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@@ -37,6 +37,7 @@ A curated list of insanely awesome libraries, packages and resources for Quants
 - [pymc3](https://docs.pymc.io/) - `Python` - Probabilistic Programming in Python: Bayesian Modeling and Probabilistic Machine Learning with Theano. [GitHub](https://github.com/pymc-devs/pymc)
 - [modelx](https://docs.modelx.io/) - `Python` - Python reimagination of spreadsheets as formula-centric objects that are interoperable with pandas. [GitHub](https://github.com/fumitoh/modelx)
 - [ArcticDB](https://github.com/man-group/ArcticDB) - `Python` - High performance datastore for time series and tick data.
+- [CRNG](https://github.com/brotto/crng) - `Python` - Contingency Random Number Generator that produces random numbers with real financial market statistical signatures (fat tails, volatility clustering, kurtosis). Matches 86% of real market metrics vs 14% for NumPy.
 - [xts](https://github.com/joshuaulrich/xts) - `R` - eXtensible Time Series: Provide for uniform handling of R's different time-based data classes by extending zoo, maximizing native format information preservation and allowing for user level customization and extension, while simplifying cross-class interoperability.
 - [data.table](https://github.com/Rdatatable/data.table) - `R` - Extension of data.frame: Fast aggregation of large data (e.g. 100GB in RAM), fast ordered joins, fast add/modify/delete of columns by group using no copies at all, list columns and a fast file reader (fread). Offers a natural and flexible syntax, for faster development.
 - [sparseEigen](https://github.com/dppalomar/sparseEigen) - `R` - Sparse principal component analysis.