| abs(X) |
Absolute value of a random variable [extrait de abs.__doc__] |
| alexandergovern(*samples, nan_policy='propagate', axis=0, keepdims=False) |
|
| alpha(*args, **kwds) |
An alpha continuous random variable. [extrait de __doc__] |
| anderson(x, dist='norm') |
Anderson-Darling test for data coming from a particular distribution. [extrait de anderson.__doc__] |
| anderson_ksamp(samples, midrank=True, *, method=None) |
The Anderson-Darling test for k-samples. [extrait de anderson_ksamp.__doc__] |
| anglit(*args, **kwds) |
An anglit continuous random variable. [extrait de __doc__] |
| ansari(x, y, alternative='two-sided', *, axis=0, nan_policy='propagate', keepdims=False) |
|
| arcsine(*args, **kwds) |
An arcsine continuous random variable. [extrait de __doc__] |
| argus(*args, **kwds) |
|
| barnard_exact(table, alternative='two-sided', pooled=True, n=32) |
Perform a Barnard exact test on a 2x2 contingency table. [extrait de barnard_exact.__doc__] |
| bartlett(*samples, axis=0, nan_policy='propagate', keepdims=False) |
|
| bayes_mvs(data, alpha=0.9) |
|
| bernoulli(*args, **kwds) |
A Bernoulli discrete random variable. [extrait de __doc__] |
| beta(*args, **kwds) |
A beta continuous random variable. [extrait de __doc__] |
| betabinom(*args, **kwds) |
A beta-binomial discrete random variable. [extrait de __doc__] |
| betanbinom(*args, **kwds) |
A beta-negative-binomial discrete random variable. [extrait de __doc__] |
| betaprime(*args, **kwds) |
A beta prime continuous random variable. [extrait de __doc__] |
| binned_statistic(x, values, statistic='mean', bins=10, range=None) |
|
| binned_statistic_2d(x, y, values, statistic='mean', bins=10, range=None, expand_binnumbers=False) |
|
| binned_statistic_dd(sample, values, statistic='mean', bins=10, range=None, expand_binnumbers=False, binned_statistic_result=None) |
|
| binom(*args, **kwds) |
A binomial discrete random variable. [extrait de __doc__] |
| binomtest(k, n, p=0.5, alternative='two-sided') |
|
| boltzmann(*args, **kwds) |
A Boltzmann (Truncated Discrete Exponential) random variable. [extrait de __doc__] |
| bootstrap(data, statistic, *, n_resamples=9999, batch=None, vectorized=None, paired=False, axis=0, confidence_level=0.95, alternative='two-sided', method='BCa', bootstrap_result=None, rng=None) |
|
| boschloo_exact(table, alternative='two-sided', n=32) |
Perform Boschloo's exact test on a 2x2 contingency table. [extrait de boschloo_exact.__doc__] |
| boxcox(x, lmbda=None, alpha=None, optimizer=None) |
Return a dataset transformed by a Box-Cox power transformation. [extrait de boxcox.__doc__] |
| boxcox_llf(lmb, data) |
The boxcox log-likelihood function. [extrait de boxcox_llf.__doc__] |
| boxcox_normmax(x, brack=None, method='pearsonr', optimizer=None, *, ymax=BIG_FLOAT) |
Compute optimal Box-Cox transform parameter for input data. [extrait de boxcox_normmax.__doc__] |
| boxcox_normplot(x, la, lb, plot=None, N=80) |
Compute parameters for a Box-Cox normality plot, optionally show it. [extrait de boxcox_normplot.__doc__] |
| bradford(*args, **kwds) |
A Bradford continuous random variable. [extrait de __doc__] |
| brunnermunzel(x, y, alternative='two-sided', distribution='t', nan_policy='propagate', *, axis=0, keepdims=False) |
|
| burr(*args, **kwds) |
A Burr (Type III) continuous random variable. [extrait de __doc__] |
| burr12(*args, **kwds) |
A Burr (Type XII) continuous random variable. [extrait de __doc__] |
| bws_test(x, y, *, alternative='two-sided', method=None) |
Perform the Baumgartner-Weiss-Schindler test on two independent samples. [extrait de bws_test.__doc__] |
| cauchy(*args, **kwds) |
A Cauchy continuous random variable. [extrait de __doc__] |
| chatterjeexi(x, y, *, axis=0, y_continuous=False, method='asymptotic', nan_policy='propagate', keepdims=False) |
|
| chi(*args, **kwds) |
A chi continuous random variable. [extrait de __doc__] |
| chi2(*args, **kwds) |
A chi-squared continuous random variable. [extrait de __doc__] |
