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‎docs/copying.html

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‎docs/description.json

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{
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"generator": "generate_html",
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"generator_version": "0.3.3",
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"date_generated": "2024-05-13",
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"date_generated": "2024-05-16",
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"package":
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{
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"name": "statistics-resampling",
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"version": "5.5.11",
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"version": "5.5.12",
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"description": "The statistics-resampling package is an Octave package and Matlab toolbox that can be used to perform a wide variety of statistics tasks 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 calculate p-values for null hypothesis significance tests. Variations of the resampling methods are included that improve the accuracy of the statistics for small samples and samples with complex dependence structures.",
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"shortdescription": "The statistics-resampling package is an Octave package and Matlab toolbox that can be used to perform a wide variety of statistics tasks using non-parametric resampling methods",
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"date": "2024-05-13",
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"date": "2024-05-15",
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"title": "A statistics package with a variety of resampling tools",
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"author": "Andrew Penn <andy.c.penn@gmail.com>",
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"maintainer": "Andrew Penn <andy.c.penn@gmail.com>",

‎docs/function/boot.html

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‎docs/function/boot1way.html

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-----------------------------------------------------------------------------
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| Comparison | Test # | Ref # | Difference | t | p |
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|------------|------------|------------|------------|------------|----------|
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| 1 | 2 | 1 | -0.3684 | -0.63 | .564 |
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| 1 | 2 | 1 | +0.8304 | +1.63 | .161 |
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-----------------------------------------------------------------------------
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| GROUP # | GROUP label | N |
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-----------------------------------------------------------------------------
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| Comparison | Test # | Ref # | Difference | t | p |
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|------------|------------|------------|------------|------------|----------|
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| 1 | 2 | 1 | +0.3039 | +0.44 | .600 |
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| 1 | 2 | 1 | +0.6446 | +1.33 | .078 |
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| GROUP # | GROUP label | N |

‎docs/function/bootbayes.html

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Posterior Statistics:
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original bias median stdev CI_lower CI_upper
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+184.5 +0.01529 +184.5 1.351 +181.7 +187.2</pre>
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+184.5 -0.02210 +184.5 1.240 +182.1 +186.9</pre>
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</div>
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<h3><a name="2">Demonstration 2</a></h3>
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Posterior Statistics:
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original bias median stdev CI_lower CI_upper
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+175.5 -0.07775 +175.4 2.287 +171.1 +179.9
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+0.1904 +0.002169 +0.1929 0.07622 +0.03454 +0.3340</pre>
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+175.5 +0.03532 +175.5 2.384 +171.1 +180.3
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+0.1904 -0.001074 +0.1909 0.07912 +0.04160 +0.3442</pre>
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<p>Package: <a href="../index.html">statistics-resampling</a></p>

‎docs/function/bootcdf.html

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‎docs/function/bootci.html

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<p>Produces the following output</p>
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<pre class="example">ci =
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-0.13896 -0.72921
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0.7127 0.31607</pre>
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-0.74781 -0.35862
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0.276 0.50962</pre>
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<h3><a name="11">Demonstration 11</a></h3>

‎docs/function/bootclust.html

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cluster/block (i.e. no clustering or blocking). The means of the resamples
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are then computed and the following statistics are displayed:
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- original: the original estimate(s) calculated by BOOTFUN and the DATA
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- bias: bootstrap bias of the estimate(s)
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- std_error: bootstrap standard error of the estimate(s)
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- CI_lower: lower bound(s) of the 95% bootstrap confidence interval
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- CI_upper: upper bound(s) of the 95% bootstrap confidence interval
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- bias: bootstrap estimate of the bias of the sampling distribution(s)
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- std_error: bootstrap estimate(s) of the standard error(s)
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- CI_lower: lower bound(s) of the 95% bootstrap confidence interval(s)
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- CI_upper: upper bound(s) of the 95% bootstrap confidence interval(s)
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'bootclust (DATA, NBOOT)' specifies the number of bootstrap resamples,
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where NBOOT is a scalar, positive integer corresponding to the number
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Bootstrap Statistics:
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original bias std_error CI_lower CI_upper
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+0.3052 -0.01557 +0.2059 -0.007480 +0.6725
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-0.1663 +0.02261 +0.1710 -0.4057 +0.1247</pre>
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+0.3625 -0.01706 +0.2193 +0.01007 +0.7266
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+0.2251 +0.001246 +0.2250 -0.1672 +0.5763</pre>
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<h3><a name="8">Demonstration 8</a></h3>
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Bootstrap Statistics:
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original bias std_error CI_lower CI_upper
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+0.1401 +0.006113 +0.2929 -0.4786 +0.5297
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+0.04985 +0.009865 +0.1333 -0.1655 +0.2615</pre>
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+0.1434 +0.02090 +0.1595 -0.03552 +0.4799
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-0.1623 +0.01750 +0.1080 -0.3331 +0.005695</pre>
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<h3><a name="9">Demonstration 9</a></h3>

