A | |
| add [Sample.Summary] |
Adds a value to the data set.
|
| adjust [Tests.Multiple] |
Adjusts obtained P-values for multiple comparisons using a given
adjustment method.
|
B | |
| bca [Resampling.Bootstrap] |
Bias-corrected and accelerated (BCa) bootstrap.
|
| bernoulli [Distributions] | |
| beta [Distributions] | |
| binomial [Distributions] | |
C | |
| cauchy [Distributions] | |
| chi_squared [Distributions] | |
| compare [Distributions.Categorical.OrderedType] | |
| continuous_by [Sample.Quantile] |
O(n log n) Estimates sample quantile corresponding to the given
probability
p, using the continuous sample method with given
parameters.
|
| create [Distributions.Categorical.S] |
Creates a categorical distribution over values of type
elt,
where each value is given a probability, which defaults to 0
for values not in the list.
|
| create [Distributions.NegativeBinomial] |
Creates negative Binomial distribution with a given number of
failures and success probability.
|
| create [Distributions.Hypergeometric] |
Creates Hypergeometric distribution.
|
| create [Distributions.Geometric] |
Creates Geometric distribution with a given probability of success.
|
| create [Distributions.Binomial] |
Creates binomial distribution.
|
| create [Distributions.Bernoulli] |
Creates Bernoulli distribution with given success probability
p.
|
| create [Distributions.Poisson] |
Creates a Poisson distribution.
|
| create [Distributions.Logistic] |
Creates logistic distribution.
|
| create [Distributions.Beta] |
Creates beta distribution.
|
| create [Distributions.Cauchy] |
Creates Cauchy-Lorentz distribution from parameters.
|
| create [Distributions.Gamma] |
Creates gamma distribution.
|
| create [Distributions.T] |
Creates Student's t-distribution with a given number of degrees
of freedom.
|
| create [Distributions.F] |
Creates Fisher-Snedecor distribution with a given number of degrees
of freedom.
|
| create [Distributions.ChiSquared] |
Creates chi-squared distribution.
|
| create [Distributions.Exponential] |
Creates exponential distribution.
|
| create [Distributions.Uniform] |
Creates uniform distribution over a given interval.
|
| create [Distributions.LogNormal] |
Creates log-normal distribution from parameters.
|
| create [Distributions.Normal] |
Creates normal distribution from parameters.
|
| cumulative [Base] |
O(n) Calculates a cumulative statistic over a given array.
|
| cumulative_probability [Distributions.ContinuousDistribution] |
Computes cumulative probability function for a given value
n,
i.
|
| cumulative_probability [Distributions.DiscreteDistribution] |
Computes cumulative probability function for a given value
n,
i.
|
D | |
| density [Distributions.ContinuousDistribution] |
Computes probability density function for a given value
n, i.
|
E | |
| empty [Sample.Summary] |
Empty data set.
|
| estimate_pdf [Sample.KDE] |
O(n * points) Simple kernel density estimator.
|
| exponential [Distributions] | |
F | |
| f [Distributions] | |
G | |
| gamma [Distributions] | |
| geometric [Distributions] | |
| goodness_of_fit [Tests.KolmogorovSmirnov] |
One-sample Kolmogorov-Smirnov test for goodness of fit, which
evaluates the distribution
G(x) of the observed random variable
against a given distribution F(x).
|
| goodness_of_fit [Tests.ChiSquared] | |
H | |
| histogram [Sample] |
O(n) Computes histogram of a data set.
|
| hypergeometric [Distributions] | |
I | |
| independence [Tests.ChiSquared] | |
| iqr [Sample.Quantile] |
O(n log n) Estimates interquantile range of a given sample,
using the continuous sample method with given parameters.
|
| iqr [Sample] |
O(n log n) Estimates interquantile range of a given sample,
using the continuous sample method with given parameters.
|
J | |
| jackknife [Resampling] |
Repeatidly computes a statistical estimate over the data set, leaving
out a single observation at a time.
|
K | |
| kurtosis [Sample.Summary] |
Returns the excess kurtosis of the values that have been added
or
nan if the data set is empty.