| chi2_contingency(observed, correction=True, lambda_=None, *, method=None) |
Chi-square test of independence of variables in a contingency table. [extrait de chi2_contingency.__doc__] |
| chisquare(f_obs, f_exp=None, ddof=0, axis=0, *, sum_check=True) |
Perform Pearson's chi-squared test. [extrait de chisquare.__doc__] |
| circmean(samples, high=6.283185307179586, low=0, axis=None, nan_policy='propagate', *, keepdims=False) |
|
| circstd(samples, high=6.283185307179586, low=0, axis=None, nan_policy='propagate', *, normalize=False, keepdims=False) |
|
| circvar(samples, high=6.283185307179586, low=0, axis=None, nan_policy='propagate', *, keepdims=False) |
|
| combine_pvalues(pvalues, method='fisher', weights=None, *, axis=0, nan_policy='propagate', keepdims=False) |
|
| cosine(*args, **kwds) |
A cosine continuous random variable. [extrait de __doc__] |
| cramervonmises(rvs, cdf, args=(), *, axis=0, nan_policy='propagate', keepdims=False) |
|
| cramervonmises_2samp(x, y, method='auto', *, axis=0, nan_policy='propagate', keepdims=False) |
|
| crystalball(*args, **kwds) |
|
| cumfreq(a, numbins=10, defaultreallimits=None, weights=None) |
Return a cumulative frequency histogram, using the histogram function. [extrait de cumfreq.__doc__] |
| describe(a, axis=0, ddof=1, bias=True, nan_policy='propagate') |
Compute several descriptive statistics of the passed array. [extrait de describe.__doc__] |
| dgamma(*args, **kwds) |
A double gamma continuous random variable. [extrait de __doc__] |
| differential_entropy(values: Union[collections.abc.Buffer, numpy._typing._array_like._SupportsArray[numpy.dtype[Any]], numpy._typing._nested_sequence._NestedSequence[numpy._typing._array_like._SupportsArray[numpy.dtype[Any]]], bool, int, float, complex, str, bytes, numpy._typing._nested_sequence._NestedSequence[bool | int | float | complex | str | bytes]], *, window_length: int | None = None, base: float | None = None, axis: int = 0, method: str = 'auto', nan_policy='propagate', keepdims=False) -> numpy.number | numpy.ndarray |
|
| directional_stats(samples, *, axis=0, normalize=True) |
|
| dirichlet(alpha, seed=None) |
A Dirichlet random variable. [extrait de __doc__] |
| dirichlet_multinomial(alpha, n, seed=None) |
A Dirichlet multinomial random variable. [extrait de __doc__] |
| dlaplace(*args, **kwds) |
A Laplacian discrete random variable. [extrait de __doc__] |
| dpareto_lognorm(*args, **kwds) |
A double Pareto lognormal continuous random variable. [extrait de __doc__] |
| dunnett(*samples: 'npt.ArrayLike', control: 'npt.ArrayLike', alternative: Literal['two-sided', 'less', 'greater'] = 'two-sided', rng: int | numpy.integer | numpy.random._generator.Generator | numpy.random.mtrand.RandomState | None = None) -> scipy.stats._multicomp.DunnettResult |
Dunnett's test: multiple comparisons of means against a control group. [extrait de dunnett.__doc__] |
| dweibull(*args, **kwds) |
A double Weibull continuous random variable. [extrait de __doc__] |
| ecdf(sample: 'npt.ArrayLike | CensoredData') -> scipy.stats._survival.ECDFResult |
Empirical cumulative distribution function of a sample. [extrait de ecdf.__doc__] |
| energy_distance(u_values, v_values, u_weights=None, v_weights=None) |
Compute the energy distance between two 1D distributions. [extrait de energy_distance.__doc__] |
| entropy(pk: Union[collections.abc.Buffer, numpy._typing._array_like._SupportsArray[numpy.dtype[Any]], numpy._typing._nested_sequence._NestedSequence[numpy._typing._array_like._SupportsArray[numpy.dtype[Any]]], bool, int, float, complex, str, bytes, numpy._typing._nested_sequence._NestedSequence[bool | int | float | complex | str | bytes]], qk: Union[collections.abc.Buffer, numpy._typing._array_like._SupportsArray[numpy.dtype[Any]], numpy._typing._nested_sequence._NestedSequence[numpy._typing._array_like._SupportsArray[numpy.dtype[Any]]], bool, int, float, complex, str, bytes, numpy._typing._nested_sequence._NestedSequence[bool | int | float | complex | str | bytes], NoneType] = None, base: float | None = None, axis: int = 0, *, nan_policy='propagate', keepdims=False) -> numpy.number | numpy.ndarray |
