Tessero\Stats

apply(string $name, mixed ...$args): mixed

Call a function by its SciPy/NumPy name: Stats::apply('log_ndtr', $x).

alexandergovern(mixed $sample1, mixed $sample2, mixed $sample3 = null, mixed $sample4 = null, mixed $sample5 = null, mixed $sample6 = null, mixed $sample7 = null, mixed $sample8 = null, mixed $sample9 = null, mixed $sample10 = null, mixed $nanPolicy = 'propagate', mixed $axis = 0, mixed $keepdims = false): mixed

Alexander-Govern test (scipy.stats.alexandergovern); the samples are sample1, sample2, ... (up to 10).

alpha(mixed $a, mixed $loc = 0, mixed $scale = 1): Tessero\Stats\Distribution

An alpha continuous random variable.

anderson(mixed $x, mixed $dist = 'norm', mixed $method = null): mixed

Anderson-Darling test (scipy.stats.anderson) for norm, expon, logistic, gumbel_r, gumbel_l (gumbel, extreme1); method='interpolate' gives a p-value.

andersonKsamp(mixed $samples, mixed $midrank = null, mixed $variant = null, mixed $method = null): mixed

k-sample Anderson-Darling test (scipy.stats.anderson_ksamp); samples: one sample per row of a 2-D array.

anglit(mixed $loc = 0, mixed $scale = 1): Tessero\Stats\Distribution

An anglit continuous random variable.

ansari(mixed $x, mixed $y, mixed $alternative = 'two-sided', mixed $axis = 0, mixed $nanPolicy = 'propagate', mixed $keepdims = false): mixed

Ansari-Bradley test for equal scale parameters (scipy.stats.ansari).

arcsine(mixed $loc = 0, mixed $scale = 1): Tessero\Stats\Distribution

An arcsine continuous random variable.

argus(mixed $chi, mixed $loc = 0, mixed $scale = 1): Tessero\Stats\Distribution

Argus distribution

bartlett(mixed $sample1, mixed $sample2 = null, mixed $sample3 = null, mixed $sample4 = null, mixed $sample5 = null, mixed $sample6 = null, mixed $sample7 = null, mixed $sample8 = null, mixed $sample9 = null, mixed $sample10 = null, mixed $sample11 = null, mixed $sample12 = null, mixed $axis = 0, mixed $nanPolicy = 'propagate', mixed $keepdims = false): mixed

Bartlett's test for equal variances (scipy.stats.bartlett).

bernoulli(mixed $p, mixed $loc = 0): Tessero\Stats\Distribution

A Bernoulli discrete random variable.

beta(mixed $a, mixed $b, mixed $loc = 0, mixed $scale = 1): Tessero\Stats\Distribution

A beta continuous random variable.

betabinom(mixed $n, mixed $a, mixed $b, mixed $loc = 0): Tessero\Stats\Distribution

A beta-binomial discrete random variable.

betanbinom(mixed $n, mixed $a, mixed $b, mixed $loc = 0): Tessero\Stats\Distribution

A beta-negative-binomial discrete random variable.

betaprime(mixed $a, mixed $b, mixed $loc = 0, mixed $scale = 1): Tessero\Stats\Distribution

A beta prime continuous random variable.

binnedStatistic(mixed $x, mixed $values, mixed $statistic = 'mean', mixed $bins = 10, mixed $range = null): mixed

A statistic of the values in each bin of x (scipy.stats.binned_statistic; named statistics only).

binnedStatistic2d(mixed $x, mixed $y, mixed $values, mixed $statistic = 'mean', mixed $bins = 10, mixed $range = null, mixed $expandBinnumbers = false): mixed

A two-dimensional binned statistic (scipy.stats.binned_statistic_2d; named statistics only).

binnedStatisticDd(mixed $sample, mixed $values, mixed $statistic = 'mean', mixed $bins = 10, mixed $range = null, mixed $expandBinnumbers = false, mixed $binnedStatisticResult = null): mixed

A multidimensional binned statistic (scipy.stats.binned_statistic_dd; named statistics only; bin_edges as one (D, k) array).

binom(mixed $n, mixed $p, mixed $loc = 0): Tessero\Stats\Distribution

A binomial discrete random variable.

binomtest(mixed $k, mixed $n, mixed $p = 0.5, mixed $alternative = 'two-sided'): mixed

Test that the probability of success is p (scipy.stats.binomtest).

boltzmann(mixed $lambda_, mixed $N, mixed $loc = 0): Tessero\Stats\Distribution

A Boltzmann (Truncated Discrete Exponential) random variable.

boxcox(mixed $x, mixed $lmbda = null, mixed $alpha = null, mixed $optimizer = null): mixed

The Box-Cox power transformation with a given lambda (scipy.stats.boxcox; lmbda required).

boxcoxLlf(mixed $lmb, mixed $data, mixed $axis = 0, mixed $keepdims = false, mixed $nanPolicy = 'propagate'): mixed

Box-Cox log-likelihood (scipy.stats.boxcox_llf).

boxcoxNormplot(mixed $x, mixed $la, mixed $lb, mixed $N = 80): mixed

Box-Cox normality plot: lambdas and probability-plot correlations (scipy.stats.boxcox_normplot).

bradford(mixed $c, mixed $loc = 0, mixed $scale = 1): Tessero\Stats\Distribution

A Bradford continuous random variable.

brunnermunzel(mixed $x, mixed $y, mixed $alternative = 'two-sided', mixed $distribution = 't', mixed $nanPolicy = 'propagate', mixed $axis = 0, mixed $keepdims = false): mixed

Brunner-Munzel test on samples x and y (scipy.stats.brunnermunzel).

burr(mixed $c, mixed $d, mixed $loc = 0, mixed $scale = 1): Tessero\Stats\Distribution

A Burr (Type III) continuous random variable.

burr12(mixed $c, mixed $d, mixed $loc = 0, mixed $scale = 1): Tessero\Stats\Distribution

A Burr (Type XII) continuous random variable.

bwsTest(mixed $x, mixed $y, mixed $alternative = 'two-sided', mixed $method = null): mixed

Baumgartner-Weiss-Schindler test (scipy.stats.bws_test) with the exact permutation distribution (at most 9999 partitions).

cauchy(mixed $loc = 0, mixed $scale = 1): Tessero\Stats\Distribution

A Cauchy continuous random variable.

chatterjeexi(mixed $x, mixed $y, array|int|null $axis = 0, int|float|bool|null $yContinuous = false, string $nanPolicy = 'propagate', bool $keepdims = false): array

Chatterjee's xi correlation with the asymptotic p-value (scipy.stats.chatterjeexi; method='asymptotic').

