updated package manual and toolbox installer

acpennlab · Jan 8, 2024 · c22960f · c22960f
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diff --git a/docs/description.json b/docs/description.json
@@ -8,7 +8,7 @@
     "version": "5.5.4",
     "description": "The statistics-resampling package is an Octave  package and Matlab toolbox that can be used to overcome a wide  variety of statistics problems using non-parametric resampling  methods. In particular, the functions included can be used to  estimate bias, uncertainty (standard errors and confidence  intervals), prediction error, and test hypotheses (p-values).  Variations of the resampling methods are included that improve  the accuracy of the statistics for small samples and samples  with complex dependence structures.",
     "shortdescription": "The statistics-resampling package is an Octave  package and Matlab toolbox that can be used to overcome a wide  variety of statistics problems using non-parametric resampling  methods",
-    "date": "2024-01-04",
+    "date": "2024-01-08",
     "title": "A statistics package with a variety of resampling tools",
     "author": "Andrew Penn <[email protected]>",
     "maintainer": "Andrew Penn <[email protected]>",

diff --git a/docs/function/boot.html b/docs/function/boot.html
@@ -205,6 +205,7 @@ <h3>Demonstration 4</h3>
 <div class="demo">
 <p>The following code</p>
 <pre class="example">
+
  % Vector (X) as input; balanced resampling with replacement; setting weights
  x = [23; 44; 36];
  boot(x, 10, false, 1)            % equal weighting
@@ -226,7 +227,7 @@ <h3>Demonstration 5</h3>
 <div class="demo">
 <p>The following code</p>
 <pre class="example">
- 
+
  % N as input; resampling without replacement; 3 trials
  boot(6, 1, false, 1) % Sample 1; Set random seed for first sample only
  boot(6, 1, false)    % Sample 2

diff --git a/docs/function/boot1way.html b/docs/function/boot1way.html
@@ -166,8 +166,7 @@ <h3>Demonstration 1</h3>
 <p>The following code</p>
 <pre class="example">
 
- ## EXAMPLE 1A: 
- ## ONE-WAY ANOVA WITH EQUAL SAMPLE SIZES: Treatment vs. Control (1)
+ % ONE-WAY ANOVA WITH EQUAL SAMPLE SIZES: Treatment vs. Control (1)
 
  y = [111.39 110.21  89.21  76.64  95.35  90.97  62.78;
       112.93  60.36  92.29  59.54  98.93  97.03  79.65;
@@ -180,7 +179,7 @@ <h3>Demonstration 1</h3>
 
  boot1way (y(:),g(:),'ref',1,'nboot',4999);
 
- ## Please be patient, the calculations will be completed soon...</pre>
+ % Please be patient, the calculations will be completed soon...</pre>
 <p>Produces the following output</p>
 <pre class="example">Summary of bootstrap multiple comparison tests in a one-way layout
 *****************************************************************************
@@ -226,8 +225,7 @@ <h3>Demonstration 2</h3>
 <p>The following code</p>
 <pre class="example">
 
- ## EXAMPLE 1B: 
- ## ROBUST ONE-WAY ANOVA WITH EQUAL SAMPLE SIZES: Treatment vs. Control (1)
+ % ROBUST ONE-WAY ANOVA WITH EQUAL SAMPLE SIZES: Treatment vs. Control (1)
 
  y = [111.39 110.21  89.21  76.64  95.35  90.97  62.78;
       112.93  60.36  92.29  59.54  98.93  97.03  79.65;
@@ -240,7 +238,7 @@ <h3>Demonstration 2</h3>
 
  boot1way (y(:), g(:), 'ref', 1, 'nboot', 4999, 'bootfun', 'robust');
 
- ## Please be patient, the calculations will be completed soon...</pre>
+ % Please be patient, the calculations will be completed soon...</pre>
 <p>Produces the following output</p>
 <pre class="example">Summary of bootstrap multiple comparison tests in a one-way layout
 *****************************************************************************
@@ -286,9 +284,8 @@ <h3>Demonstration 3</h3>
 <p>The following code</p>
 <pre class="example">
 
