update online documentation for release-1.7.7

Author: pr0m1th3as

NEWS

@@ -251,7 +251,7 @@
  ** ztest2: improve input validation (#226)
 
 
- Summary of important user-visible changes for statistics 1.7.6:
+ Summary of important user-visible changes for statistics 1.7.7:
 -------------------------------------------------------------------
 
  Important Notice: 1) `mad`, `mean`, `median`, `std`, `var` functions

docs/BetaDistribution.html

@@ -1891,10 +1891,10 @@ <h3 class="d-inline-block mr-2">
   BetaDistribution
 
   beta distribution
-        a = 2.29841   [1.78335, 2.96222]
-        b = 5.28788   [4.23907, 6.59618]
+        a = 2.37491   [1.76557, 3.19455]
+        b = 6.23768   [4.57955, 8.49618]
 
-nlogL = 48.645
+nlogL = 57.538
                     </pre></td></tr></tbody>
                   </table>
 
@@ -1996,13 +1996,13 @@ <h3 class="d-inline-block mr-2">
   BetaDistribution
 
   beta distribution
-        a = 1.93585   [1.78762, 2.09637]
-        b = 4.91037   [4.49283, 5.36672]
+        a = 1.89199   [1.7412, 2.05584]
+        b =  4.8405   [4.43529, 5.28274]
 
 ci =
 
-   1.7876   4.4928
-   2.0964   5.3667
+   1.7412   4.4353
+   2.0558   5.2827
 
                     </pre></td></tr></tbody>
                   </table>

docs/ExhaustiveSearcher.html

@@ -1181,11 +1181,11 @@ <h4 class="d-inline-block my-3">
 
 Columns 1 through 1:
 
-    {[5 6 21 46 48 44 37 22 1 8 11 27 26 33 18 12 39 7 40 49 32 47 19 3 28 29 17 20 43 23 42 16 30 4 56 34 85 24 10 51 ... ]}    
-    {[98 95 83 88 78 89 55 63 69 91 94 90 82 92 59 80 100 138 131 57 66 67 81 61 125 102 115 145 134 96 110 118 126 93 ... ]}    
-    {[  104 128 127 106 117 123 58 52 76 50 107 86 74 101 65 75 77 131 96 72 54 73 115 129 51 63 67 91 71 87 110 92 62 ... ]}    
-    {[ 135 65 75 101 111 117 77 54 50 86 137 52 76 119 141 128 74 71 121 142 143 136 58 124 51 109 110 104 68 87 96 31 ... ]}    
-    {[ 102 115 91 94 63 56 145 83 88 95 55 131 66 85 122 78 70 103 125 73 98 84 134 123 51 90 126 67 100 138 61 96 139 ... ]}    
+    {[  5 6 21 46 48 44 37 22 1 8 11 27 26 33 18 12 39 7 40 49 32 47 19 3 28 29 17 20 43 23 42 16 30 4 56 34 ... ]}    
+    {[ 98 95 83 88 78 89 55 63 69 91 94 90 82 92 59 80 100 138 131 57 66 67 81 61 125 102 115 145 134 96 110 ... ]}    
+    {[  104 128 127 106 117 123 58 52 76 50 107 86 74 101 65 75 77 131 96 72 54 73 115 129 51 63 67 91 71 87 ... ]}    
+    {[135 65 75 101 111 117 77 54 50 86 137 52 76 119 141 128 74 71 121 142 143 136 58 124 51 109 110 104 68 ... ]}    
+    {[ 102 115 91 94 63 56 145 83 88 95 55 131 66 85 122 78 70 103 125 73 98 84 134 123 51 90 126 67 100 138 ... ]}    
 </pre></td></tr>
                   </table>
 
@@ -1391,29 +1391,29 @@ <h4 class="d-inline-block my-3">
 For the first query point in rangesearch:
 Query point: 0.3         0.3
 Indices of points within radius:
- Columns 1 through 25:
+ Columns 1 through 23:
 