chi(mixed $df, mixed $loc = 0, mixed $scale = 1): Tessero\Stats\Distribution

A chi continuous random variable.

chi2(mixed $df, mixed $loc = 0, mixed $scale = 1): Tessero\Stats\Distribution

A chi-squared continuous random variable.

chi2Contingency(mixed $observed, mixed $correction = true, mixed $lambda_ = null, mixed $method = null): mixed

Chi-square test of independence of variables in a contingency table (scipy.stats.chi2_contingency).

chisquare(mixed $fObs, mixed $fExp = null, mixed $ddof = 0, mixed $axis = 0, mixed $sumCheck = true, mixed $nanPolicy = 'propagate', mixed $keepdims = false): mixed

Pearson's chi-squared test (scipy.stats.chisquare).

circmean(mixed $samples, mixed $high = 6.283185307179586, mixed $low = 0, array|int|null $axis = null, string $nanPolicy = 'propagate', bool $keepdims = false): Tessero\NDArray|int|float

Circular mean of samples in [low, high] (scipy.stats.circmean).

circstd(mixed $samples, mixed $high = 6.283185307179586, mixed $low = 0, array|int|null $axis = null, string $nanPolicy = 'propagate', int|float|bool|null $normalize = false, bool $keepdims = false): Tessero\NDArray|int|float

Circular standard deviation, sqrt(-2 log R) (scipy.stats.circstd).

circvar(mixed $samples, mixed $high = 6.283185307179586, mixed $low = 0, array|int|null $axis = null, string $nanPolicy = 'propagate', bool $keepdims = false): Tessero\NDArray|int|float

Circular variance, 1 - R (scipy.stats.circvar).

combinePvalues(mixed $pvalues, mixed $method = 'fisher', mixed $weights = null, mixed $axis = 0, mixed $nanPolicy = 'propagate', mixed $keepdims = false): mixed

Combine p-values of independent tests (scipy.stats.combine_pvalues).

cosine(mixed $loc = 0, mixed $scale = 1): Tessero\Stats\Distribution

A cosine continuous random variable.

cramervonmises(mixed $rvs, mixed $cdf, mixed $args = null, mixed $axis = 0, mixed $nanPolicy = 'propagate', mixed $keepdims = false): mixed

One-sample Cramer-von Mises test (scipy.stats.cramervonmises); cdf is a distribution name (norm, expon, uniform, gamma, beta, t, chi2, f), args its parameters.

cramervonmises2samp(mixed $x, mixed $y, mixed $method = 'auto', mixed $axis = 0, mixed $nanPolicy = 'propagate', mixed $keepdims = false): mixed

Two-sample Cramer-von Mises test (scipy.stats.cramervonmises_2samp).

crystalball(mixed $beta, mixed $m, mixed $loc = 0, mixed $scale = 1): Tessero\Stats\Distribution

Crystalball distribution

cumfreq(mixed $a, mixed $numbins = 10, mixed $defaultreallimits = null, mixed $weights = null): mixed

Cumulative frequency histogram (scipy.stats.cumfreq).

describe(mixed $a, mixed $axis = 0, mixed $ddof = 1, mixed $bias = true, mixed $nanPolicy = 'propagate'): mixed

Descriptive statistics; minmax is returned as one array stacking (min, max) (scipy.stats.describe).

dgamma(mixed $a, mixed $loc = 0, mixed $scale = 1): Tessero\Stats\Distribution

A double gamma continuous random variable.

differentialEntropy(mixed $values, array|int|null $axis = 0, int|float|bool|null $windowLength = null, int|float|bool|null $base = null, string $method = 'auto', string $nanPolicy = 'propagate', bool $keepdims = false): Tessero\NDArray|int|float

Differential entropy estimated from a sample (scipy.stats.differential_entropy).

directionalStats(mixed $samples): mixed

Mean direction and resultant length of unit vectors (scipy.stats.directional_stats).

dirichlet(mixed $alpha, mixed $x): mixed

Dirichlet pdf at each row of x (scipy.stats.dirichlet).

dirichletMultinomial(mixed $alpha, mixed $n, mixed $x): mixed

Dirichlet-multinomial pmf at each row of x (scipy.stats.dirichlet_multinomial).

dlaplace(mixed $a, mixed $loc = 0): Tessero\Stats\Distribution

A Laplacian discrete random variable.

dparetoLognorm(mixed $u, mixed $s, mixed $a, mixed $b, mixed $loc = 0, mixed $scale = 1): Tessero\Stats\Distribution

A double Pareto lognormal continuous random variable.

dweibull(mixed $c, mixed $loc = 0, mixed $scale = 1): Tessero\Stats\Distribution

A double Weibull continuous random variable.

ecdf(mixed $sample): mixed

Empirical CDF: sorted unique values and cumulative proportions (scipy.stats.ecdf).

energyDistance(mixed $uValues, mixed $vValues, mixed $uWeights = null, mixed $vWeights = null): mixed

Energy distance between two 1-D distributions (scipy.stats.energy_distance).

entropy(mixed $pk, mixed $qk = null, array|int|null $axis = 0, int|float|bool|null $base = null, string $nanPolicy = 'propagate', bool $keepdims = false): Tessero\NDArray|int|float

Shannon entropy of pk, or the relative entropy D(pk||qk) (scipy.stats.entropy).

eppsSingleton2samp(mixed $x, mixed $y, mixed $t = null, mixed $axis = 0, mixed $nanPolicy = 'propagate', mixed $keepdims = false): mixed

Epps-Singleton two-sample test (scipy.stats.epps_singleton_2samp); t defaults to (0.4, 0.8).

erlang(mixed $a, mixed $loc = 0, mixed $scale = 1): Tessero\Stats\Distribution

An Erlang continuous random variable.

expectile(mixed $a, mixed $alpha = 0.5, mixed $weights = null): mixed

Expectile of the data at level alpha (scipy.stats.expectile).

expon(mixed $loc = 0, mixed $scale = 1): Tessero\Stats\Distribution

An exponential continuous random variable.

exponnorm(mixed $K, mixed $loc = 0, mixed $scale = 1): Tessero\Stats\Distribution

An exponentially modified Normal continuous random variable.

exponpow(mixed $b, mixed $loc = 0, mixed $scale = 1): Tessero\Stats\Distribution

An exponential power continuous random variable.

exponweib(mixed $a, mixed $c, mixed $loc = 0, mixed $scale = 1): Tessero\Stats\Distribution

An exponentiated Weibull continuous random variable.

f(mixed $dfn, mixed $dfd, mixed $loc = 0, mixed $scale = 1): Tessero\Stats\Distribution

An F continuous random variable.

