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update online documentation for release-1.7.7
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‎NEWS‎

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@@ -251,7 +251,7 @@
251251
** ztest2: improve input validation (#226)
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253253

254-
Summary of important user-visible changes for statistics 1.7.6:
254+
Summary of important user-visible changes for statistics 1.7.7:
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-------------------------------------------------------------------
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Important Notice: 1) `mad`, `mean`, `median`, `std`, `var` functions

‎docs/BetaDistribution.html‎

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@@ -1891,10 +1891,10 @@ <h3 class="d-inline-block mr-2">
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BetaDistribution
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beta distribution
1894-
a = 2.29841 [1.78335, 2.96222]
1895-
b = 5.28788 [4.23907, 6.59618]
1894+
a = 2.37491 [1.76557, 3.19455]
1895+
b = 6.23768 [4.57955, 8.49618]
18961896

1897-
nlogL = 48.645
1897+
nlogL = 57.538
18981898
</pre></td></tr></tbody>
18991899
</table>
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@@ -1996,13 +1996,13 @@ <h3 class="d-inline-block mr-2">
19961996
BetaDistribution
19971997

19981998
beta distribution
1999-
a = 1.93585 [1.78762, 2.09637]
2000-
b = 4.91037 [4.49283, 5.36672]
1999+
a = 1.89199 [1.7412, 2.05584]
2000+
b = 4.8405 [4.43529, 5.28274]
20012001

20022002
ci =
20032003

2004-
1.7876 4.4928
2005-
2.0964 5.3667
2004+
1.7412 4.4353
2005+
2.0558 5.2827
20062006

20072007
</pre></td></tr></tbody>
20082008
</table>

‎docs/ExhaustiveSearcher.html‎

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Columns 1 through 1:
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{[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 ... ]}
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{[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 ... ]}
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{[ 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 ... ]}
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{[ 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 ... ]}
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{[ 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 ... ]}
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{[ 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 ... ]}
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{[ 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 ... ]}
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{[ 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 ... ]}
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{[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 ... ]}
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{[ 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 ... ]}
11891189
</pre></td></tr>
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</table>
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@@ -1391,29 +1391,29 @@ <h4 class="d-inline-block my-3">
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For the first query point in rangesearch:
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Query point: 0.3 0.3
13931393
Indices of points within radius:
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Columns 1 through 25:
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Columns 1 through 23:
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1396-
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
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49 19 14 46 34 23 12 41 1 3 42 48 24 2 11 37 10 27 32 20 44 40 39
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Columns 26 through 34:
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Columns 24 through 34:
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31 45 30 4 16 47 9 29 8
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21 7 31 45 30 4 16 47 9 29 8
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Distances:
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Columns 1 through 11:
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Columns 1 through 10:
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0.029932 0.040026 0.046845 0.051107 0.054789 0.063517 0.067855 0.071365 0.073769 0.075991 0.082686
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0.029932 0.040026 0.046845 0.051107 0.054789 0.063517 0.067855 0.071365 0.073769 0.075991
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Columns 12 through 22:
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Columns 11 through 20:
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1408-
0.084066 0.090008 0.095171 0.096337 0.096836 0.097593 0.098255 0.098948 0.101780 0.108652 0.108983
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0.082686 0.084066 0.090008 0.095171 0.096337 0.096836 0.097593 0.098255 0.098948 0.101780
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Columns 23 through 33:
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Columns 21 through 30:
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0.114272 0.116760 0.120198 0.121721 0.122560 0.127342 0.128062 0.128687 0.130208 0.136007 0.142870
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0.108652 0.108983 0.114272 0.116760 0.120198 0.121721 0.122560 0.127342 0.128062 0.128687
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Column 34:
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Columns 31 through 34:
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0.143009</pre></td></tr>
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0.130208 0.136007 0.142870 0.143009</pre></td></tr>
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</table>
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<div class="text-center">
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<img src="assets/ExhaustiveSearcher_402.png" class="rounded img-thumbnail" alt="plotted figure">

