confint_2group_diff

A range of functions to compute bootstraps for the mean difference

calculate_weighted_delta

 calculate_weighted_delta (group_var, differences)

Compute the weighted deltas.

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calculate_group_var

 calculate_group_var (control_var, control_N, test_var, test_N)

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compute_interval_limits

 compute_interval_limits (bias, acceleration, n_boots, ci=95)

*Returns the indexes of the interval limits for a given bootstrap.

Supply the bias, acceleration factor, and number of bootstraps.*

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compute_meandiff_bias_correction

 compute_meandiff_bias_correction (bootstraps, effsize)

Computes the bias correction required for the BCa method of confidence interval construction.

	Type	Details
bootstraps		An numerical iterable, comprising bootstrap resamples of the effect size.
effsize		The effect size for the original sample.
Returns	bias: numeric	The bias correction value for the given bootstraps and effect size.

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compute_delta2_bootstrapped_diff

 compute_delta2_bootstrapped_diff (x1:numpy.ndarray, x2:numpy.ndarray,
                                   x3:numpy.ndarray, x4:numpy.ndarray,
                                   is_paired:str=None, resamples:int=5000,
                                   random_seed:int=12345,
                                   proportional:bool=False)

Bootstraps the effect size deltas’ g or proportional delta-delta

	Type	Default	Details
x1	ndarray		Control group 1
x2	ndarray		Test group 1
x3	ndarray		Control group 2
x4	ndarray		Test group 2
is_paired	str	None
resamples	int	5000
random_seed	int	12345
proportional	bool	False
Returns	tuple

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delta2_bootstrap_loop

 delta2_bootstrap_loop (x1, x2, x3, x4, resamples, pooled_sd, rng_seed,
                        is_paired, proportional=False)

Compute bootstrapped differences for delta-delta, handling both regular and proportional data

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compute_bootstrapped_diff

 compute_bootstrapped_diff (x0, x1, is_paired, effect_size,
                            resamples=5000, random_seed=12345)

Bootstraps the effect_size for 2 groups.

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bootstrap_indices

 bootstrap_indices (is_paired, x0_len, x1_len, resamples, random_seed)

	Details
is_paired
x0_len
x1_len
resamples
random_seed	parallelization must be turned off for random number generation

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compute_meandiff_jackknife

 compute_meandiff_jackknife (x0, x1, is_paired, effect_size)

Given two arrays, returns the jackknife for their effect size.

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create_repeated_indexes

 create_repeated_indexes (data)

Convenience function. Given an array-like with length N, returns a generator that yields N indexes [0, 1, …, N].

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create_jackknife_indexes

 create_jackknife_indexes (data)

*Given an array-like, creates a jackknife bootstrap.

For a given set of data Y, the jackknife bootstrap sample J[i] is defined as the data set Y with the ith data point deleted.*

	Type	Details
data
Returns	Generator that yields all jackknife bootstrap samples.