package.json

{
  "name"          : "yagnus",
  "description"   : "JavaScript stats library containing parallel distributed streaming algorithms to compute important frequently used statistics on big data. The library calculates commonly used univariate, multivariate and discrete statistics. It can be used alone in a webpage, or server-side in nodejs (or both since mss's can be merged), or within a big-data no-sql engines such as hadoop, mongodb.",
  "homepage"      : "http://www.yagn.us",
  "keywords"      : ["stats", "statistics", "analytics", "univariate", "multivariate", "discrete", "mean", "variance", "standard deviation", "unbiased", "constant time", "constant space", "fixed space", "efficient", "fast", "awesome", "cool", "skewness", "kurtosis", "contingency table", "multidimensional", "counter", "Pearson", "correlation", "covariance", "coskew", "cokurtosis", "covariance matrix", "aggregation", "map","reduce", "node", "mongodb", "javascript", "server", "client", "browser", "big data", "big-data", "parallel", "distributed", "streaming", "massive", "humoungous", "functional", "median", "min", "minimum", "max", "maximum", "quantile", "quartile", "order statistics", "statistic", "order stats", "minimum sufficient statistics", "mss", "average", "stable", "sampling", "sampler", "reservoir sampling", "sketch", "probablistic data structure","time series", "ARIMA", "IMA", "Markov Chain", "Monte Carlo", "MCMC", "sufficient intermediates", "continuous", "nominal", "categorical", "monoid", "group", "semi-group", "algebra", "transitive", "associative", "distributive", "identity", "closure", "inverse", "sliding window"],
  "author"        : "Alexia B. Chang <Alexia.B.Chang@yagn.us>",
  "contributors"  : [ "Herb<hsb@yagn.us>", "Hc Busy<hc.busy@yagn.us>"],
  "license"       : "Yagnus",
  "repository"    : "git://github.com/alexia-b-chang/yag.js.git",
  "main"          : "yagnus.js",
  "version"       : "0.0.17",
  "readmeFilename":"README.md",
}