|
| epps_singleton_2samp(x, y, t=(0.4, 0.8), *, axis=0, nan_policy='propagate', keepdims=False) |
|
| erlang(*args, **kwds) |
An Erlang continuous random variable. [extrait de __doc__] |
| exp(X) |
Natural exponential of a random variable [extrait de exp.__doc__] |
| expectile(a, alpha=0.5, *, weights=None) |
Compute the expectile at the specified level. [extrait de expectile.__doc__] |
| expon(*args, **kwds) |
An exponential continuous random variable. [extrait de __doc__] |
| exponnorm(*args, **kwds) |
An exponentially modified Normal continuous random variable. [extrait de __doc__] |
| exponpow(*args, **kwds) |
An exponential power continuous random variable. [extrait de __doc__] |
| exponweib(*args, **kwds) |
An exponentiated Weibull continuous random variable. [extrait de __doc__] |
| f(*args, **kwds) |
An F continuous random variable. [extrait de __doc__] |
| f_oneway(*samples, axis=0, nan_policy='propagate', keepdims=False) |
|
| false_discovery_control(ps, *, axis=0, method='bh') |
Adjust p-values to control the false discovery rate. [extrait de false_discovery_control.__doc__] |
| fatiguelife(*args, **kwds) |
A fatigue-life (Birnbaum-Saunders) continuous random variable. [extrait de __doc__] |
| find_repeats(arr) |
Find repeats and repeat counts. [extrait de find_repeats.__doc__] |
| fisher_exact(table, alternative=None, *, method=None) |
Perform a Fisher exact test on a contingency table. [extrait de fisher_exact.__doc__] |
| fisk(*args, **kwds) |
A Fisk continuous random variable. [extrait de __doc__] |
| fit(dist, data, bounds=None, *, guess=None, method='mle', optimizer=<function differential_evolution at 0x0000020DFE609580>) |
Fit a discrete or continuous distribution to data [extrait de fit.__doc__] |
| fligner(*samples, center='median', proportiontocut=0.05, axis=0, nan_policy='propagate', keepdims=False) |
|
| foldcauchy(*args, **kwds) |
A folded Cauchy continuous random variable. [extrait de __doc__] |
| foldnorm(*args, **kwds) |
A folded normal continuous random variable. [extrait de __doc__] |
| friedmanchisquare(*samples, axis=0, nan_policy='propagate', keepdims=False) |
|
| gamma(*args, **kwds) |
A gamma continuous random variable. [extrait de __doc__] |
| gausshyper(*args, **kwds) |
A Gauss hypergeometric continuous random variable. [extrait de __doc__] |
| genexpon(*args, **kwds) |
A generalized exponential continuous random variable. [extrait de __doc__] |
| genextreme(*args, **kwds) |
A generalized extreme value continuous random variable. [extrait de __doc__] |
| gengamma(*args, **kwds) |
A generalized gamma continuous random variable. [extrait de __doc__] |
| genhalflogistic(*args, **kwds) |
A generalized half-logistic continuous random variable. [extrait de __doc__] |
| genhyperbolic(*args, **kwds) |
A generalized hyperbolic continuous random variable. [extrait de __doc__] |
| geninvgauss(*args, **kwds) |
A Generalized Inverse Gaussian continuous random variable. [extrait de __doc__] |
| genlogistic(*args, **kwds) |
A generalized logistic continuous random variable. [extrait de __doc__] |
| gennorm(*args, **kwds) |
A generalized normal continuous random variable. [extrait de __doc__] |
| genpareto(*args, **kwds) |
A generalized Pareto continuous random variable. [extrait de __doc__] |
| geom(*args, **kwds) |
A geometric discrete random variable. [extrait de __doc__] |
| gibrat(*args, **kwds) |
A Gibrat continuous random variable. [extrait de __doc__] |
| gmean(a, axis=0, dtype=None, weights=None, *, nan_policy='propagate', keepdims=False) |
|
| gompertz(*args, **kwds) |
A Gompertz (or truncated Gumbel) continuous random variable. [extrait de __doc__] |
| goodness_of_fit(dist, data, *, known_params=None, fit_params=None, guessed_params=None, statistic='ad', n_mc_samples=9999, rng=None) |
|
| gstd(a, axis=0, ddof=1) |
|
| gumbel_l(*args, **kwds) |
A left-skewed Gumbel continuous random variable. [extrait de __doc__] |