-   49   19   14   46   34   23   12   41    1    3   42   48   24    2   11   37   10   27   32   20   44   40   39   21    7
+   49   19   14   46   34   23   12   41    1    3   42   48   24    2   11   37   10   27   32   20   44   40   39
 
- Columns 26 through 34:
+ Columns 24 through 34:
 
-   31   45   30    4   16   47    9   29    8
+   21    7   31   45   30    4   16   47    9   29    8
 Distances:
- Columns 1 through 11:
+ Columns 1 through 10:
 
-   0.029932   0.040026   0.046845   0.051107   0.054789   0.063517   0.067855   0.071365   0.073769   0.075991   0.082686
+   0.029932   0.040026   0.046845   0.051107   0.054789   0.063517   0.067855   0.071365   0.073769   0.075991
 
- Columns 12 through 22:
+ Columns 11 through 20:
 
-   0.084066   0.090008   0.095171   0.096337   0.096836   0.097593   0.098255   0.098948   0.101780   0.108652   0.108983
+   0.082686   0.084066   0.090008   0.095171   0.096337   0.096836   0.097593   0.098255   0.098948   0.101780
 
- Columns 23 through 33:
+ Columns 21 through 30:
 
-   0.114272   0.116760   0.120198   0.121721   0.122560   0.127342   0.128062   0.128687   0.130208   0.136007   0.142870
+   0.108652   0.108983   0.114272   0.116760   0.120198   0.121721   0.122560   0.127342   0.128062   0.128687
 
- Column 34:
+ Columns 31 through 34:
 
-   0.143009</pre></td></tr>
+   0.130208   0.136007   0.142870   0.143009</pre></td></tr>
                   </table>
                   <div class="text-center">
                     <img src="assets/ExhaustiveSearcher_402.png" class="rounded img-thumbnail" alt="plotted figure">

docs/GeneralizedExtremeValueDistribution.html

@@ -2134,9 +2134,9 @@ <h3 class="d-inline-block mr-2">
 
  ## Compute confidence intervals for parameters of a fitted Generalized Extreme Value
  ## distribution
- pd = makedist ("GeneralizedExtremeValue", "k", 0, "sigma", 1, "mu", 0)
- rand ("seed", 21);
- data = random (pd, 1000, 1);
+ pd = makedist ("GeneralizedExtremeValue", "k", -0.5, "sigma", 1, "mu", 2)
+ rand ("seed", 1);
+ data = random (pd, 5000, 1);
  pd_fitted = fitdist (data, "GeneralizedExtremeValue")
  ci = paramci (pd_fitted, "Alpha", 0.05)
 
@@ -2147,43 +2147,22 @@ <h3 class="d-inline-block mr-2">
   GeneralizedExtremeValueDistribution
 
   Generalized Extreme Value distribution
-        k = 0
-    sigma = 1
-       mu = 0
-
-warning: matrix singular to machine precision, rcond = 3.77481e-135
-warning: called from
-    polyfit at line 157 column 3
-    gevfit at line 176 column 3
-    fit at line 778 column 8
-    fitdist at line 377 column 9
-    build_DEMOS at line 94 column 11
-    classdef_texi2html at line 310 column 7
-    package_texi2html at line 298 column 9
+        k = -0.5
+    sigma =    1
+       mu =    2
 
 pd_fitted =
   GeneralizedExtremeValueDistribution
 
   Generalized Extreme Value distribution
-        k = -0.0179158   [-0.0600151, 0.0241835]
-    sigma =    1.01104   [0.962602, 1.06192]
-       mu =  0.0393083   [-0.0302083, 0.108825]
-
-warning: matrix singular to machine precision, rcond = 3.77481e-135
-warning: called from
-    polyfit at line 157 column 3
-    gevfit at line 176 column 3
-    mle at line 243 column 10
-    __paramci__ at line 97 column 6
-    paramci at line 508 column 9
-    build_DEMOS at line 94 column 11
-    classdef_texi2html at line 310 column 7
-    package_texi2html at line 298 column 9
+        k = -0.505978   [-0.520806, -0.491149]
+    sigma =   1.00491   [0.982564, 1.02776]
+       mu =   2.00433   [1.97453, 2.03414]
 
 ci =
 
-  -0.060015   0.962602  -0.030208
-   0.024184   1.061923   0.108825
+  -0.5208   0.9826   1.9745
+  -0.4911   1.0278   2.0341
 
