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PI Science API is scientific library for JAVA. You need to add only one .jar to your application (how ? -> see here).
Class Description Examples
Math 
[pi.math.api]
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
Regression 
[pi.regression.api]
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 
[pi.smoothing.api]
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 
[pi.statistics.api]
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 
[pi.distribution.api]
Normal distribution
(PINormalDistribution, 1.0.8)
Get probability for Z, get Z for probability, calc probability density. -> examples

Scheduled areas for next development:

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