| gumbel_r(*args, **kwds) |
A right-skewed Gumbel continuous random variable. [extrait de __doc__] |
| gzscore(a, *, axis=0, ddof=0, nan_policy='propagate') |
|
| halfcauchy(*args, **kwds) |
A Half-Cauchy continuous random variable. [extrait de __doc__] |
| halfgennorm(*args, **kwds) |
The upper half of a generalized normal continuous random variable. [extrait de __doc__] |
| halflogistic(*args, **kwds) |
A half-logistic continuous random variable. [extrait de __doc__] |
| halfnorm(*args, **kwds) |
A half-normal continuous random variable. [extrait de __doc__] |
| hmean(a, axis=0, dtype=None, *, weights=None, nan_policy='propagate', keepdims=False) |
|
| hypergeom(*args, **kwds) |
A hypergeometric discrete random variable. [extrait de __doc__] |
| hypsecant(*args, **kwds) |
A hyperbolic secant continuous random variable. [extrait de __doc__] |
| invgamma(*args, **kwds) |
An inverted gamma continuous random variable. [extrait de __doc__] |
| invgauss(*args, **kwds) |
An inverse Gaussian continuous random variable. [extrait de __doc__] |
| invweibull(*args, **kwds) |
An inverted Weibull continuous random variable. [extrait de __doc__] |
| invwishart(df=None, scale=None, seed=None) |
An inverse Wishart random variable. [extrait de __doc__] |
| iqr(x, axis=None, rng=(25, 75), scale=1.0, nan_policy='propagate', interpolation='linear', keepdims=False) |
|
| irwinhall(*args, **kwds) |
An Irwin-Hall (Uniform Sum) continuous random variable. [extrait de __doc__] |
| jarque_bera(x, *, axis=None, nan_policy='propagate', keepdims=False) |
|
| jf_skew_t(*args, **kwds) |
Jones and Faddy skew-t distribution. [extrait de __doc__] |
| johnsonsb(*args, **kwds) |
A Johnson SB continuous random variable. [extrait de __doc__] |
| johnsonsu(*args, **kwds) |
A Johnson SU continuous random variable. [extrait de __doc__] |
| kappa3(*args, **kwds) |
Kappa 3 parameter distribution. [extrait de __doc__] |
| kappa4(*args, **kwds) |
Kappa 4 parameter distribution. [extrait de __doc__] |
| kendalltau(x, y, *, nan_policy='propagate', method='auto', variant='b', alternative='two-sided') |
Calculate Kendall's tau, a correlation measure for ordinal data. [extrait de kendalltau.__doc__] |
| kruskal(*samples, nan_policy='propagate', axis=0, keepdims=False) |
|
| ks_1samp(x, cdf, args=(), alternative='two-sided', method='auto', *, axis=0, nan_policy='propagate', keepdims=False) |
|
| ks_2samp(data1, data2, alternative='two-sided', method='auto', *, axis=0, nan_policy='propagate', keepdims=False) |
|
| ksone(*args, **kwds) |
Kolmogorov-Smirnov one-sided test statistic distribution. [extrait de __doc__] |
| kstat(data, n=2, *, axis=None, nan_policy='propagate', keepdims=False) |
|
| kstatvar(data, n=2, *, axis=None, nan_policy='propagate', keepdims=False) |
|
| kstest(rvs, cdf, args=(), N=20, alternative='two-sided', method='auto', *, axis=0, nan_policy='propagate', keepdims=False) |
|
| kstwo(*args, **kwds) |
Kolmogorov-Smirnov two-sided test statistic distribution. [extrait de __doc__] |
| kstwobign(*args, **kwds) |
Limiting distribution of scaled Kolmogorov-Smirnov two-sided test statistic. [extrait de __doc__] |
| kurtosis(a, axis=0, fisher=True, bias=True, nan_policy='propagate', *, keepdims=False) |
|
| kurtosistest(a, axis=0, nan_policy='propagate', alternative='two-sided', *, keepdims=False) |
|
| landau(*args, **kwds) |
A Landau continuous random variable. [extrait de __doc__] |
| laplace(*args, **kwds) |
A Laplace continuous random variable. [extrait de __doc__] |
| laplace_asymmetric(*args, **kwds) |
An asymmetric Laplace continuous random variable. [extrait de __doc__] |
| levene(*samples, center='median', proportiontocut=0.05, axis=0, nan_policy='propagate', keepdims=False) |
|
| levy(*args, **kwds) |
A Levy continuous random variable. [extrait de __doc__] |
| levy_l(*args, **kwds) |
A left-skewed Levy continuous random variable. [extrait de __doc__] |
| levy_stable(*args, **params) |
A Levy-stable continuous random variable. [extrait de __doc__] |