fOneway(mixed $sample1, mixed $sample2 = null, mixed $sample3 = null, mixed $sample4 = null, mixed $sample5 = null, mixed $sample6 = null, mixed $sample7 = null, mixed $sample8 = null, mixed $sample9 = null, mixed $sample10 = null, mixed $sample11 = null, mixed $sample12 = null, mixed $axis = 0, mixed $equalVar = true, mixed $nanPolicy = 'propagate', mixed $keepdims = false): mixed

One-way ANOVA; Welch's ANOVA with equal_var=False (scipy.stats.f_oneway).

falseDiscoveryControl(mixed $ps, ?int $axis = 0, string $method = 'bh'): Tessero\NDArray|int|float

Benjamini-Hochberg / Benjamini-Yekutieli adjusted p-values (scipy.stats.false_discovery_control).

fatiguelife(mixed $c, mixed $loc = 0, mixed $scale = 1): Tessero\Stats\Distribution

A fatigue-life (Birnbaum-Saunders) continuous random variable.

fisherExact(mixed $table, mixed $alternative = null, mixed $method = null): mixed

Fisher exact test on a 2x2 contingency table (scipy.stats.fisher_exact).

fisk(mixed $c, mixed $loc = 0, mixed $scale = 1): Tessero\Stats\Distribution

A Fisk continuous random variable.

fligner(mixed $sample1, mixed $sample2 = null, mixed $sample3 = null, mixed $sample4 = null, mixed $sample5 = null, mixed $sample6 = null, mixed $sample7 = null, mixed $sample8 = null, mixed $sample9 = null, mixed $sample10 = null, mixed $sample11 = null, mixed $sample12 = null, mixed $center = 'median', mixed $proportiontocut = 0.05, mixed $axis = 0, mixed $nanPolicy = 'propagate', mixed $keepdims = false): mixed

Fligner-Killeen test for equality of variance (scipy.stats.fligner).

foldcauchy(mixed $c, mixed $loc = 0, mixed $scale = 1): Tessero\Stats\Distribution

A folded Cauchy continuous random variable.

foldnorm(mixed $c, mixed $loc = 0, mixed $scale = 1): Tessero\Stats\Distribution

A folded normal continuous random variable.

friedmanchisquare(mixed $sample1, mixed $sample2 = null, mixed $sample3 = null, mixed $sample4 = null, mixed $sample5 = null, mixed $sample6 = null, mixed $sample7 = null, mixed $sample8 = null, mixed $sample9 = null, mixed $sample10 = null, mixed $sample11 = null, mixed $sample12 = null, mixed $axis = 0, mixed $nanPolicy = 'propagate', mixed $keepdims = false): mixed

Friedman test for repeated samples (scipy.stats.friedmanchisquare).

gamma(mixed $a, mixed $loc = 0, mixed $scale = 1): Tessero\Stats\Distribution

A gamma continuous random variable.

gausshyper(mixed $a, mixed $b, mixed $c, mixed $z, mixed $loc = 0, mixed $scale = 1): Tessero\Stats\Distribution

A Gauss hypergeometric continuous random variable.

gaussianKde(mixed $dataset, mixed $points): mixed

Gaussian kernel density estimate (Scott's factor) evaluated at points (scipy.stats.gaussian_kde).

genexpon(mixed $a, mixed $b, mixed $c, mixed $loc = 0, mixed $scale = 1): Tessero\Stats\Distribution

A generalized exponential continuous random variable.

genextreme(mixed $c, mixed $loc = 0, mixed $scale = 1): Tessero\Stats\Distribution

A generalized extreme value continuous random variable.

gengamma(mixed $a, mixed $c, mixed $loc = 0, mixed $scale = 1): Tessero\Stats\Distribution

A generalized gamma continuous random variable.

genhalflogistic(mixed $c, mixed $loc = 0, mixed $scale = 1): Tessero\Stats\Distribution

A generalized half-logistic continuous random variable.

genhyperbolic(mixed $p, mixed $a, mixed $b, mixed $loc = 0, mixed $scale = 1): Tessero\Stats\Distribution

A generalized hyperbolic continuous random variable.

geninvgauss(mixed $p, mixed $b, mixed $loc = 0, mixed $scale = 1): Tessero\Stats\Distribution

A Generalized Inverse Gaussian continuous random variable.

genlogistic(mixed $c, mixed $loc = 0, mixed $scale = 1): Tessero\Stats\Distribution

A generalized logistic continuous random variable.

gennorm(mixed $beta, mixed $loc = 0, mixed $scale = 1): Tessero\Stats\Distribution

A generalized normal continuous random variable.

genpareto(mixed $c, mixed $loc = 0, mixed $scale = 1): Tessero\Stats\Distribution

A generalized Pareto continuous random variable.

geom(mixed $p, mixed $loc = 0): Tessero\Stats\Distribution

A geometric discrete random variable.

gibrat(mixed $loc = 0, mixed $scale = 1): Tessero\Stats\Distribution

A Gibrat continuous random variable.

gmean(mixed $a, mixed $weights = null, array|int|null $axis = 0, string $nanPolicy = 'propagate', bool $keepdims = false): Tessero\NDArray|int|float

Weighted geometric mean along an axis (scipy.stats.gmean; dtype is not supported).

gompertz(mixed $c, mixed $loc = 0, mixed $scale = 1): Tessero\Stats\Distribution

A Gompertz (or truncated Gumbel) continuous random variable.

gstd(mixed $a, array|int|null $axis = 0, int|float|bool|null $ddof = 1, string $nanPolicy = 'propagate', bool $keepdims = false): Tessero\NDArray|int|float

Geometric standard deviation (scipy.stats.gstd).

gumbelL(mixed $loc = 0, mixed $scale = 1): Tessero\Stats\Distribution

A left-skewed Gumbel continuous random variable.

gumbelR(mixed $loc = 0, mixed $scale = 1): Tessero\Stats\Distribution

A right-skewed Gumbel continuous random variable.

gzscore(mixed $a, ?int $axis = 0, int|float|bool|null $ddof = 0, string $nanPolicy = 'propagate'): Tessero\NDArray|int|float

Geometric z-scores: z-scores of log(a) (scipy.stats.gzscore).

halfcauchy(mixed $loc = 0, mixed $scale = 1): Tessero\Stats\Distribution

A Half-Cauchy continuous random variable.

halfgennorm(mixed $beta, mixed $loc = 0, mixed $scale = 1): Tessero\Stats\Distribution

The upper half of a generalized normal continuous random variable.

halflogistic(mixed $loc = 0, mixed $scale = 1): Tessero\Stats\Distribution

A half-logistic continuous random variable.

halfnorm(mixed $loc = 0, mixed $scale = 1): Tessero\Stats\Distribution

A half-normal continuous random variable.