                     </pre></td></tr></tbody>
                   </table>
@@ -2593,9 +2572,9 @@ <h3 class="d-inline-block mr-2">
 
  ## Compute and plot the profile likelihood for the shape parameter of a fitted
  ## Generalized Extreme Value distribution
- pd = makedist ("GeneralizedExtremeValue", "k", 0, "sigma", 1, "mu", 0)
- rand ("seed", 21);
- data = random (pd, 1000, 1);
+ pd = makedist ("GeneralizedExtremeValue", "k", -0.5, "sigma", 1, "mu", 2)
+ rand ("seed", 1);
+ data = random (pd, 5000, 1);
  pd_fitted = fitdist (data, "GeneralizedExtremeValue")
  [nlogL, param] = proflik (pd_fitted, 1, "Display", "on");
 
@@ -2606,27 +2585,17 @@ <h3 class="d-inline-block mr-2">
   GeneralizedExtremeValueDistribution
 
   Generalized Extreme Value distribution
-        k = 0
-    sigma = 1
-       mu = 0
-
-warning: matrix singular to machine precision, rcond = 3.77481e-135
-warning: called from
-    polyfit at line 157 column 3
-    gevfit at line 176 column 3
-    fit at line 778 column 8
-    fitdist at line 377 column 9
-    build_DEMOS at line 94 column 11
-    classdef_texi2html at line 310 column 7
-    package_texi2html at line 298 column 9
+        k = -0.5
+    sigma =    1
+       mu =    2
 
 pd_fitted =
   GeneralizedExtremeValueDistribution
 
   Generalized Extreme Value distribution
-        k = -0.0179158   [-0.0600151, 0.0241835]
-    sigma =    1.01104   [0.962602, 1.06192]
-       mu =  0.0393083   [-0.0302083, 0.108825]
+        k = -0.505978   [-0.520806, -0.491149]
+    sigma =   1.00491   [0.982564, 1.02776]
+       mu =   2.00433   [1.97453, 2.03414]
 
                     </pre></td></tr></tbody>
                   </table>

docs/NegativeBinomialDistribution.html

@@ -906,13 +906,13 @@ <h3 class="d-inline-block mr-2">
  ## Negative Binomial distribution. R must be a positive scalar, controlling
  ## the shape and often fixed based on the problem context, like successes in trials.
 
-ans = 5.1405
+ans = 5.2135
 pd =
   NegativeBinomialDistribution
 
   negative binomial distribution
         R =       10
-        P = 0.505124
+        P = 0.508399
 
                     </pre></td></tr></tbody>
                   </table>
@@ -1032,12 +1032,12 @@ <h3 class="d-inline-block mr-2">
  ## Binomial distribution. P must be a real scalar in (0,1], influencing the
  ## mean and variance of the distribution.
 
-ans = 0.5115
+ans = 0.4926
 pd =
   NegativeBinomialDistribution
 
   negative binomial distribution
-        R = 5.19879
+        R = 4.87025
         P =     0.3
 
                     </pre></td></tr></tbody>
@@ -1742,8 +1742,8 @@ <h3 class="d-inline-block mr-2">
  ## distributions with different success probabilities, representing average
  ## number of failures.
 
-mean1 = 11.653
-mean2 = 5.0744
+mean1 = 11.684
+mean2 = 5.0440
                     </pre></td></tr></tbody>
                   </table>
 
@@ -1887,7 +1887,7 @@ <h3 class="d-inline-block mr-2">
  ## This is useful for assessing the fit of a Negative Binomial distribution to
  ## data, with lower values indicating a better fit, often used in model comparison.
 