| linregress(x, y=None, alternative='two-sided') |
|
| lmoment(sample, order=None, *, axis=0, sorted=False, standardize=True, nan_policy='propagate', keepdims=False) |
|
| log(X) |
Natural logarithm of a non-negative random variable [extrait de log.__doc__] |
| loggamma(*args, **kwds) |
A log gamma continuous random variable. [extrait de __doc__] |
| logistic(*args, **kwds) |
A logistic (or Sech-squared) continuous random variable. [extrait de __doc__] |
| loglaplace(*args, **kwds) |
A log-Laplace continuous random variable. [extrait de __doc__] |
| lognorm(*args, **kwds) |
A lognormal continuous random variable. [extrait de __doc__] |
| logrank(x: 'npt.ArrayLike | CensoredData', y: 'npt.ArrayLike | CensoredData', alternative: Literal['two-sided', 'less', 'greater'] = 'two-sided') -> scipy.stats._survival.LogRankResult |
Compare the survival distributions of two samples via the logrank test. [extrait de logrank.__doc__] |
| logser(*args, **kwds) |
A Logarithmic (Log-Series, Series) discrete random variable. [extrait de __doc__] |
| loguniform(*args, **kwds) |
A loguniform or reciprocal continuous random variable. [extrait de __doc__] |
| lomax(*args, **kwds) |
A Lomax (Pareto of the second kind) continuous random variable. [extrait de __doc__] |
| make_distribution(dist) |
Generate a `ContinuousDistribution` from an instance of `rv_continuous` [extrait de make_distribution.__doc__] |
| mannwhitneyu(x, y, use_continuity=True, alternative='two-sided', axis=0, method='auto', *, nan_policy='propagate', keepdims=False) |
|
| matrix_normal(mean=None, rowcov=1, colcov=1, seed=None) |
A matrix normal random variable. [extrait de __doc__] |
| maxwell(*args, **kwds) |
A Maxwell continuous random variable. [extrait de __doc__] |
| median_abs_deviation(x, axis=0, center=<function median at 0x0000020DE8B2F2B0>, scale=1.0, nan_policy='propagate') |
|
| median_test(*samples, ties='below', correction=True, lambda_=1, nan_policy='propagate') |
Perform a Mood's median test. [extrait de median_test.__doc__] |
| mielke(*args, **kwds) |
A Mielke Beta-Kappa / Dagum continuous random variable. [extrait de __doc__] |
| mode(a, axis=0, nan_policy='propagate', keepdims=False) |
|
| moment(a, order=1, axis=0, nan_policy='propagate', *, center=None, keepdims=False) |
|
| monte_carlo_test(data, rvs, statistic, *, vectorized=None, n_resamples=9999, batch=None, alternative='two-sided', axis=0) |
Perform a Monte Carlo hypothesis test. [extrait de monte_carlo_test.__doc__] |
| mood(x, y, axis=0, alternative='two-sided', *, nan_policy='propagate', keepdims=False) |
|
| moyal(*args, **kwds) |
A Moyal continuous random variable. [extrait de __doc__] |
| multinomial(n, p, seed=None) |
A multinomial random variable. [extrait de __doc__] |
| multiscale_graphcorr(x, y, compute_distance=<function _euclidean_dist at 0x0000020D9A0556C0>, reps=1000, workers=1, is_twosamp=False, random_state=None) |
Computes the Multiscale Graph Correlation (MGC) test statistic. [extrait de multiscale_graphcorr.__doc__] |
| multivariate_hypergeom(m, n, seed=None) |
A multivariate hypergeometric random variable. [extrait de __doc__] |
| multivariate_normal(mean=None, cov=1, allow_singular=False, seed=None) |
A multivariate normal random variable. [extrait de __doc__] |
| multivariate_t(loc=None, shape=1, df=1, allow_singular=False, seed=None) |
A multivariate t-distributed random variable. [extrait de __doc__] |
| mvsdist(data) |
|
| nakagami(*args, **kwds) |
A Nakagami continuous random variable. [extrait de __doc__] |
| nbinom(*args, **kwds) |
A negative binomial discrete random variable. [extrait de __doc__] |
| ncf(*args, **kwds) |
A non-central F distribution continuous random variable. [extrait de __doc__] |
| nchypergeom_fisher(*args, **kwds) |
A Fisher's noncentral hypergeometric discrete random variable. [extrait de __doc__] |
| nchypergeom_wallenius(*args, **kwds) |
A Wallenius' noncentral hypergeometric discrete random variable. [extrait de __doc__] |
| nct(*args, **kwds) |
