Java Statistics Library was developed as a general Java library for common statistical functions. All operations are floating-point safe utilizing the BigDecimal framework. You can make use of this handy library to improve your development.

Java Statistics Library provides advanced statistical functions that are not part of the standard Java programming language, specifically, to enhance the robustness and speed of numerical computations. Included are calculations on the Z- and Y-score of distributions, and the computation of percentiles of distributions. Features of the Java Statistics Library: * A library of robust statistical tools for the Java programming language * The floating-point comonad and BigDecimal framework * The Empirical distribution and the continuous nonparametric distributions * The t distribution and its special cases, including the t distribution and Student-t distribution * The Chi-squared distribution and its special cases, including the hypergeometric, beta, and gamma distributions * The scaled and unscaled Pareto distributions * The quantile function, including the quantile function for scaled and unscaled distributions * The kappa function for discrete distributions * The cumulative distribution function * The inverse cumulative distribution function * The probability density function * The cumulative probability distribution function * The cumulative distribution function * The incomplete gamma function and the lower incomplete gamma function * The logarithm function, including the logarithmic moments * The Meijer G-function * The CDF of the exponential distribution * The Z- and Y-score of distributions, including the number of measurements, rank, percentile, percentiles, and the normal z-score * New generalized Pareto distribution * The mean absolute deviation * The modified linear correlation coefficient * The modified quadratic correlation coefficient * The robust Z-score and Y-score with default parameters * The Z- and Y-standardization * The Z- and Y-standard score * The estimation of the expected value and mean value * The estimation of the standard deviation * The Weibull distribution and its special cases, including the exponential Weibull and Weibull distribution * The exponential distribution and its special cases * The Gamma distribution and its special cases * The binomial distribution and its special cases * The log-normal distribution and its special cases * The Student-t distribution and its special cases * The Cauchy distribution and its special cases * The chi-squared distribution and its special cases * The Poisson distribution and its special cases * The beta distribution and its special cases * The arcsine distribution and its special cases * The hypergeometric distribution and its special

General Java Library for common statistical functions. * * Basic functionalities * * BigDecimal support * * Count, Mean, Min, Max, Product, Median, Power, Range, Quantile, StandardDeviation, StandardizedDeviation, Mean and Variance * * Independent and paired sample T-test, Wilcoxon rank-sum test, Non-Parametric Rank Sum and Wilcoxon Sign Rank test * * Fisher’s Exact test, Chi Square and Bonferroni correction * * R, Python, and SPSS like data import and export * * Addition of new methods on existing classes * * Other statistical functions for categorical data, like the Binomial and Poisson distributions * * Aggregation of Data into new Data structures * * Basic distribution functions, like Student’s T-Distribution, Beta, and Logistic * * Basic sum/integration functions, like Monte Carlo and Monte Carlo Integration * * Optimization functions, like: Linear and Quadratic Programming, Convex Optimization, Binary and Quadratic Programming * * XSLT, Query Language for the XSLT * * Webservice via the RESTful, JAX-RS * * Java Graphics2D like methods via the BufferedImage model. * * **More!** * Optional functionalities * * Optimization Solver * * Interpolation and Smoothness * * Univariate and Multivariate T-Distributions with different parameters * * Gamma and Weibull distributions, exponential, power, double-exponential, and inverse double-exponential * * Fast real numbers, BigDecimal, Period, Duration and LocalDate from Joda-Time * * Customary string manipulation like Upper and Lower Case * * Multiline String handling via StringBuffer * * Customary string formatting, like: The International System of Units * * Customary dates and times parsing and formatting, like: The ISO standard formats, The ICU extension, the Joda-Time extension * * More generally: High precision data handling and formatting. Very BigDecimal and the BigDecimalâ€™s API. New methods are added. Also methods are already added as mentioned in the above (Optional). All methods have been tested and almost all of them tested with BigDecimal. The majority of the methods already defined in Java (Swing) were used. Those methods are often also used in the fields. But are also used when the user sends it data with one of those types. We 2f7fe94e24

Some stats algorithms are not so easy to implement. For this reason, Java Statistics Library offers some ready-to-use implementations of statistical functions. For instance, a simple average is defined as follows: public float average(int[] array) { BigDecimal sum = new BigDecimal(0); for (int i=0;i

It is an independent library that provides various statistical tools to developers, It is a general library for common statistical functions that can be applied in any domain, such as Network Programming and Data Mining. Java API Documentation for gaussmetrics This is java implementation of Gaussian Metrics Library (see the Gaussian Metrics website for the class description). Gaussian Metrics contains metrics for 4- and 8-neighbor versions of many existing clustering and classification algorithms. It is a Java version of the Gaussian Metrics library that was written in Python. It is a set of 40 classes, and there are several hundred of functions. Some of them are very useful for data science and Machine Learning applications. Akaike Info Criterion Akaike Information Criterion (AIC) is the most popular model selection criterion. AIC uses a small model selection criterion as an approximation for the asymptotic BIC (Bayes Information Criterion). The approximated log-likelihood of the AIC depends on an additional parameter alpha. Its value is chosen so that the AIC is asymptotically equivalent to the true likelihood when the sample size tends to infinity. In addition, the AIC is useful for comparing several models with different number of parameters. Argyle – Pruning The Argyle algorithm is a pruning algorithm used for decision trees. In this implementation of Argyle, the algorithm can prune leaf nodes (nodes with only one child) instead of the nodes with only one child. Also, it can discard a large number of variables and save time when the dataset is large. Basic Block Class for displaying a quick block graph of a data set (blocks are a group of connected data). BasicMetric Basic Metric is a class for the basic metric of Gaussian Metrics. This basic class implements a customizable version of the basic distance used to compute the classic Gaussian kernel distance. BasicNetwork This is a class for a network of nodes and edges, with properties for both nodes and edges. BioPro BioPro is a Java implementation of the BioPro algorithm for RNA bioinformatics problem which is used for finding Hidden Markov Models (HMM) in RNA sequences. The input sequences can contain some heterogeneity. BioPro is an efficient algorithm for finding HMM models in sequences. BioPro can be used for finding alignments, searching and counting BioPro allows to find your Hidden Mark

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