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src/c/o/cogent-1.5.3/cogent/maths/stats/distribution.py   cogent(Download)
"""
from __future__ import division
from cogent.maths.stats.special import erf, erfc, igamc, igam, betai, log1p, \
    expm1, SQRTH, MACHEP, MAXNUM, PI, ndtri, incbi, igami, fix_rounding_error,\
    ln_binomial
            tz = 1
            j = 3
            while (j <= (k-2)) and ((tz/f) > MACHEP):
                tz *= (j-1)/(z*j)
                f += tz
        tz = 1
        j = 2
        while (j <= (k-2)) and ((tz/f) > MACHEP):
            tz *= (j-1)/(z*j)
            f += tz

src/p/y/pycogent-HEAD/cogent/maths/stats/distribution.py   pycogent(Download)
"""
from __future__ import division
from cogent.maths.stats.special import erf, erfc, igamc, igam, betai, log1p, \
    expm1, SQRTH, MACHEP, MAXNUM, PI, ndtri, incbi, igami, fix_rounding_error,\
    ln_binomial
            tz = 1
            j = 3
            while (j <= (k-2)) and ((tz/f) > MACHEP):
                tz *= (j-1)/(z*j)
                f += tz
        tz = 1
        j = 2
        while (j <= (k-2)) and ((tz/f) > MACHEP):
            tz *= (j-1)/(z*j)
            f += tz

src/q/i/qiime-HEAD/qiime/pycogent_backports/test.py   qiime(Download)
                                             t_high, t_low, tprob, f_high, f_low, fprob, binomial_high, binomial_low,
                                             ndtri)
from cogent.maths.stats.special import log_one_minus, one_minus_exp, MACHEP
from cogent.maths.stats import chisqprob
from cogent.maths.stats.ks import psmirnov2x, pkstwo
    # One important difference is I preserve the original sample sizes
    # instead of making them equal
    tol = MACHEP * 100
    combined = array(list(x) + list(y))
    observed_stat, _p = ks_test(x, y, exact=False, warn_for_ties=False)
    Uses the same Monte-Carlo resampling code as kw_boot
    """
    tol = MACHEP * 100
    combined = array(list(x) + list(y))
    observed_stat, obs_p = mw_test(x, y)

src/p/y/pycogent-HEAD/cogent/maths/stats/test.py   pycogent(Download)
    t_high, t_low, tprob, f_high, f_low, fprob, binomial_high, binomial_low, \
    ndtri
from cogent.maths.stats.special import lgam, log_one_minus, one_minus_exp,\
    MACHEP
from cogent.maths.stats.ks import psmirnov2x, pkstwo
    # One important difference is I preserve the original sample sizes
    # instead of making them equal
    tol = MACHEP * 100
    combined = array(list(x) + list(y))
    observed_stat, _p = ks_test(x, y, exact=False, warn_for_ties=False)
    Uses the same Monte-Carlo resampling code as kw_boot
    """
    tol = MACHEP * 100
    combined = array(list(x) + list(y))
    observed_stat, obs_p = mw_test(x, y)

src/c/o/cogent-1.5.3/cogent/maths/stats/test.py   cogent(Download)
    t_high, t_low, tprob, f_high, f_low, fprob, binomial_high, binomial_low, \
    ndtri
from cogent.maths.stats.special import lgam, log_one_minus, one_minus_exp,\
    MACHEP
from cogent.maths.stats.ks import psmirnov2x, pkstwo
    # One important difference is I preserve the original sample sizes
    # instead of making them equal
    tol = MACHEP * 100
    combined = array(list(x) + list(y))
    observed_stat, _p = ks_test(x, y, exact=False, warn_for_ties=False)
    Uses the same Monte-Carlo resampling code as kw_boot
    """
    tol = MACHEP * 100
    combined = array(list(x) + list(y))
    observed_stat, obs_p = mw_test(x, y)

src/q/i/qiime-1.8.0/qiime/pycogent_backports/test.py   qiime(Download)
    t_high, t_low, tprob, f_high, f_low, fprob, binomial_high, binomial_low,
    ndtri)
from cogent.maths.stats.special import (lgam, log_one_minus, one_minus_exp,
    MACHEP)
from cogent.maths.stats import chisqprob
    # One important difference is I preserve the original sample sizes
    # instead of making them equal
    tol = MACHEP * 100
    combined = array(list(x) + list(y))
    observed_stat, _p = ks_test(x, y, exact=False, warn_for_ties=False)
    Uses the same Monte-Carlo resampling code as kw_boot
    """
    tol = MACHEP * 100
    combined = array(list(x) + list(y))
    observed_stat, obs_p = mw_test(x, y)