A non-central Student's t continuous random variable. [extrait de __doc__] |
| ncx2(*args, **kwds) |
A non-central chi-squared continuous random variable. [extrait de __doc__] |
| nhypergeom(*args, **kwds) |
A negative hypergeometric discrete random variable. [extrait de __doc__] |
| norm(*args, **kwds) |
A normal continuous random variable. [extrait de __doc__] |
| normal_inverse_gamma(mu=0, lmbda=1, a=1, b=1, seed=None) |
Normal-inverse-gamma distribution. [extrait de __doc__] |
| normaltest(a, axis=0, nan_policy='propagate', *, keepdims=False) |
|
| norminvgauss(*args, **kwds) |
A Normal Inverse Gaussian continuous random variable. [extrait de __doc__] |
| obrientransform(*samples) |
Compute the O'Brien transform on input data (any number of arrays). [extrait de obrientransform.__doc__] |
| order_statistic(X, /, *, r, n) |
Probability distribution of an order statistic [extrait de order_statistic.__doc__] |
| ortho_group(dim=None, seed=None) |
An Orthogonal matrix (O(N)) random variable. [extrait de __doc__] |
| page_trend_test(data, ranked=False, predicted_ranks=None, method='auto') |
|
| pareto(*args, **kwds) |
A Pareto continuous random variable. [extrait de __doc__] |
| pearson3(*args, **kwds) |
A pearson type III continuous random variable. [extrait de __doc__] |
| pearsonr(x, y, *, alternative='two-sided', method=None, axis=0) |
|
| percentileofscore(a, score, kind='rank', nan_policy='propagate') |
Compute the percentile rank of a score relative to a list of scores. [extrait de percentileofscore.__doc__] |
| permutation_test(data, statistic, *, permutation_type='independent', vectorized=None, n_resamples=9999, batch=None, alternative='two-sided', axis=0, rng=None) |
|
| planck(*args, **kwds) |
A Planck discrete exponential random variable. [extrait de __doc__] |
| pmean(a, p, *, axis=0, dtype=None, weights=None, nan_policy='propagate', keepdims=False) |
|
| pointbiserialr(x, y) |
Calculate a point biserial correlation coefficient and its p-value. [extrait de pointbiserialr.__doc__] |
| poisson(*args, **kwds) |
A Poisson discrete random variable. [extrait de __doc__] |
| poisson_binom(*args, **kwds) |
A Poisson Binomial discrete random variable. [extrait de __doc__] |
| poisson_means_test(k1, n1, k2, n2, *, diff=0, alternative='two-sided') |
|
| power(test, rvs, n_observations, *, significance=0.01, vectorized=None, n_resamples=10000, batch=None, kwargs=None) |
Simulate the power of a hypothesis test under an alternative hypothesis. [extrait de power.__doc__] |
| power_divergence(f_obs, f_exp=None, ddof=0, axis=0, lambda_=None) |
Cressie-Read power divergence statistic and goodness of fit test. [extrait de power_divergence.__doc__] |
| powerlaw(*args, **kwds) |
A power-function continuous random variable. [extrait de __doc__] |
| powerlognorm(*args, **kwds) |
A power log-normal continuous random variable. [extrait de __doc__] |
| powernorm(*args, **kwds) |
A power normal continuous random variable. [extrait de __doc__] |
| ppcc_max(x, brack=(0.0, 1.0), dist='tukeylambda') |
Calculate the shape parameter that maximizes the PPCC. [extrait de ppcc_max.__doc__] |
| ppcc_plot(x, a, b, dist='tukeylambda', plot=None, N=80) |
Calculate and optionally plot probability plot correlation coefficient. [extrait de ppcc_plot.__doc__] |
| probplot(x, sparams=(), dist='norm', fit=True, plot=None, rvalue=False) |
|
| quantile_test(x, *, q=0, p=0.5, alternative='two-sided') |
|
| randint(*args, **kwds) |
A uniform discrete random variable. [extrait de __doc__] |
| random_correlation(eigs, seed=None, tol=1e-13, diag_tol=1e-07) |
A random correlation matrix. [extrait de __doc__] |
| random_table(row, col, *, seed=None) |
Contingency tables from independent samples with fixed marginal sums. [extrait de __doc__] |
| rankdata(a, method='average', *, axis=None, nan_policy='propagate') |
Assign ranks to data, dealing with ties appropriately. [extrait de rankdata.__doc__] |
| ranksums(x, y, alternative='two-sided', *, axis=0, nan_policy='propagate', keepdims=False) |