hmean(mixed $a, mixed $weights = null, array|int|null $axis = 0, string $nanPolicy = 'propagate', bool $keepdims = false): Tessero\NDArray|int|float

Weighted harmonic mean along an axis (scipy.stats.hmean; dtype is not supported).

hypergeom(mixed $M, mixed $n, mixed $N, mixed $loc = 0): Tessero\Stats\Distribution

A hypergeometric discrete random variable.

hypsecant(mixed $loc = 0, mixed $scale = 1): Tessero\Stats\Distribution

A hyperbolic secant continuous random variable.

invgamma(mixed $a, mixed $loc = 0, mixed $scale = 1): Tessero\Stats\Distribution

An inverted gamma continuous random variable.

invgauss(mixed $mu, mixed $loc = 0, mixed $scale = 1): Tessero\Stats\Distribution

An inverse Gaussian continuous random variable.

invweibull(mixed $c, mixed $loc = 0, mixed $scale = 1): Tessero\Stats\Distribution

An inverted Weibull continuous random variable.

invwishart(mixed $df, mixed $scale, mixed $x): mixed

Inverse-Wishart pdf and log-pdf at an SPD matrix x (scipy.stats.invwishart).

iqr(mixed $x, mixed $axis = null, mixed $rng = null, mixed $scale = 1.0, mixed $nanPolicy = 'propagate', mixed $interpolation = 'linear', mixed $keepdims = false): mixed

Interquartile range; rng=None means (25, 75) (scipy.stats.iqr).

irwinhall(mixed $n, mixed $loc = 0, mixed $scale = 1): Tessero\Stats\Distribution

An Irwin-Hall (Uniform Sum) continuous random variable.

jarqueBera(mixed $x, mixed $axis = null, mixed $nanPolicy = 'propagate', mixed $keepdims = false): mixed

Jarque-Bera goodness of fit test on sample data (scipy.stats.jarque_bera).

jfSkewT(mixed $a, mixed $b, mixed $loc = 0, mixed $scale = 1): Tessero\Stats\Distribution

Jones and Faddy skew-t distribution.

johnsonsb(mixed $a, mixed $b, mixed $loc = 0, mixed $scale = 1): Tessero\Stats\Distribution

A Johnson SB continuous random variable.

johnsonsu(mixed $a, mixed $b, mixed $loc = 0, mixed $scale = 1): Tessero\Stats\Distribution

A Johnson SU continuous random variable.

kappa3(mixed $a, mixed $loc = 0, mixed $scale = 1): Tessero\Stats\Distribution

Kappa 3 parameter distribution.

kappa4(mixed $h, mixed $k, mixed $loc = 0, mixed $scale = 1): Tessero\Stats\Distribution

Kappa 4 parameter distribution.

kendalltau(mixed $x, mixed $y, array|int|null $axis = null, string $method = 'auto', string $variant = 'b', string $alternative = 'two-sided', string $nanPolicy = 'propagate', bool $keepdims = false): array

Kendall's tau-b or tau-c with an exact or asymptotic p-value (scipy.stats.kendalltau).

kruskal(mixed $sample1, mixed $sample2 = null, mixed $sample3 = null, mixed $sample4 = null, mixed $sample5 = null, mixed $sample6 = null, mixed $sample7 = null, mixed $sample8 = null, mixed $sample9 = null, mixed $sample10 = null, mixed $sample11 = null, mixed $sample12 = null, mixed $nanPolicy = 'propagate', mixed $axis = 0, mixed $keepdims = false): mixed

Kruskal-Wallis H-test for independent samples (scipy.stats.kruskal).

ks1samp(mixed $x, mixed $cdf, mixed $args = null, mixed $alternative = 'two-sided', mixed $method = 'auto', mixed $axis = 0, mixed $nanPolicy = 'propagate', mixed $keepdims = false): mixed

One-sample Kolmogorov-Smirnov test (scipy.stats.ks_1samp); cdf is a distribution name, args its parameters.

ks2samp(mixed $data1, mixed $data2, mixed $alternative = 'two-sided', mixed $method = 'auto', mixed $axis = 0, mixed $nanPolicy = 'propagate', mixed $keepdims = false): mixed

Two-sample Kolmogorov-Smirnov test (scipy.stats.ks_2samp).

ksone(mixed $n, mixed $loc = 0, mixed $scale = 1): Tessero\Stats\Distribution

Kolmogorov-Smirnov one-sided test statistic distribution.

kstat(mixed $data, mixed $n = 2, array|int|null $axis = null, string $nanPolicy = 'propagate', bool $keepdims = false): Tessero\NDArray|int|float

n-th k-statistic, the unbiased estimator of the n-th cumulant, 1 <= n <= 4 (scipy.stats.kstat).

kstatvar(mixed $data, mixed $n = 2, array|int|null $axis = null, string $nanPolicy = 'propagate', bool $keepdims = false): Tessero\NDArray|int|float

Unbiased estimator of the variance of the k-statistic, n = 1 or 2 (scipy.stats.kstatvar).

kstest(mixed $rvs, mixed $cdf, mixed $args = null, mixed $N = 20, mixed $alternative = 'two-sided', mixed $method = 'auto', mixed $axis = 0, mixed $nanPolicy = 'propagate', mixed $keepdims = false): mixed

Kolmogorov-Smirnov test (scipy.stats.kstest): cdf is a distribution name (one-sample) or a second sample.

kstwo(mixed $n, mixed $loc = 0, mixed $scale = 1): Tessero\Stats\Distribution

Kolmogorov-Smirnov two-sided test statistic distribution.

kstwobign(mixed $loc = 0, mixed $scale = 1): Tessero\Stats\Distribution

Limiting distribution of scaled Kolmogorov-Smirnov two-sided test statistic.

kurtosis(mixed $a, array|int|null $axis = 0, int|float|bool|null $fisher = true, int|float|bool|null $bias = true, string $nanPolicy = 'propagate', bool $keepdims = false): Tessero\NDArray|int|float

Kurtosis (Fisher or Pearson) of a data set (scipy.stats.kurtosis).

kurtosistest(mixed $a, mixed $axis = 0, mixed $nanPolicy = 'propagate', mixed $alternative = 'two-sided', mixed $keepdims = false): mixed

Test whether a dataset has normal kurtosis (scipy.stats.kurtosistest).

landau(mixed $loc = 0, mixed $scale = 1): Tessero\Stats\Distribution

A Landau continuous random variable.

laplace(mixed $loc = 0, mixed $scale = 1): Tessero\Stats\Distribution

A Laplace continuous random variable.

laplaceAsymmetric(mixed $kappa, mixed $loc = 0, mixed $scale = 1): Tessero\Stats\Distribution