- ## EXAMPLE 2A:
- ## COMPARISON OF TWO INDEPENDENT GROUPS WITH UNEQUAL SAMPLE SIZES 
- ## (analagous to Student's t-test)
+ % COMPARISON OF TWO INDEPENDENT GROUPS WITH UNEQUAL SAMPLE SIZES 
+ % (analagous to Student's t-test)
 
  y =    [54       43
          23       34
@@ -305,7 +302,7 @@ <h3>Demonstration 3</h3>
 
  boot1way (y(:), g(:), 'ref', 'male', 'nboot', 4999);
 
- ## Please be patient, the calculations will be completed soon...</pre>
+ % Please be patient, the calculations will be completed soon...</pre>
 <p>Produces the following output</p>
 <pre class="example">Summary of bootstrap multiple comparison tests in a one-way layout
 *****************************************************************************
@@ -341,8 +338,7 @@ <h3>Demonstration 4</h3>
 <p>The following code</p>
 <pre class="example">
 
- ## EXAMPLE 2B:
- ## ONE-WAY ANOVA WITH UNEQUAL SAMPLE SIZES: pairwise comparisons
+ % ONE-WAY ANOVA WITH UNEQUAL SAMPLE SIZES: pairwise comparisons
 
  y = [54  87  45
       23  98  39
@@ -359,7 +355,7 @@ <h3>Demonstration 4</h3>
 
  boot1way (y(:), g(:), 'nboot', 4999);
 
- ## Please be patient, the calculations will be completed soon...</pre>
+ % Please be patient, the calculations will be completed soon...</pre>
 <p>Produces the following output</p>
 <pre class="example">Summary of bootstrap multiple comparison tests in a one-way layout
 *****************************************************************************
@@ -397,8 +393,7 @@ <h3>Demonstration 5</h3>
 <p>The following code</p>
 <pre class="example">
 
- ## EXAMPLE 2C:
- ## COMPARE STANDARD DEVIATIONS BETWEEN 3 GROUPS: pairwise comparisons
+ % COMPARE STANDARD DEVIATIONS BETWEEN 3 GROUPS: pairwise comparisons
 
  y = [54  87  45
       23  98  39
@@ -451,14 +446,13 @@ <h3>Demonstration 6</h3>
 <p>The following code</p>
 <pre class="example">
 
- ## EXAMPLE 3:
- ## COMPARE CORRELATION COEFFICIENTS BETWEEN 2 DATA SETS
+ % COMPARE CORRELATION COEFFICIENTS BETWEEN 2 DATA SETS
  Y = randn (20, 2); g = [zeros(10, 1); ones(10, 1)];
  func = @(M) cor (M(:,1), M(:,2));
 
  boot1way (Y, g, 'bootfun', func);
 
- ## Please be patient, the calculations will be completed soon...</pre>
+ % Please be patient, the calculations will be completed soon...</pre>
 <p>Produces the following output</p>
 <pre class="example">Summary of bootstrap multiple comparison tests in a one-way layout
 *****************************************************************************
@@ -473,7 +467,7 @@ <h3>Demonstration 6</h3>
 -----------------------------------------------------------------------------
 | Comparison |     Test # |      Ref # | Difference |          t |        p |
 |------------|------------|------------|------------|------------|----------|
-|          1 |          2 |          1 |    +0.3923 |      +0.77 |     .456 |
+|          1 |          2 |          1 |    +0.5212 |      +1.41 |     .135 |
 
 -----------------------------------------------------------------------------
 |    GROUP # |                                       GROUP label |        N |
@@ -494,16 +488,15 @@ <h3>Demonstration 7</h3>
 <p>The following code</p>
 <pre class="example">
 