|
| rayleigh(*args, **kwds) |
A Rayleigh continuous random variable. [extrait de __doc__] |
| rdist(*args, **kwds) |
An R-distributed (symmetric beta) continuous random variable. [extrait de __doc__] |
| recipinvgauss(*args, **kwds) |
A reciprocal inverse Gaussian continuous random variable. [extrait de __doc__] |
| reciprocal(*args, **kwds) |
A loguniform or reciprocal continuous random variable. [extrait de __doc__] |
| rel_breitwigner(*args, **kwds) |
A relativistic Breit-Wigner random variable. [extrait de __doc__] |
| relfreq(a, numbins=10, defaultreallimits=None, weights=None) |
Return a relative frequency histogram, using the histogram function. [extrait de relfreq.__doc__] |
| rice(*args, **kwds) |
A Rice continuous random variable. [extrait de __doc__] |
| scoreatpercentile(a, per, limit=(), interpolation_method='fraction', axis=None) |
Calculate the score at a given percentile of the input sequence. [extrait de scoreatpercentile.__doc__] |
| sem(a, axis=0, ddof=1, nan_policy='propagate', *, keepdims=False) |
|
| semicircular(*args, **kwds) |
A semicircular continuous random variable. [extrait de __doc__] |
| shapiro(x, *, axis=None, nan_policy='propagate', keepdims=False) |
|
| siegelslopes(y, x=None, method='hierarchical') |
|
| sigmaclip(a, low=4.0, high=4.0) |
Perform iterative sigma-clipping of array elements. [extrait de sigmaclip.__doc__] |
| skellam(*args, **kwds) |
A Skellam discrete random variable. [extrait de __doc__] |
| skew(a, axis=0, bias=True, nan_policy='propagate', *, keepdims=False) |
|
| skewcauchy(*args, **kwds) |
A skewed Cauchy random variable. [extrait de __doc__] |
| skewnorm(*args, **kwds) |
A skew-normal random variable. [extrait de __doc__] |
| skewtest(a, axis=0, nan_policy='propagate', alternative='two-sided', *, keepdims=False) |
|
| sobol_indices(*, func, n, dists=None, method='saltelli_2010', rng=None) |
Global sensitivity indices of Sobol'. [extrait de sobol_indices.__doc__] |
| somersd(x, y=None, alternative='two-sided') |
Calculates Somers' D, an asymmetric measure of ordinal association. [extrait de somersd.__doc__] |
| spearmanr(a, b=None, axis=0, nan_policy='propagate', alternative='two-sided') |
Calculate a Spearman correlation coefficient with associated p-value. [extrait de spearmanr.__doc__] |
| special_ortho_group(dim=None, seed=None) |
A Special Orthogonal matrix (SO(N)) random variable. [extrait de __doc__] |
| studentized_range(*args, **kwds) |
A studentized range continuous random variable. [extrait de __doc__] |
| t(*args, **kwds) |
A Student's t continuous random variable. [extrait de __doc__] |
| test(label='fast', verbose=1, extra_argv=None, doctests=False, coverage=False, tests=None, parallel=None) |
|
| theilslopes(y, x=None, alpha=0.95, method='separate') |
|
| tiecorrect(rankvals) |
Tie correction factor for Mann-Whitney U and Kruskal-Wallis H tests. [extrait de tiecorrect.__doc__] |
| tmax(a, upperlimit=None, axis=0, inclusive=True, nan_policy='propagate', *, keepdims=False) |
|
| tmean(a, limits=None, inclusive=(True, True), axis=None, *, nan_policy='propagate', keepdims=False) |
|
| tmin(a, lowerlimit=None, axis=0, inclusive=True, nan_policy='propagate', *, keepdims=False) |
|
| trapezoid(*args, **kwds) |
A trapezoidal continuous random variable. [extrait de __doc__] |
| trapz(*args, **kwds) |
|
| triang(*args, **kwds) |
A triangular continuous random variable. [extrait de __doc__] |
| trim1(a, proportiontocut, tail='right', axis=0) |
Slice off a proportion from ONE end of the passed array distribution. [extrait de trim1.__doc__] |
| trim_mean(a, proportiontocut, axis=0) |
Return mean of array after trimming a specified fraction of extreme values [extrait de trim_mean.__doc__] |
| trimboth(a, proportiontocut, axis=0) |
Slice off a proportion of items from both ends of an array. [extrait de trimboth.__doc__] |
| truncate(X, lb=-inf, ub=inf) |
Truncate the support of a random variable. [extrait de truncate.__doc__] |
| truncexpon(*args, **kwds) |
