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Showing results 11 to 20 of 1,221.


Function pavaf1 [pi0 v1.4-1]
keywords
univar
title
pooling adjacent violator algorithm estimate of p-value density at 1
description
pooling adjacent violator algorithm estimate of p-value density at 1
Function geomean and harmean [pi0 v1.4-1]
keywords
univar
title
Geometric mean and harmonic mean functions
description
Geometric mean and harmonic mean functions
Function gjack [pi0 v1.4-1]
keywords
univar
title
Generalized jackknife
description
Generalized jackknife bias correction
Function one.boot [simpleboot v1.1-7]
keywords
univar
title
One sample bootstrap of a univariate statistic.
description
one.boot is used for bootstrapping a univariate statistic for one sample problems. Examples include the mean, median, etc.
Function two.boot [simpleboot v1.1-7]
keywords
univar
title
Two sample bootstrap of differences between univariate statistics.
description
two.boot is used to bootstrap the difference between various univariate statistics. An example is the difference of means. Bootstrapping is done by independently resampling from sample1 and sample2.
Function perc [simpleboot v1.1-7]
keywords
univar
title
Extract percentiles from a bootstrap sampling distribution.
description
perc can be used to extract percentiles from the sampling distribution of a statistic.
Function pairs_boot [simpleboot v1.1-7]
keywords
univar
title
Two sample bootstrap.
description
pairs.boot is used to bootstrap a statistic which operates on two samples and returns a single value. An example of such a statistic is the correlation coefficient (i.e. cor). Resampling is done pairwise, so x and y must have the same length (and be ordered correctly). One can alternatively pass a two-column matrix to x.
Function extremalindex [extRemes v2.0-12]
keywords
univar
title
Extemal Index
description
Estimate the extremal index.
Function hdquantile [Hmisc v4.4-0]
keywords
univar
title
Harrell-Davis Distribution-Free Quantile Estimator
description
Computes the Harrell-Davis (1982) quantile estimator and jacknife standard errors of quantiles. The quantile estimator is a weighted linear combination or order statistics in which the order statistics used in traditional nonparametric quantile estimators are given the greatest weight. In small samples the H-D estimator is more efficient than traditional ones, and the two methods are asymptotically equivalent. The H-D estimator is the limit of a bootstrap average as the number of bootstrap resamples becomes infinitely large.
Function geometricmean [compositions v1.40-5]
keywords
univar
title
The geometric mean
description
Computes the geometric mean.