‎docs/GeneralizedExtremeValueDistribution.html‎

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## Compute confidence intervals for parameters of a fitted Generalized Extreme Value
21362136
## distribution
2137-
pd = makedist ("GeneralizedExtremeValue", "k", 0, "sigma", 1, "mu", 0)
2138-
rand ("seed", 21);
2139-
data = random (pd, 1000, 1);
2137+
pd = makedist ("GeneralizedExtremeValue", "k", -0.5, "sigma", 1, "mu", 2)
2138+
rand ("seed", 1);
2139+
data = random (pd, 5000, 1);
21402140
pd_fitted = fitdist (data, "GeneralizedExtremeValue")
21412141
ci = paramci (pd_fitted, "Alpha", 0.05)
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21472147
GeneralizedExtremeValueDistribution
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21492149
Generalized Extreme Value distribution
2150-
k = 0
2151-
sigma = 1
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mu = 0
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2154-
warning: matrix singular to machine precision, rcond = 3.77481e-135
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warning: called from
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polyfit at line 157 column 3
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gevfit at line 176 column 3
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fit at line 778 column 8
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fitdist at line 377 column 9
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build_DEMOS at line 94 column 11
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classdef_texi2html at line 310 column 7
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package_texi2html at line 298 column 9
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k = -0.5
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sigma = 1
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mu = 2
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pd_fitted =
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GeneralizedExtremeValueDistribution
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21672157
Generalized Extreme Value distribution
2168-
k = -0.0179158 [-0.0600151, 0.0241835]
2169-
sigma = 1.01104 [0.962602, 1.06192]
2170-
mu = 0.0393083 [-0.0302083, 0.108825]
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2172-
warning: matrix singular to machine precision, rcond = 3.77481e-135
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warning: called from
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polyfit at line 157 column 3
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gevfit at line 176 column 3
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mle at line 243 column 10
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__paramci__ at line 97 column 6
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paramci at line 508 column 9
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build_DEMOS at line 94 column 11
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classdef_texi2html at line 310 column 7
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package_texi2html at line 298 column 9
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k = -0.505978 [-0.520806, -0.491149]
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sigma = 1.00491 [0.982564, 1.02776]
2160+
mu = 2.00433 [1.97453, 2.03414]
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21832162
ci =
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-0.060015 0.962602 -0.030208
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0.024184 1.061923 0.108825
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-0.5208 0.9826 1.9745
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-0.4911 1.0278 2.0341
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</pre></td></tr></tbody>
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</table>
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## Compute and plot the profile likelihood for the shape parameter of a fitted
25952574
## Generalized Extreme Value distribution
2596-
pd = makedist ("GeneralizedExtremeValue", "k", 0, "sigma", 1, "mu", 0)
2597-
rand ("seed", 21);
2598-
data = random (pd, 1000, 1);
2575+
pd = makedist ("GeneralizedExtremeValue", "k", -0.5, "sigma", 1, "mu", 2)
2576+
rand ("seed", 1);
2577+
data = random (pd, 5000, 1);
25992578
pd_fitted = fitdist (data, "GeneralizedExtremeValue")
26002579
[nlogL, param] = proflik (pd_fitted, 1, "Display", "on");
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GeneralizedExtremeValueDistribution
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26082587
Generalized Extreme Value distribution
2609-
k = 0
2610-
sigma = 1
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mu = 0
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warning: matrix singular to machine precision, rcond = 3.77481e-135
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warning: called from
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polyfit at line 157 column 3
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gevfit at line 176 column 3
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fit at line 778 column 8
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fitdist at line 377 column 9
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build_DEMOS at line 94 column 11
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classdef_texi2html at line 310 column 7
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package_texi2html at line 298 column 9
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k = -0.5
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sigma = 1
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mu = 2
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26232592
pd_fitted =
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GeneralizedExtremeValueDistribution
26252594

26262595
Generalized Extreme Value distribution
2627-
k = -0.0179158 [-0.0600151, 0.0241835]
2628-
sigma = 1.01104 [0.962602, 1.06192]
2629-
mu = 0.0393083 [-0.0302083, 0.108825]
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k = -0.505978 [-0.520806, -0.491149]
2597+
sigma = 1.00491 [0.982564, 1.02776]
2598+
mu = 2.00433 [1.97453, 2.03414]
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</pre></td></tr></tbody>
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</table>