‎docs/function/bootint.html

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‎docs/function/bootknife.html

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(column vector or matrix) and compute their means and display the
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following statistics:
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- original: the original estimate(s) calculated by BOOTFUN and the DATA
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- bias: bootstrap bias of the estimate(s)
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- std_error: bootstrap standard error of the estimate(s)
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- CI_lower: lower bound(s) of the 95% bootstrap confidence interval
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- CI_upper: upper bound(s) of the 95% bootstrap confidence interval
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- bias: bootstrap estimate of the bias of the sampling distribution(s)
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- std_error: bootstrap estimate(s) of the standard error(s)
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- CI_lower: lower bound(s) of the 95% bootstrap confidence interval(s)
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- CI_upper: upper bound(s) of the 95% bootstrap confidence interval(s)
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'bootknife (DATA, NBOOT)' specifies the number of bootstrap resamples,
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Bootstrap Statistics:
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+0.1034 -0.001186 +0.1974 -0.1832 +0.4674
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-0.04645 +0.004099 +0.2699 -0.3929 +0.5239</pre>
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+0.1331 -0.01506 +0.2166 -0.2376 +0.4716
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-0.5121 +0.01136 +0.1966 -0.9021 -0.2342</pre>
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<h3><a name="9">Demonstration 9</a></h3>

‎docs/function/bootlm.html

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‎docs/function/bootmode.html

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<p>Produces the following output</p>
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ans = H1 is 1 with p = 0.001 so reject the null hypothesisthat there is 1 mode
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ans = H1 is 1 with p = 0.0005 so reject the null hypothesisthat there is 1 mode
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ans = H2 is 0 with p = 0.343 so accept the null hypothesis that there are 2 modes</pre>
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<p>Package: <a href="../index.html">statistics-resampling</a></p>

‎docs/function/bootstrp.html

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'[BOOTSTAT, BOOTSAM, STATS] = bootstrp (...)' also calculates and returns
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- original: the original estimate(s) calculated by BOOTFUN and the DATA
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- mean: the mean of the bootstrap distribution(s)
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- bias: bootstrap estimate of the bias of the sampling distribution(s)
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- bias_corrected: original estimate(s) after subtracting the bias
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- var: bootstrap variance of the original estimate(s)
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- std_error: bootstrap estimate(s) of the standard error(s)
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‎docs/function/bootwild.html

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p-values after imposing the null hypothesis (H0) [1-4]. The following
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statistics are printed to the standard output:
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- original: the mean of the data vector y
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- std_err: heteroscedasticity-consistent standard error(s)
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- std_err: heteroscedasticity-consistent standard error(s) (HC0)
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- CI_lower: lower bound(s) of the 95% bootstrap-t confidence interval
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- CI_upper: upper bound(s) of the 95% bootstrap-t confidence interval
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- tstat: Student's t-statistic
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Test Statistics:
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original std_err CI_lower CI_upper t-stat p-val FPR
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+184.5 +1.243 +181.6 +187.4 +148. <.001 .010</pre>
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+184.5 +1.243 +181.7 +187.3 +148. <.001 .010</pre>
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<h3><a name="2">Demonstration 2</a></h3>
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Test Statistics:
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original std_err CI_lower CI_upper t-stat p-val FPR
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+175.5 +2.502 +169.8 +181.2 +70.1 <.001 .010
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+0.1904 +0.08261 +0.003534 +0.3773 +2.31 .047 .280</pre>
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+175.5 +2.502 +169.9 +181.1 +70.1 <.001 .010
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+0.1904 +0.08261 +0.01031 +0.3705 +2.31 .039 .257</pre>
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<p>Package: <a href="../index.html">statistics-resampling</a></p>

‎docs/function/cor.html

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‎docs/function/credint.html

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‎docs/function/deffcalc.html

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‎docs/function/randtest.html

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<p>Produces the following output</p>
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<pre class="example">pval =
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stat =
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‎docs/function/randtest1.html

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@(A, B) log (var (A) ./ var (B)))
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</pre>
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<p>Produces the following output</p>
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<pre class="example">pval = 0.3668
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pval = 0.2698
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pval = 0.30905</pre>
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<pre class="example">pval = 0.3526
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pval = 0.2718
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pval = 0.31043</pre>
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</div>
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<h3><a name="2">Demonstration 2</a></h3>

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