|
| kurtosis [Sample] |
O(n) Computes the excess kurtosis of a sample, which is a
measure of a "peakedness" of its distribution.
|
| kurtosis [Distributions.Features.S] | |
| kurtosis_opt [Distributions.Features.Opt] | |
L | |
| log_normal [Distributions] | |
| logistic [Distributions] | |
M | |
| mappend [Algebra.Monoid.S] |
Associative binary operation.
|
| max [Sample.Summary] |
Returns the maximum added value or
nan if the data set is empty.
|
| max [Sample] | |
| mean [Sample.Summary] |
Returns the arithmetic mean of the values that have been added
or
nan if the data set is empty.
|
| mean [Sample] |
O(n) Computes sample's arithmetic mean.
|
| mean [Distributions.Features.S] | |
| mean_opt [Distributions.Features.Opt] | |
| mempty [Algebra.Monoid.S] |
Identity, subject to
mappend mempty x = mappend x mempty = x.
|
| min [Sample.Summary] |
Returns the minimum added value or
nan is the data set is empty.
|
| min [Sample] | |
| minmax [Sample] | |
| mle [Distributions.Categorical.S] |
Creates a categorical distribution with a MLE of parameters,
estimated from given data.
|
| mle [Distributions.Bernoulli] |
Creates a Bernoulli distribution with a MLE of parameters, estimated
from given data.
|
| mle [Distributions.Poisson] |
Creates a Poisson distribution with a MLE of parameters, estimated
from given data.
|
| mle [Distributions.Exponential] |
Creates exponential distribution with a MLE of parameters, estimated
from given data.
|
| mle [Distributions.Uniform] |
Creates uniform distribution with a MLE of parameters, estimated
from given data.
|
| mle [Distributions.LogNormal] |
Creates log-normal distribution with a MLE of parameters, estimated
from given data.
|
| mle [Distributions.Normal] |
Creates normal distribution with a MLE of parameters, estimated
from given data.
|
| mme [Distributions.NegativeBinomial] |
Creates negative Binomial distribution with parameters, estimated
with method of moments.
|
| mme [Distributions.Geometric] |
Creates Geometric distribution with parameters, estimated with
method of moments.
|
| mme [Distributions.Binomial] |
Creates binomial distribution with parameters, estimated with
method of moments.
|
| mme [Distributions.Logistic] |
Creates logistic distribution with parameters, estimated with method
of moments.
|
| mme [Distributions.Beta] |
Creates beta distribution with parameters, estimated with method
of moments.
|
| mme [Distributions.Gamma] |
Creates gamma distribution with parameters, estimated with method
of moments.
|
| mme [Distributions.T] |
Creates Student's t-distribution with parameters, estimated with
method of moments.
|
| mme [Distributions.F] |
Creates Fisher-Snedecor distribution with parameters, estimated
with method of moments.
|
| mme [Distributions.ChiSquared] |
Creates chi-squared distribution with parameters, estimated with
method of moments.
|
| moments [Sample] |
O(n k) Computes an array of sample moments of order 1 to k, i.
|
N | |
| negative_binomial [Distributions] | |
| normal [Distributions] | |
O | |
| one_sample [Tests.Sign] |
Sign test, which evaluates the null hypothesis that sample median is
equal to the specified
shift.
|
| one_sample [Tests.WilcoxonT] |
Wilcoxon signed-rank test, which evaluates the null hypothesis
that sample median is equal to the specified
shift.
|
| one_sample [Tests.T] |
One sample Student's t-test, which evaluates the null hypothesis
that a
mean of a normally distributed variable is equal to the
specified value.
|
P | |
| pearson [Sample.Correlation.Auto] |
O(n^2) Computes autocorrelation, using Person product-moment
correlation coefficient.
|
| pearson [Sample.Correlation] |
O(n) Computes Pearson product-moment correlation coefficient
for two given samples.
|
| poisson [Distributions] | |
| probability [Distributions.DiscreteDistribution] |
Computes probability mass function for a given value
n, i.
