{"name":"ghc-statistics","version":"0.15.2.0","synopsis":{"source":"Haskell library of statistical types, data, and functions","html":"

Haskell library of statistical types, data, and functions

","plain":"Haskell library of statistical types, data, and functions\n\n","locale":"en_US.UTF-8"},"description":{"source":"This library provides a number of common functions\nand types useful in statistics. We focus on high performance, numerical\nrobustness, and use of good algorithms. Where possible, we provide references\nto the statistical literature.\n\nThe library's facilities can be divided into four broad categories:\n\n@itemize\n@item Working with widely used discrete and continuous probability\ndistributions. (There are dozens of exotic distributions in use; we focus\non the most common.)\n\n@item Computing with sample data: quantile estimation, kernel density\nestimation, histograms, bootstrap methods, significance testing,\nand regression and autocorrelation analysis.\n\n@item Random variate generation under several different distributions.\n\n@item Common statistical tests for significant differences between samples.\n@end itemize","html":"

This library provides a number of common functions and types useful in statistics. We focus on high performance, numerical robustness, and use of good algorithms. Where possible, we provide references to the statistical literature.

The library's facilities can be divided into four broad categories:

• Working with widely used discrete and continuous probability distributions. (There are dozens of exotic distributions in use; we focus on the most common.)

• Computing with sample data: quantile estimation, kernel density estimation, histograms, bootstrap methods, significance testing, and regression and autocorrelation analysis.

• Random variate generation under several different distributions.

• Common statistical tests for significant differences between samples.

","plain":"This library provides a number of common functions and types useful in\nstatistics. We focus on high performance, numerical robustness, and use of good\nalgorithms. Where possible, we provide references to the statistical\nliterature.\n\nThe library's facilities can be divided into four broad categories:\n\n * Working with widely used discrete and continuous probability distributions.\n (There are dozens of exotic distributions in use; we focus on the most\n common.)\n\n * Computing with sample data: quantile estimation, kernel density estimation,\n histograms, bootstrap methods, significance testing, and regression and\n autocorrelation analysis.\n\n * Random variate generation under several different distributions.\n\n * Common statistical tests for significant differences between samples.\n\n","locale":"en_US.UTF-8"},"home-page":"https://github.com/bos/mwc-random","derivations":[{"system":"x86_64-linux","target":"","derivation":"/gnu/store/zfa8mb3cbrzj8zkljs9iq7zrpr7yg443-ghc-statistics-0.15.2.0.drv"},{"system":"riscv64-linux","target":"","derivation":"/gnu/store/nd034ycal1dfa2h3qyg46afgigpkizh0-ghc-statistics-0.15.2.0.drv"},{"system":"powerpc-linux","target":"","derivation":"/gnu/store/qrps0diqfqlp32sl3hx608d3chff5l9h-ghc-statistics-0.15.2.0.drv"},{"system":"powerpc64le-linux","target":"","derivation":"/gnu/store/dmlqvxxfy85i7ki6vpc01k4xvsc1f7sc-ghc-statistics-0.15.2.0.drv"},{"system":"mips64el-linux","target":"","derivation":"/gnu/store/fkd11xxah2d0pqm2yy7k6rc53xzqlpfp-ghc-statistics-0.15.2.0.drv"},{"system":"i686-linux","target":"","derivation":"/gnu/store/fbid37bshn56c01ax63llx3ymfyalb67-ghc-statistics-0.15.2.0.drv"},{"system":"i586-gnu","target":"","derivation":"/gnu/store/s47p762h2q58vd20plfxl9d7l8lxbazf-ghc-statistics-0.15.2.0.drv"},{"system":"armhf-linux","target":"","derivation":"/gnu/store/91zn0zypa929rn2xyz5vmpq215c1v96g-ghc-statistics-0.15.2.0.drv"},{"system":"aarch64-linux","target":"","derivation":"/gnu/store/zqwpazhjki8qf211g95y7w8sf8ixhsvi-ghc-statistics-0.15.2.0.drv"}]}