An asymmetric Laplace continuous random variable.

levene(mixed $sample1, mixed $sample2 = null, mixed $sample3 = null, mixed $sample4 = null, mixed $sample5 = null, mixed $sample6 = null, mixed $sample7 = null, mixed $sample8 = null, mixed $sample9 = null, mixed $sample10 = null, mixed $sample11 = null, mixed $sample12 = null, mixed $center = 'median', mixed $proportiontocut = 0.05, mixed $axis = 0, mixed $nanPolicy = 'propagate', mixed $keepdims = false): mixed

Levene test for equal variances (scipy.stats.levene).

levy(mixed $loc = 0, mixed $scale = 1): Tessero\Stats\Distribution

A Levy continuous random variable.

levyL(mixed $loc = 0, mixed $scale = 1): Tessero\Stats\Distribution

A left-skewed Levy continuous random variable.

levyStable(mixed $alpha, mixed $beta, mixed $loc = 0, mixed $scale = 1): Tessero\Stats\Distribution

A Levy-stable continuous random variable.

linregress(mixed $x, mixed $y, mixed $alternative = 'two-sided', mixed $axis = 0, mixed $nanPolicy = 'propagate', mixed $keepdims = false): mixed

Least-squares regression line of two sets of measurements (scipy.stats.linregress).

lmoment(mixed $sample, mixed $order = null, mixed $axis = 0, mixed $sorted = false, mixed $standardize = true, mixed $nanPolicy = 'propagate', mixed $keepdims = false): mixed

Sample L-moments, standardized as L-moment ratios by default (scipy.stats.lmoment).

loggamma(mixed $c, mixed $loc = 0, mixed $scale = 1): Tessero\Stats\Distribution

A log gamma continuous random variable.

logistic(mixed $loc = 0, mixed $scale = 1): Tessero\Stats\Distribution

A logistic (or Sech-squared) continuous random variable.

loglaplace(mixed $c, mixed $loc = 0, mixed $scale = 1): Tessero\Stats\Distribution

A log-Laplace continuous random variable.

lognorm(mixed $s, mixed $loc = 0, mixed $scale = 1): Tessero\Stats\Distribution

A lognormal continuous random variable.

logser(mixed $p, mixed $loc = 0): Tessero\Stats\Distribution

A Logarithmic (Log-Series, Series) discrete random variable.

loguniform(mixed $a, mixed $b, mixed $loc = 0, mixed $scale = 1): Tessero\Stats\Distribution

A loguniform or reciprocal continuous random variable.

lomax(mixed $c, mixed $loc = 0, mixed $scale = 1): Tessero\Stats\Distribution

A Lomax (Pareto of the second kind) continuous random variable.

mannwhitneyu(mixed $x, mixed $y, mixed $useContinuity = true, mixed $alternative = 'two-sided', mixed $axis = 0, mixed $method = 'auto', mixed $nanPolicy = 'propagate', mixed $keepdims = false): mixed

Mann-Whitney U rank test on two independent samples (scipy.stats.mannwhitneyu).

matrixNormal(mixed $mean, mixed $rowcov, mixed $colcov, mixed $x): mixed

Matrix-normal pdf and log-pdf at a matrix x (scipy.stats.matrix_normal).

maxwell(mixed $loc = 0, mixed $scale = 1): Tessero\Stats\Distribution

A Maxwell continuous random variable.

medianAbsDeviation(mixed $x, mixed $axis = 0, mixed $center = null, mixed $scale = 1.0, mixed $nanPolicy = 'propagate', mixed $keepdims = false): mixed

Median absolute deviation; center must be None (the median) (scipy.stats.median_abs_deviation).

medianTest(mixed $sample1, mixed $sample2 = null, mixed $sample3 = null, mixed $sample4 = null, mixed $sample5 = null, mixed $sample6 = null, mixed $sample7 = null, mixed $sample8 = null, mixed $sample9 = null, mixed $sample10 = null, mixed $sample11 = null, mixed $sample12 = null, mixed $ties = 'below', mixed $correction = true, mixed $lambda_ = 1, mixed $nanPolicy = 'propagate'): mixed

Mood's median test (scipy.stats.median_test).

mielke(mixed $k, mixed $s, mixed $loc = 0, mixed $scale = 1): Tessero\Stats\Distribution

A Mielke Beta-Kappa / Dagum continuous random variable.

mode(mixed $a, mixed $axis = 0, mixed $nanPolicy = 'propagate', mixed $keepdims = false): mixed

Modal (most common) value and its count; ties give the smallest value (scipy.stats.mode).

moment(mixed $a, mixed $order = 1, array|int|null $axis = 0, string $nanPolicy = 'propagate', int|float|bool|null $center = null, bool $keepdims = false): Tessero\NDArray|int|float

n-th central moment (or about center) of a sample; an array of orders gives one result each (scipy.stats.moment).

mood(mixed $x, mixed $y, mixed $axis = 0, mixed $alternative = 'two-sided', mixed $nanPolicy = 'propagate', mixed $keepdims = false): mixed

Mood's test for equal scale parameters (scipy.stats.mood).

moyal(mixed $loc = 0, mixed $scale = 1): Tessero\Stats\Distribution

A Moyal continuous random variable.

multinomial(mixed $n, mixed $p, mixed $x): mixed

Multinomial pmf at each row of x (scipy.stats.multinomial).

multivariateHypergeom(mixed $m, mixed $n, mixed $x): mixed

Multivariate hypergeometric pmf at each row of x (scipy.stats.multivariate_hypergeom).

multivariateNormal(mixed $mean, mixed $cov, mixed $x): mixed

Multivariate normal pdf and log-pdf at each row of x (scipy.stats.multivariate_normal).

multivariateT(mixed $loc, mixed $shape, mixed $df, mixed $x): mixed

Multivariate Student-t pdf and log-pdf at each row of x (scipy.stats.multivariate_t).

nakagami(mixed $nu, mixed $loc = 0, mixed $scale = 1): Tessero\Stats\Distribution

A Nakagami continuous random variable.

nbinom(mixed $n, mixed $p, mixed $loc = 0): Tessero\Stats\Distribution

A negative binomial discrete random variable.

ncf(mixed $dfn, mixed $dfd, mixed $nc, mixed $loc = 0, mixed $scale = 1): Tessero\Stats\Distribution

A non-central F distribution continuous random variable.

nchypergeomFisher(mixed $M, mixed $n, mixed $N, mixed $odds, mixed $loc = 0): Tessero\Stats\Distribution

A Fisher's noncentral hypergeometric discrete random variable.

nchypergeomWallenius(mixed $M, mixed $n, mixed $N, mixed $odds, mixed $loc = 0): Tessero\Stats\Distribution