|
Q | |
| quantile [Sample] |
O(n log n) Estimates sample quantile corresponding to the given
probability
p, using the continuous sample method with default
parameters.
|
| quantile [Distributions.ContinuousDistribution] |
Computes inverse cumulative probability function for a given
probability
p.
|
R | |
| range [Sample] |
O(n) Computes sample's range, i.
|
| range [Base] |
Creates an array of integers given a semiopen range
[a, b).
|
| rank [Sample] |
O(n log n) Computes sample's ranks,
ties_strategy controls
which ranks are assigned to equal values:
|
| reorder [Base] |
O(n) Reorders values in
src into dst, according to a given
permutation of indices.
|
| resample [Resampling] |
Repeatidly resamples a given data set with replacement, computing a
statistical estimate over the resampled data.
|
| run_test [Tests] |
Assess significance of the statistical test at a given
significance_level, which defaults to 0.05.
|
S | |
| sample [Distributions.ContinuousDistribution] |
Samples
size data points from the distribution.
|
| sample [Distributions.DiscreteDistribution] |
Samples
size data points from the distribution.
|
| sample [Base] |
O(n) Takes a sample of the specified
size from the given
array either with or without replacement.
|
| sd [Sample.Summary] |
Returns the standard deviation of the values that have been added
or
nan if the data set is empty.
|
| sd [Sample] |
O(n) Computes sample's standard deviation.
|
| search_sorted [Base] |
O(log n) Searches for the index of a given element
v in array
vs, sorted with a given comparison function cmp.
|
| shuffle [Base] |
O(n) Shuffles a given array using Fisher-Yates shuffle.
|
| size [Sample.Summary] |
Returns the number of available values.
|
| skewness [Sample.Summary] |
Returns the skewness of the values that have been added or
nan if
the data set is empty.
|
| skewness [Sample] |
O(n) Computes the skewness of a sample, which is a measure of
asymmetry of its distribution.
|
| skewness [Distributions.Features.S] | |
| skewness_opt [Distributions.Features.Opt] | |
| spearman [Sample.Correlation] |
O(n log n) Computes Spearman rank correlation coefficient for
two given samples, which is essentially Pearson correlation
calculated for sample ranks.
|
| standard [Distributions.Cauchy] |
Cauchy-Lorentz distribution with 0
location and scale equal to 1.
|
| standard [Distributions.Normal] |
Standard normal distribution with 0
mean and sd equal to 1.
|
T | |
| t [Distributions] | |
| two_sample [Tests.KolmogorovSmirnov] |
Two-sample Kolmogorov-Smirnov test, which evaluates the null
hypothesis, that two independent samples are drawn from the
same continious distribution.
|
| two_sample_independent [Tests.MannWhitneyU] |
Mann-Whitney U test (also known as Mann-Whitney-Wilcoxon test and
Wilcoxon rank sum test) is a non-paramteric test, which evaluates
the null hypothesis that two independent samples have equal
medians.
|
| two_sample_independent [Tests.T] |
Two sample t-test, which evaluates the null hypothesis that the
difference of means of two independent normally distributed
populations is equal to the specified value.
|
| two_sample_paired [Tests.Sign] |
Dependent samples sign test, which evaluates the null hypothesis
that the median difference between observations from two related
samples is zero.
|
| two_sample_paired [Tests.WilcoxonT] |
Wilcoxon paired signed-rank test, which evaluates the null hypothesis
that two related samples have equal medians.
|
| two_sample_paired [Tests.T] |
Paired two sample t-test, which evaluates the null hypothes that
the difference of means of the two paired normally distributed
populations is equal to the specified value.
|
U | |
| uniform [Distributions] | |
V | |
| variance [Sample.Summary] |
Returns the variance of the available values or
nan if the
data set is empty.
|
| variance [Sample] |
O(n) Computes unbiased estimate of a sample's variance, also
known as the sample variance, where the denominator is
n - 1.
|
| variance [Distributions.Features.S] | |
| variance_opt [Distributions.Features.Opt] |