A truncated exponential continuous random variable. [extrait de __doc__] |
| truncnorm(*args, **kwds) |
A truncated normal continuous random variable. [extrait de __doc__] |
| truncpareto(*args, **kwds) |
An upper truncated Pareto continuous random variable. [extrait de __doc__] |
| truncweibull_min(*args, **kwds) |
A doubly truncated Weibull minimum continuous random variable. [extrait de __doc__] |
| tsem(a, limits=None, inclusive=(True, True), axis=0, ddof=1, *, nan_policy='propagate', keepdims=False) |
|
| tstd(a, limits=None, inclusive=(True, True), axis=0, ddof=1, *, nan_policy='propagate', keepdims=False) |
|
| ttest_1samp(a, popmean, axis=0, nan_policy='propagate', alternative='two-sided', *, keepdims=False) |
|
| ttest_ind(a, b, *, axis=0, equal_var=True, nan_policy='propagate', permutations=None, random_state=None, alternative='two-sided', trim=0, method=None, keepdims=False) |
|
| ttest_ind_from_stats(mean1, std1, nobs1, mean2, std2, nobs2, equal_var=True, alternative='two-sided') |
|
| ttest_rel(a, b, axis=0, nan_policy='propagate', alternative='two-sided', *, keepdims=False) |
|
| tukey_hsd(*args) |
Perform Tukey's HSD test for equality of means over multiple treatments. [extrait de tukey_hsd.__doc__] |
| tukeylambda(*args, **kwds) |
A Tukey-Lamdba continuous random variable. [extrait de __doc__] |
| tvar(a, limits=None, inclusive=(True, True), axis=0, ddof=1, *, nan_policy='propagate', keepdims=False) |
|
| uniform(*args, **kwds) |
A uniform continuous random variable. [extrait de __doc__] |
| uniform_direction(dim=None, seed=None) |
A vector-valued uniform direction. [extrait de __doc__] |
| unitary_group(dim=None, seed=None) |
A matrix-valued U(N) random variable. [extrait de __doc__] |
| variation(a, axis=0, nan_policy='propagate', ddof=0, *, keepdims=False) |
|
| vonmises(*args, **kwds) |
A Von Mises continuous random variable. [extrait de __doc__] |
| vonmises_fisher(mu=None, kappa=1, seed=None) |
A von Mises-Fisher variable. [extrait de __doc__] |
| vonmises_line(*args, **kwds) |
A Von Mises continuous random variable. [extrait de __doc__] |
| wald(*args, **kwds) |
A Wald continuous random variable. [extrait de __doc__] |
| wasserstein_distance(u_values, v_values, u_weights=None, v_weights=None) |
|
| wasserstein_distance_nd(u_values, v_values, u_weights=None, v_weights=None) |
|
| weibull_max(*args, **kwds) |
Weibull maximum continuous random variable. [extrait de __doc__] |
| weibull_min(*args, **kwds) |
Weibull minimum continuous random variable. [extrait de __doc__] |
| weightedtau(x, y, rank=True, weigher=None, additive=True) |
Compute a weighted version of Kendall's :math:`\tau`. [extrait de weightedtau.__doc__] |
| wilcoxon(x, y=None, zero_method='wilcox', correction=False, alternative='two-sided', method='auto', *, axis=0, nan_policy='propagate', keepdims=False) |
|
| wishart(df=None, scale=None, seed=None) |
A Wishart random variable. [extrait de __doc__] |
| wrapcauchy(*args, **kwds) |
A wrapped Cauchy continuous random variable. [extrait de __doc__] |
| yeojohnson(x, lmbda=None) |
Return a dataset transformed by a Yeo-Johnson power transformation. [extrait de yeojohnson.__doc__] |
| yeojohnson_llf(lmb, data) |
The yeojohnson log-likelihood function. [extrait de yeojohnson_llf.__doc__] |
| yeojohnson_normmax(x, brack=None) |
Compute optimal Yeo-Johnson transform parameter. [extrait de yeojohnson_normmax.__doc__] |
| yeojohnson_normplot(x, la, lb, plot=None, N=80) |
Compute parameters for a Yeo-Johnson normality plot, optionally show it. [extrait de yeojohnson_normplot.__doc__] |
| yulesimon(*args, **kwds) |
A Yule-Simon discrete random variable. [extrait de __doc__] |
| zipf(*args, **kwds) |
A Zipf (Zeta) discrete random variable. [extrait de __doc__] |
| zipfian(*args, **kwds) |
A Zipfian discrete random variable. [extrait de __doc__] |
| zmap(scores, compare, axis=0, ddof=0, nan_policy='propagate') |
|
| zscore(a, axis=0, ddof=0, nan_policy='propagate') |
|
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