A Wallenius' noncentral hypergeometric discrete random variable.

nct(mixed $df, mixed $nc, mixed $loc = 0, mixed $scale = 1): Tessero\Stats\Distribution

A non-central Student's t continuous random variable.

ncx2(mixed $df, mixed $nc, mixed $loc = 0, mixed $scale = 1): Tessero\Stats\Distribution

A non-central chi-squared continuous random variable.

nhypergeom(mixed $M, mixed $n, mixed $r, mixed $loc = 0): Tessero\Stats\Distribution

A negative hypergeometric discrete random variable.

norm(mixed $loc = 0, mixed $scale = 1): Tessero\Stats\Distribution

A normal continuous random variable.

normaltest(mixed $a, mixed $axis = 0, mixed $nanPolicy = 'propagate', mixed $keepdims = false): mixed

D'Agostino and Pearson's test for normality (scipy.stats.normaltest).

norminvgauss(mixed $a, mixed $b, mixed $loc = 0, mixed $scale = 1): Tessero\Stats\Distribution

A Normal Inverse Gaussian continuous random variable.

obrientransform(mixed $sample1, mixed $sample2 = null, mixed $sample3 = null, mixed $sample4 = null, mixed $sample5 = null, mixed $sample6 = null, mixed $sample7 = null, mixed $sample8 = null): mixed

O'Brien transform of one or more equal-length samples (scipy.stats.obrientransform).

pageTrendTest(mixed $data, mixed $ranked = false, mixed $predictedRanks = null, mixed $method = 'auto'): mixed

Page's test for ordered alternatives (scipy.stats.page_trend_test); the method used is not returned.

pareto(mixed $b, mixed $loc = 0, mixed $scale = 1): Tessero\Stats\Distribution

A Pareto continuous random variable.

pearson3(mixed $skew, mixed $loc = 0, mixed $scale = 1): Tessero\Stats\Distribution

A pearson type III continuous random variable.

pearsonr(mixed $x, mixed $y, ?int $axis = 0, string $alternative = 'two-sided'): array

Pearson correlation coefficient and p-value (scipy.stats.pearsonr; method=None only, the confidence_interval method is not provided).

percentileofscore(mixed $a, mixed $score, mixed $kind = 'rank', mixed $nanPolicy = 'propagate'): mixed

Percentile rank of score(s) relative to a (scipy.stats.percentileofscore).

planck(mixed $lambda_, mixed $loc = 0): Tessero\Stats\Distribution

A Planck discrete exponential random variable.

pmean(mixed $a, mixed $p, mixed $weights = null, array|int|null $axis = 0, string $nanPolicy = 'propagate', bool $keepdims = false): Tessero\NDArray|int|float

Weighted power mean along an axis (scipy.stats.pmean; dtype is not supported).

pointbiserialr(mixed $x, mixed $y, array|int|null $axis = 0, string $nanPolicy = 'propagate', bool $keepdims = false): array

Point-biserial correlation coefficient (scipy.stats.pointbiserialr).

poisson(mixed $mu, mixed $loc = 0): Tessero\Stats\Distribution

A Poisson discrete random variable.

poissonMeansTest(mixed $k1, mixed $n1, mixed $k2, mixed $n2, mixed $diff = 0, mixed $alternative = 'two-sided'): mixed

Poisson means test, the E-test (scipy.stats.poisson_means_test).

powerDivergence(mixed $fObs, mixed $fExp = null, mixed $ddof = 0, mixed $axis = 0, mixed $lambda_ = null, mixed $nanPolicy = 'propagate', mixed $keepdims = false): mixed

Cressie-Read power divergence statistic and goodness of fit test (scipy.stats.power_divergence).

powerlaw(mixed $a, mixed $loc = 0, mixed $scale = 1): Tessero\Stats\Distribution

A power-function continuous random variable.

powerlognorm(mixed $c, mixed $s, mixed $loc = 0, mixed $scale = 1): Tessero\Stats\Distribution

A power log-normal continuous random variable.

powernorm(mixed $c, mixed $loc = 0, mixed $scale = 1): Tessero\Stats\Distribution

A power normal continuous random variable.

probplot(mixed $x): mixed

Probability plot data: order-statistic medians, ordered values, and the fit (scipy.stats.probplot).

quantile(mixed $x, mixed $p, mixed $method = 'linear', mixed $axis = 0, mixed $nanPolicy = 'propagate', mixed $keepdims = null, mixed $weights = null): mixed

Quantiles of the data along an axis (scipy.stats.quantile).

quantileTest(mixed $x, mixed $q = 0, mixed $p = 0.5, mixed $alternative = 'two-sided'): mixed

Test that the p-th population quantile is q (scipy.stats.quantile_test).

randint(mixed $low, mixed $high, mixed $loc = 0): Tessero\Stats\Distribution

A uniform discrete random variable.

rankdata(mixed $a, mixed $method = 'average', mixed $axis = null, mixed $nanPolicy = 'propagate'): mixed

Ranks of the data, ties handled by method (scipy.stats.rankdata).

ranksums(mixed $x, mixed $y, mixed $alternative = 'two-sided', mixed $axis = 0, mixed $nanPolicy = 'propagate', mixed $keepdims = false): mixed

Wilcoxon rank-sum statistic for two samples (scipy.stats.ranksums).

rayleigh(mixed $loc = 0, mixed $scale = 1): Tessero\Stats\Distribution

A Rayleigh continuous random variable.

rdist(mixed $c, mixed $loc = 0, mixed $scale = 1): Tessero\Stats\Distribution

An R-distributed (symmetric beta) continuous random variable.

recipinvgauss(mixed $mu, mixed $loc = 0, mixed $scale = 1): Tessero\Stats\Distribution

A reciprocal inverse Gaussian continuous random variable.

reciprocal(mixed $a, mixed $b, mixed $loc = 0, mixed $scale = 1): Tessero\Stats\Distribution

A loguniform or reciprocal continuous random variable.

relBreitwigner(mixed $rho, mixed $loc = 0, mixed $scale = 1): Tessero\Stats\Distribution

A relativistic Breit-Wigner random variable.

relfreq(mixed $a, mixed $numbins = 10, mixed $defaultreallimits = null, mixed $weights = null): mixed

Relative frequency histogram (scipy.stats.relfreq).

rice(mixed $b, mixed $loc = 0, mixed $scale = 1): Tessero\Stats\Distribution

A Rice continuous random variable.

scoreatpercentile(mixed $a, mixed $per, mixed $limit = null, mixed $interpolationMethod = 'fraction', mixed $axis = null): mixed

Score at the given percentile(s) of the data (scipy.stats.scoreatpercentile).

