time-to-botec

Benchmark sampling in different programming languages
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      1 
      2 {{alias}}( [options] )
      3     Returns an accumulator function which incrementally performs Grubbs' test
      4     for detecting outliers.
      5 
      6     Grubbs' test assumes that data is normally distributed. Accordingly, one
      7     should first verify that the data can be reasonably approximated by a normal
      8     distribution before applying the Grubbs' test.
      9 
     10     If provided a value, the accumulator function returns updated test results.
     11     If not provided a value, the accumulator function returns the current test
     12     results.
     13 
     14     If provided `NaN` or a value which, when used in computations, results in
     15     `NaN`, the test statistic is `NaN` for all future invocations.
     16 
     17     The accumulator must be provided *at least* three data points before
     18     performing Grubbs' test. Until at least three data points are provided, the
     19     accumulator returns `null`.
     20 
     21     The accumulator function returns an object having the following fields:
     22 
     23     - rejected: boolean indicating whether the null hypothesis should be
     24     rejected.
     25     - alpha: significance level.
     26     - criticalValue: critical value.
     27     - statistic: test statistic.
     28     - df: degrees of freedom.
     29     - mean: sample mean.
     30     - sd: corrected sample standard deviation.
     31     - min: minimum value.
     32     - max: maximum value.
     33     - alt: alternative hypothesis.
     34     - method: method name.
     35     - print: method for pretty-printing test output.
     36 
     37     Parameters
     38     ----------
     39     options: Object (optional)
     40         Function options.
     41 
     42     options.alpha: number (optional)
     43         Significance level. Default: 0.05.
     44 
     45     options.alternative: string (optional)
     46         Alternative hypothesis. The option may be one of the following values:
     47 
     48         - 'two-sided': test whether the minimum or maximum value is an outlier.
     49         - 'min': test whether the minimum value is an outlier.
     50         - 'max': test whether the maximum value is an outlier.
     51 
     52         Default: 'two-sided'.
     53 
     54     options.init: integer (optional)
     55         Number of data points the accumulator should use to compute initial
     56         statistics *before* testing for an outlier. Until the accumulator is
     57         provided the number of data points specified by this option, the
     58         accumulator returns `null`. Default: 100.
     59 
     60     Returns
     61     -------
     62     acc: Function
     63         Accumulator function.
     64 
     65     Examples
     66     --------
     67     > var acc = {{alias}}();
     68     > var res = acc()
     69     null
     70     > for ( var i = 0; i < 200; i++ ) {
     71     ...     res = acc( {{alias:@stdlib/random/base/normal}}( 10.0, 5.0 ) );
     72     ... };
     73     > res.print()
     74 
     75     References
     76     ----------
     77     - Grubbs, Frank E. 1950. "Sample Criteria for Testing Outlying
     78     Observations." _The Annals of Mathematical Statistics_ 21 (1). The Institute
     79     of Mathematical Statistics: 27–58. doi:10.1214/aoms/1177729885.
     80     - Grubbs, Frank E. 1969. "Procedures for Detecting Outlying Observations in
     81     Samples." _Technometrics_ 11 (1). Taylor & Francis: 1–21. doi:10.1080/
     82     00401706.1969.10490657.
     83 
     84     See Also
     85     --------
     86