PI Science API is scientific library for .NET (Standard).
Class Description Examples
Math 
[namespace pi.science.math]
Matrices
(PIMatrix, 1.0.0)
Addition, subtraction, multiplication, operations between matrix and constant, transposion, opposition, inversion, determinant, rank. -> examples
Cramer`s rule
(PICramerRule, 1.0.0)
Solution for linear equitions (Ax = B). -> examples
Gamma function
(PIGamma, 1.1.0)
Gamma function:
gamma
-> examples
Gamma incomplete function
(PIGamma, 1.1.4)
Gamma incomplete function:
gamma_lower gamma_upper
-> examples
Beta function
(PIBeta, 1.1.0)
Beta function:
beta
-> examples
Numerical integration:
(PINumericalIntegrationTrapezoidal, 1.2.0)
Trapezoidal method:
trapezoidal
-> examples
Numerical integration:
(PINumericalIntegrationRectangle, 1.2.0)
Rectangle method:
rectangle
-> examples
Numerical integration:
(PINumericalIntegrationSimpson, 1.2.0)
Simpson`s method:
simpson
-> examples
Discrete math 
[namespace pi.science.discretemath]
Prime
(PIPrime, 1.1.8)
Working with primes. -> examples
Prime factorization
(PIPrimeFactorizationSimple, 1.1.8)
Simple method for prime factorization. -> examples
Prime factorization - Fermat
(PIPrimeFactorizationFermat, 1.1.8)
Fermat method for prime factorization. -> examples
Probability 
[namespace pi.science.probability]
Probability utils
(PIProbabilityUtils, 1.1.8)
Factorial, Combination, Catalan number. -> examples
Regression 
[namespace pi.science.regression]
Linear regression
(PILinearRegression, 1.0.1)
Linear regression:
linear_regression
-> examples
Polynomial regression
(PIPolynomialRegression, 1.0.2)
Polynomial regression (..by degree):
polynomial_regression
-> examples
Exponential regression
(PIExponentialRegression, 1.0.3)
Exponential regression:
exponential_regression
-> examples
Exponential modified regression
(PIExponentialModifiedRegression, 1.0.3)
Exponential modified regression:
exponential_modified_regression
-> examples
Power regression
(PIPowerRegression, 1.0.3)
Power regression:
power_regression
-> examples
Gompertz regression
(PIGompertzRegression, 1.0.4)
Gompertz regression, supported method PARTIAL_SUMS, PARTIAL_AVERAGES, SELECTED_POINTS:
gompertz_regression
-> examples
Logistic regression
(PILogisticRegression, 1.0.5)
Logistic regression:
logistic_regression
-> examples
Smoothing 
[namespace pi.science.smoothing]
Moving average
(PIMovingAverageSmoothing, 1.0.6)
Moving average. -> examples
Median smoothing
(PIMedianSmoothing, 1.0.6)
Median smoothing. -> examples
Simple exponencial smoothing
(PISimpleExponentialSmoothing, 1.0.6)
Simple exponential smoothing (Brown`s exponential smoothing); several methods for generating first value:
simple_exponential_smoothing
-> examples
Double exponencial smoothing
(PIDoubleExponentialSmoothing, 1.0.7)
Double exponential smoothing, several methods for generating first value:
double_exponential_smoothing
-> examples
Statistics 
[namespace pi.science.statistic]
Descriptive statistics
(PIVariable, 1.0.0)
Population/sample mean, geometric mean, min, max, sum, range, quartiles, mode, median, interquartile range, population/sample variance, population/sample standard deviation, ZScore, skewness, kurtosis. -> examples
Statistics classes
(PIStatisticsClasses, 1.0.0)
Classes for histogram, generating interval (bins) (Sturge`s rule, Scott`s rule, Square-root rule, Rice`s rule, Doane`s rule, Freedman-Diaconis rule, auto rule), frequencies, relative frequencies, cululative relative frequencies. -> examples
Distribution 
[namespace pi.science.distribution]
Normal distribution
(PINormalDistribution, 1.0.8)
Get probability for X, get X for probability, calc probability density: normal_distribution -> examples
CHI-Square distribution
(PICHISquareDistribution, 1.1.0)
Get probability for X, get X for probability, calc probability density: chi_square -> examples
Student distribution
(PIStudentDistribution, 1.1.0)
Get probability for X, get X for probability, calc probability density: student -> examples
F distribution
(PIFDistribution, 1.1.0)
Get probability for X, get X for probability, calc probability density: f -> examples
Log normal distribution
(PILogNormalDistribution, 1.1.2)
Get probability for X, get X for probability, calc probability density: lognormal -> examples
Exponential distribution
(PIExponentialDistribution, 1.1.4)
Get probability for X (CDF), get X for probability (InverseCDF), calc probability density (PDF):
exponential
-> examples
Poisson distribution
(PIPoissonDistribution, 1.1.4)
Get probability for X (CDF), get X for probability (InverseCDF), calc probability density (PDF):
poisson
-> examples
Erlang distribution
(PIErlangDistribution, 1.1.4)
Get probability for X (CDF), get X for probability (InverseCDF), calc probability density (PDF):
erlang
-> examples
Weibull distribution
(PIWeibullDistribution, 1.1.8)
Get probability for X (CDF), get X for probability (InverseCDF), calc probability density (PDF):
weibull
-> examples
Rayleigh distribution
(PIRayleighDistribution, 1.1.8)
Get probability for X (CDF), get X for probability (InverseCDF), calc probability density (PDF):
rayleigh
-> examples
Pareto distribution
(PIParentoDistribution, 1.2.0)
Get probability for X (CDF), get X for probability (InverseCDF), calc probability density (PDF):
pareto
-> examples
Hypothesis testing 
[namespace pi.science.hypothesistesting]
Shapiro-Wilk (original)
(PIShapiroWilk, 1.2.2)
Shapiro-Wilk (original) test of normality:
shapiro_wilk_original
-> examples
Shapiro-Wilk (expanded)
(PIShapiroWilkExpanded, 1.2.4)
Shapiro-Wilk (expanded) test of normality:
shapiro_wilk_expanded
-> examples
Skewness test
(PISkewnessTest, 1.2.4)
Skewness test of normality:
skewness_test
-> examples

Scheduled areas for next development:

Integrals - numerical methods, correlations, interpolations, hypothesis testing, fractions support, neural networks, graph algorithms, cluster analysis...