sem(mixed $a, array|int|null $axis = 0, int|float|bool|null $ddof = 1, string $nanPolicy = 'propagate', bool $keepdims = false): Tessero\NDArray|int|float

Standard error of the mean (scipy.stats.sem).

semicircular(mixed $loc = 0, mixed $scale = 1): Tessero\Stats\Distribution

A semicircular continuous random variable.

shapiro(mixed $x, mixed $axis = null, mixed $nanPolicy = 'propagate', mixed $keepdims = false): mixed

Shapiro-Wilk test for normality (scipy.stats.shapiro).

siegelslopes(mixed $y, mixed $x = null, mixed $method = 'hierarchical', mixed $axis = null, mixed $nanPolicy = 'propagate', mixed $keepdims = false): mixed

Siegel's repeated-medians regression line (scipy.stats.siegelslopes).

sigmaclip(mixed $a, mixed $low = 4.0, mixed $high = 4.0): mixed

Iterative sigma-clipping of the data (scipy.stats.sigmaclip).

skellam(mixed $mu1, mixed $mu2, mixed $loc = 0): Tessero\Stats\Distribution

A Skellam discrete random variable.

skew(mixed $a, array|int|null $axis = 0, int|float|bool|null $bias = true, string $nanPolicy = 'propagate', bool $keepdims = false): Tessero\NDArray|int|float

Sample skewness of a data set (scipy.stats.skew).

skewcauchy(mixed $a, mixed $loc = 0, mixed $scale = 1): Tessero\Stats\Distribution

A skewed Cauchy random variable.

skewnorm(mixed $a, mixed $loc = 0, mixed $scale = 1): Tessero\Stats\Distribution

A skew-normal random variable.

skewtest(mixed $a, mixed $axis = 0, mixed $nanPolicy = 'propagate', mixed $alternative = 'two-sided', mixed $keepdims = false): mixed

Test whether the skew is different from the normal distribution (scipy.stats.skewtest).

somersd(mixed $x, mixed $y = null, mixed $alternative = 'two-sided'): mixed

Somers' D of two rankings or of a contingency table (scipy.stats.somersd).

spearmanr(mixed $a, mixed $b = null, mixed $axis = 0, mixed $nanPolicy = 'propagate', mixed $alternative = 'two-sided'): mixed

Spearman rank-order correlation coefficient(s) and p-value(s) (scipy.stats.spearmanr).

spearmanrho(mixed $x, mixed $y, array|int|null $axis = 0, string $alternative = 'two-sided', string $nanPolicy = 'propagate', bool $keepdims = false): array

Spearman's rank correlation (scipy.stats.spearmanrho; method=None only).

studentizedRange(mixed $k, mixed $df, mixed $loc = 0, mixed $scale = 1): Tessero\Stats\Distribution

A studentized range continuous random variable.

t(mixed $df, mixed $loc = 0, mixed $scale = 1): Tessero\Stats\Distribution

A Student's t continuous random variable.

theilslopes(mixed $y, mixed $x = null, mixed $alpha = 0.95, mixed $method = 'separate', mixed $axis = null, mixed $nanPolicy = 'propagate', mixed $keepdims = false): mixed

Theil-Sen estimator of a regression line with a confidence interval for the slope (scipy.stats.theilslopes).

tiecorrect(mixed $rankvals): mixed

Tie correction factor for Mann-Whitney U and Kruskal-Wallis H (scipy.stats.tiecorrect).

tmax(mixed $a, mixed $upperlimit = null, mixed $axis = 0, mixed $inclusive = true, mixed $nanPolicy = 'propagate', mixed $keepdims = false): mixed

Trimmed maximum (scipy.stats.tmax).

tmean(mixed $a, mixed $limits = null, mixed $inclusive = null, mixed $axis = null, mixed $nanPolicy = 'propagate', mixed $keepdims = false): mixed

Trimmed mean; inclusive=None means (True, True) (scipy.stats.tmean).

tmin(mixed $a, mixed $lowerlimit = null, mixed $axis = 0, mixed $inclusive = true, mixed $nanPolicy = 'propagate', mixed $keepdims = false): mixed

Trimmed minimum (scipy.stats.tmin).

trapezoid(mixed $c, mixed $d, mixed $loc = 0, mixed $scale = 1): Tessero\Stats\Distribution

A trapezoidal continuous random variable.

triang(mixed $c, mixed $loc = 0, mixed $scale = 1): Tessero\Stats\Distribution

A triangular continuous random variable.

trim1(mixed $a, mixed $proportiontocut, mixed $tail = 'right', mixed $axis = 0): mixed

Slice off a proportion from one end; the kept values come sorted (scipy.stats.trim1).

trimMean(mixed $a, mixed $proportiontocut, array|int|null $axis = 0, string $nanPolicy = 'propagate', bool $keepdims = false): Tessero\NDArray|int|float

Mean after trimming a proportion of the smallest and largest values (scipy.stats.trim_mean).

trimboth(mixed $a, mixed $proportiontocut, mixed $axis = 0): mixed

Slice off a proportion of items from both ends; the kept values come sorted (scipy.stats.trimboth).

truncexpon(mixed $b, mixed $loc = 0, mixed $scale = 1): Tessero\Stats\Distribution

A truncated exponential continuous random variable.

truncnorm(mixed $a, mixed $b, mixed $loc = 0, mixed $scale = 1): Tessero\Stats\Distribution

A truncated normal continuous random variable.

truncpareto(mixed $b, mixed $c, mixed $loc = 0, mixed $scale = 1): Tessero\Stats\Distribution

An upper truncated Pareto continuous random variable.

truncweibullMin(mixed $c, mixed $a, mixed $b, mixed $loc = 0, mixed $scale = 1): Tessero\Stats\Distribution

A doubly truncated Weibull minimum continuous random variable.

tsem(mixed $a, mixed $limits = null, mixed $inclusive = null, mixed $axis = 0, mixed $ddof = 1, mixed $nanPolicy = 'propagate', mixed $keepdims = false): mixed

Trimmed standard error of the mean; inclusive=None means (True, True) (scipy.stats.tsem).

tstd(mixed $a, mixed $limits = null, mixed $inclusive = null, mixed $axis = 0, mixed $ddof = 1, mixed $nanPolicy = 'propagate', mixed $keepdims = false): mixed

Trimmed standard deviation; inclusive=None means (True, True) (scipy.stats.tstd).

ttest1samp(mixed $a, mixed $popmean, mixed $axis = 0, mixed $nanPolicy = 'propagate', mixed $alternative = 'two-sided', mixed $keepdims = false): mixed

T-test for the mean of one group of scores (scipy.stats.ttest_1samp).