- ## EXAMPLE 4:
- ## COMPARE SLOPES FROM LINEAR REGRESSION ON 2 DATA SETS
+ % COMPARE SLOPES FROM LINEAR REGRESSION ON 2 DATA SETS
  y = randn (20, 1); x = randn (20, 1); X = [ones(20, 1), x];
  g = [zeros(10, 1); ones(10, 1)];
  func = @(M) subsref (M(:,2:end) \ M(:,1), ...
                       struct ('type', '()', 'subs', {{2}}));
 
  boot1way ([y, X], g, 'bootfun', func);
 
- ## Please be patient, the calculations will be completed soon...</pre>
+ % Please be patient, the calculations will be completed soon...</pre>
 <p>Produces the following output</p>
 <pre class="example">Summary of bootstrap multiple comparison tests in a one-way layout
 *****************************************************************************
@@ -518,7 +511,7 @@ <h3>Demonstration 7</h3>
 -----------------------------------------------------------------------------
 | Comparison |     Test # |      Ref # | Difference |          t |        p |
 |------------|------------|------------|------------|------------|----------|
-|          1 |          2 |          1 |    +0.8151 |      +0.83 |     .376 |
+|          1 |          2 |          1 |    -0.1399 |      -0.26 |     .640 |
 
 -----------------------------------------------------------------------------
 |    GROUP # |                                       GROUP label |        N |

diff --git a/docs/function/bootbayes.html b/docs/function/bootbayes.html
@@ -165,13 +165,13 @@ <h3>Demonstration 1</h3>
 <p>The following code</p>
 <pre class="example">
 
- ## Input univariate dataset
+ % Input univariate dataset
  heights = [183, 192, 182, 183, 177, 185, 188, 188, 182, 185].';
 
- ## 95% credible interval for the mean 
- bootbayes(heights);
+ % 95% credible interval for the mean 
+ bootbayes (heights);
 
- ## Please be patient, the calculations will be completed soon...</pre>
+ % Please be patient, the calculations will be completed soon...</pre>
 <p>Produces the following output</p>
 <pre class="example">Summary of Bayesian bootstrap estimates of bias and precision for linear models
 *******************************************************************************
@@ -186,14 +186,14 @@ <h3>Demonstration 1</h3>
 
 Posterior Statistics: 
  original     bias         median       stdev       CI_lower      CI_upper
- +184.5       +0.005727    +184.5       1.273       +182.2        +187.2</pre>
+ +184.5       +0.03166     +184.5       1.296       +182.0        +187.0</pre>
 </div>
 <h3>Demonstration 2</h3>
 <div class="demo">
 <p>The following code</p>
 <pre class="example">
 
- ## Input bivariate dataset
+ % Input bivariate dataset
  X = [ones(43,1),...
      [01,02,03,04,05,06,07,08,09,10,11,...
       12,13,14,15,16,17,18,19,20,21,22,...
@@ -204,10 +204,10 @@ <h3>Demonstration 2</h3>
      168.0,170.0,178.0,182.0,180.0,183.0,178.0,182.0,188.0,175.0,179.0,...
      183.0,192.0,182.0,183.0,177.0,185.0,188.0,188.0,182.0,185.0]';
 
- ## 95% credible interval for the regression coefficents
- bootbayes(y,X);
+ % 95% credible interval for the regression coefficents
+ bootbayes (y, X);
 
- ## Please be patient, the calculations will be completed soon...</pre>
+ % Please be patient, the calculations will be completed soon...</pre>
 <p>Produces the following output</p>
 <pre class="example">Summary of Bayesian bootstrap estimates of bias and precision for linear models
 *******************************************************************************
@@ -222,8 +222,8 @@ <h3>Demonstration 2</h3>
 
 Posterior Statistics: 
  original     bias         median       stdev       CI_lower      CI_upper
- +175.5       -0.09008     +175.4       2.407       +170.8        +180.3    
- +0.1904      +0.002195    +0.1946      0.07956     +0.02477      +0.3431</pre>
+ +175.5       -0.07101     +175.4       2.403       +170.6        +179.7    
+ +0.1904      +0.001505    +0.1921      0.07932     +0.04185      +0.3468</pre>
 </div>
 
 <p>Package: <a href="../index.html">statistics-resampling</a></p>