-nlogL_nbinlike = 236.57
+nlogL_nbinlike = 246.98
                     </pre></td></tr></tbody>
                   </table>
 
@@ -1980,8 +1980,8 @@ <h3 class="d-inline-block mr-2">
 
 ci =
 
-   3.5788   0.4133
-   5.0159   0.4987
+   4.2483   0.4637
+   6.2122   0.5595
 
                     </pre></td></tr></tbody>
                   </table>
@@ -2516,7 +2516,7 @@ <h3 class="d-inline-block mr-2">
  ## Use this to calculate the standard deviation, which measures the variability
  ## in the count values of the distribution.
 
-std_value = 3.1534
+std_value = 3.1134
                     </pre></td></tr></tbody>
                   </table>
 
@@ -2686,7 +2686,7 @@ <h3 class="d-inline-block mr-2">
  ## Use this to calculate the variance, which quantifies the spread of the
  ## count values in the distribution.
 
-var_value = 9.5457
+var_value = 9.9763
                     </pre></td></tr></tbody>
                   </table>
 
@@ -2745,8 +2745,8 @@ <h4 class="d-inline-block my-3">
   NegativeBinomialDistribution
 
   negative binomial distribution
-        R =  4.72131   [4.34848, 5.09413]
-        P = 0.483052   [0.462841, 0.503263]
+        R =  5.07566   [4.66024, 5.49108]
+        P = 0.503046   [0.482125, 0.523966]
 </pre></td></tr>
                   </table>
                   <div class="text-center">

docs/PoissonDistribution.html

@@ -903,7 +903,7 @@ <h3 class="d-inline-block mr-2">
  ## The rate parameter must be a positive real scalar, representing both the mean
  ## and variance, ideal for count data modeling.
 
-ans = 5.1700
+ans = 5.6000
 pd =
   PoissonDistribution
 
@@ -1604,8 +1604,8 @@ <h3 class="d-inline-block mr-2">
  ## This shows how to compute the expected value for Poisson distributions,
  ## which equals lambda, representing the average number of events.
 
-mean1 = 1.9968
-mean2 = 5.0161
+mean1 = 1.9884
+mean2 = 4.9659
                     </pre></td></tr></tbody>
                   </table>
 
@@ -1748,7 +1748,7 @@ <h3 class="d-inline-block mr-2">
  ## This is useful for assessing the fit of a Poisson distribution to data,
  ## with lower values indicating better fit, often used in model selection.
 
-nlogL = -221.78
+nlogL = -230.29
                     </pre></td></tr></tbody>
                   </table>
 
@@ -1840,8 +1840,8 @@ <h3 class="d-inline-block mr-2">
 
 ci =
 
-   4.7993
-   5.0747
+   4.8880
+   5.1660
 
                     </pre></td></tr></tbody>
                   </table>
@@ -2367,7 +2367,7 @@ <h3 class="d-inline-block mr-2">
  ## Use this to calculate the standard deviation, which equals sqrt(lambda),
  ## measuring variability in event counts.
 
-std_value = 2.2381
+std_value = 2.2350
                     </pre></td></tr></tbody>
                   </table>
 
@@ -2530,7 +2530,7 @@ <h3 class="d-inline-block mr-2">
  ## Use this to calculate the variance, which equals lambda, quantifying the
  ## spread in count data.
 
-var_value = 4.9952
+var_value = 4.9590
                     </pre></td></tr></tbody>
                   </table>
 
@@ -2588,7 +2588,7 @@ <h4 class="d-inline-block my-3">
   PoissonDistribution
 
   Poisson distribution
-   lambda = 5.0322   [4.97002, 5.09438]
+   lambda = 4.967   [4.90523, 5.02877]
 </pre></td></tr>
                   </table>
                   <div class="text-center">