartile range, calculated as the difference of the 75th and 25th percentiles. 
+The iqr is the inter-quartile range, calculated as the difference of the 75th and 25th percentiles.
 
 =item geometric_mean
 
@@ -537,7 +537,7 @@ The iqr is the inter-quartile range, calculated as the difference of the 75th an
 
 =item variance
 
-=item count 
+=item count
 
 =item skewness