ttestInd(mixed $a, mixed $b, mixed $axis = 0, mixed $equalVar = true, mixed $nanPolicy = 'propagate', mixed $alternative = 'two-sided', mixed $trim = 0, mixed $method = null, mixed $keepdims = false): mixed

T-test for the means of two independent samples; Welch's test with equal_var=False, Yuen's with trim (scipy.stats.ttest_ind).

ttestIndFromStats(mixed $mean1, mixed $std1, mixed $nobs1, mixed $mean2, mixed $std2, mixed $nobs2, mixed $equalVar = true, mixed $alternative = 'two-sided'): mixed

T-test for the means of two independent samples from descriptive statistics (scipy.stats.ttest_ind_from_stats).

ttestRel(mixed $a, mixed $b, mixed $axis = 0, mixed $nanPolicy = 'propagate', mixed $alternative = 'two-sided', mixed $keepdims = false): mixed

T-test on two related samples (scipy.stats.ttest_rel).

tukeyHsd(mixed $sample1, mixed $sample2 = null, mixed $sample3 = null, mixed $sample4 = null, mixed $sample5 = null, mixed $sample6 = null, mixed $sample7 = null, mixed $sample8 = null, mixed $sample9 = null, mixed $sample10 = null, mixed $sample11 = null, mixed $sample12 = null, mixed $equalVar = true): mixed

Tukey HSD pairwise comparison of means (scipy.stats.tukey_hsd).

tukeylambda(mixed $lam, mixed $loc = 0, mixed $scale = 1): Tessero\Stats\Distribution

A Tukey-Lamdba continuous random variable.

tvar(mixed $a, mixed $limits = null, mixed $inclusive = null, mixed $axis = 0, mixed $ddof = 1, mixed $nanPolicy = 'propagate', mixed $keepdims = false): mixed

Trimmed variance; inclusive=None means (True, True) (scipy.stats.tvar).

uniform(mixed $loc = 0, mixed $scale = 1): Tessero\Stats\Distribution

A uniform continuous random variable.

variation(mixed $a, array|int|null $axis = 0, string $nanPolicy = 'propagate', int|float|bool|null $ddof = 0, bool $keepdims = false): Tessero\NDArray|int|float

Coefficient of variation, std / mean (scipy.stats.variation).

vonmises(mixed $kappa, mixed $loc = 0, mixed $scale = 1): Tessero\Stats\Distribution

A Von Mises continuous random variable.

vonmisesFisher(mixed $mu, mixed $kappa, mixed $x): mixed

Von Mises-Fisher pdf at each row of x (scipy.stats.vonmises_fisher).

vonmisesLine(mixed $kappa, mixed $loc = 0, mixed $scale = 1): Tessero\Stats\Distribution

A Von Mises continuous random variable.

wald(mixed $loc = 0, mixed $scale = 1): Tessero\Stats\Distribution

A Wald continuous random variable.

wassersteinDistance(mixed $uValues, mixed $vValues, mixed $uWeights = null, mixed $vWeights = null): mixed

First Wasserstein distance between two 1-D distributions (scipy.stats.wasserstein_distance).

wassersteinDistanceNd(mixed $uValues, mixed $vValues, mixed $uWeights = null, mixed $vWeights = null): mixed

Wasserstein-1 distance between two N-D discrete distributions (scipy.stats.wasserstein_distance_nd).

weibullMax(mixed $c, mixed $loc = 0, mixed $scale = 1): Tessero\Stats\Distribution

Weibull maximum continuous random variable.

weibullMin(mixed $c, mixed $loc = 0, mixed $scale = 1): Tessero\Stats\Distribution

Weibull minimum continuous random variable.

weightedtau(mixed $x, mixed $y, mixed $rank = true, mixed $weigher = null, mixed $additive = true, mixed $axis = null, mixed $nanPolicy = 'propagate', mixed $keepdims = false): mixed

Weighted Kendall's tau with the hyperbolic weigher (scipy.stats.weightedtau; weigher=None only).

wilcoxon(mixed $x, mixed $y = null, mixed $zeroMethod = 'wilcox', mixed $correction = false, mixed $alternative = 'two-sided', mixed $method = 'auto', mixed $axis = 0, mixed $nanPolicy = 'propagate', mixed $keepdims = false): mixed

Wilcoxon signed-rank test; zstatistic with method='asymptotic' / 'approx' (scipy.stats.wilcoxon).

wishart(mixed $df, mixed $scale, mixed $x): mixed

Wishart pdf and log-pdf at an SPD matrix x (scipy.stats.wishart).

wrapcauchy(mixed $c, mixed $loc = 0, mixed $scale = 1): Tessero\Stats\Distribution

A wrapped Cauchy continuous random variable.

yeojohnson(mixed $x, mixed $lmbda = null): mixed

The Yeo-Johnson power transformation with a given lambda (scipy.stats.yeojohnson; lmbda required).

yeojohnsonLlf(mixed $lmb, mixed $data, mixed $axis = 0, mixed $nanPolicy = 'propagate', mixed $keepdims = false): mixed

Yeo-Johnson log-likelihood (scipy.stats.yeojohnson_llf).

yeojohnsonNormplot(mixed $x, mixed $la, mixed $lb, mixed $N = 80): mixed

Yeo-Johnson normality plot: lambdas and probability-plot correlations (scipy.stats.yeojohnson_normplot).

yulesimon(mixed $alpha, mixed $loc = 0): Tessero\Stats\Distribution

A Yule-Simon discrete random variable.

zipf(mixed $a, mixed $loc = 0): Tessero\Stats\Distribution

A Zipf (Zeta) discrete random variable.

zipfian(mixed $a, mixed $n, mixed $loc = 0): Tessero\Stats\Distribution

A Zipfian discrete random variable.

zmap(mixed $scores, mixed $compare, ?int $axis = 0, int|float|bool|null $ddof = 0, string $nanPolicy = 'propagate'): Tessero\NDArray|int|float

z-scores of scores relative to the mean and standard deviation of compare (scipy.stats.zmap).

zscore(mixed $a, ?int $axis = 0, int|float|bool|null $ddof = 0, string $nanPolicy = 'propagate'): Tessero\NDArray|int|float

z-scores of each value relative to the sample mean and standard deviation (scipy.stats.zscore).

poissonBinom(mixed $p, mixed $loc = 0): Tessero\Stats\PoissonBinom

The Poisson binomial distribution: the number of successes in independent Bernoulli trials whose success probabilities are the vector $p.

poissonBinomPmf(mixed $k, mixed $p, mixed $loc = 0): Tessero\NDArray|float

Probability mass of the Poisson binomial distribution with probabilities $p, evaluated at $k (a functional shortcut for poissonBinom($p)->pmf($k)).