‎docs/NegativeBinomialDistribution.html‎

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## Negative Binomial distribution. R must be a positive scalar, controlling
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## the shape and often fixed based on the problem context, like successes in trials.
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909-
ans = 5.1405
909+
ans = 5.2135
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pd =
911911
NegativeBinomialDistribution
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913913
negative binomial distribution
914914
R = 10
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P = 0.505124
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P = 0.508399
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</pre></td></tr></tbody>
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</table>
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10321032
## Binomial distribution. P must be a real scalar in (0,1], influencing the
10331033
## mean and variance of the distribution.
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1035-
ans = 0.5115
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ans = 0.4926
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pd =
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NegativeBinomialDistribution
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negative binomial distribution
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R = 5.19879
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R = 4.87025
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P = 0.3
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</pre></td></tr></tbody>
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## distributions with different success probabilities, representing average
17431743
## number of failures.
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1745-
mean1 = 11.653
1746-
mean2 = 5.0744
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mean1 = 11.684
1746+
mean2 = 5.0440
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</pre></td></tr></tbody>
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</table>
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## This is useful for assessing the fit of a Negative Binomial distribution to
18881888
## data, with lower values indicating a better fit, often used in model comparison.
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1890-
nlogL_nbinlike = 236.57
1890+
nlogL_nbinlike = 246.98
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</pre></td></tr></tbody>
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</table>
18931893

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19801980

19811981
ci =
19821982

1983-
3.5788 0.4133
1984-
5.0159 0.4987
1983+
4.2483 0.4637
1984+
6.2122 0.5595
19851985

19861986
</pre></td></tr></tbody>
19871987
</table>
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25162516
## Use this to calculate the standard deviation, which measures the variability
25172517
## in the count values of the distribution.
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2519-
std_value = 3.1534
2519+
std_value = 3.1134
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</pre></td></tr></tbody>
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</table>
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26862686
## Use this to calculate the variance, which quantifies the spread of the
26872687
## count values in the distribution.
26882688

2689-
var_value = 9.5457
2689+
var_value = 9.9763
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</pre></td></tr></tbody>
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</table>
26922692

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NegativeBinomialDistribution
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27472747
negative binomial distribution
2748-
R = 4.72131 [4.34848, 5.09413]
2749-
P = 0.483052 [0.462841, 0.503263]
2748+
R = 5.07566 [4.66024, 5.49108]
2749+
P = 0.503046 [0.482125, 0.523966]
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</pre></td></tr>
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</table>
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<div class="text-center">

‎docs/PoissonDistribution.html‎

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## The rate parameter must be a positive real scalar, representing both the mean
904904
## and variance, ideal for count data modeling.
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906-
ans = 5.1700
906+
ans = 5.6000
907907
pd =
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PoissonDistribution
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16041604
## This shows how to compute the expected value for Poisson distributions,
16051605
## which equals lambda, representing the average number of events.
16061606

1607-
mean1 = 1.9968
1608-
mean2 = 5.0161
1607+
mean1 = 1.9884
1608+
mean2 = 4.9659
16091609
</pre></td></tr></tbody>
16101610
</table>
16111611

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17481748
## This is useful for assessing the fit of a Poisson distribution to data,
17491749
## with lower values indicating better fit, often used in model selection.
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1751-
nlogL = -221.78
1751+
nlogL = -230.29
17521752
</pre></td></tr></tbody>
17531753
</table>
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18401840

18411841
ci =
18421842

1843-
4.7993
1844-
5.0747
1843+
4.8880
1844+
5.1660
18451845

18461846
</pre></td></tr></tbody>
18471847
</table>
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23672367
## Use this to calculate the standard deviation, which equals sqrt(lambda),
23682368
## measuring variability in event counts.
23692369

2370-
std_value = 2.2381
2370+
std_value = 2.2350
23712371
</pre></td></tr></tbody>
23722372
</table>
23732373

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25302530
## Use this to calculate the variance, which equals lambda, quantifying the
25312531
## spread in count data.
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2533-
var_value = 4.9952
2533+
var_value = 4.9590
25342534
</pre></td></tr></tbody>
25352535
</table>
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PoissonDistribution
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25902590
Poisson distribution
2591-
lambda = 5.0322 [4.97002, 5.09438]
2591+
lambda = 4.967 [4.90523, 5.02877]
25922592
</pre></td></tr>
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</table>
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<div class="text-center">

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