Apache Groovy vs. Crystal vs. Java vs. statsmodels Comparison Chart

Crystal’s syntax is heavily inspired by Ruby’s, so it feels natural to read and easy to write, and has the added benefit of a lower learning curve for experienced Ruby devs. Crystal is statically type-checked, so any type errors will be caught early by the compiler rather than fail on runtime. Moreover, and to keep the language clean, Crystal has built-in type inference, so most type annotations are unneeded. All types are non-nilable in Crystal, and available variables are represented as a union between the type and nil. As a consequence, the compiler will automatically check for null references in compile time. Crystal’s answer to metaprogramming is a powerful macro system, which ranges from basic templating and AST inspection to types inspection and running arbitrary external programs.

statsmodels is a Python module that provides classes and functions for the estimation of many different statistical models, as well as for conducting statistical tests and statistical data exploration. An extensive list of result statistics is available for each estimator. The results are tested against existing statistical packages to ensure that they are correct. The package is released under the open-source Modified BSD (3-clause) license. statsmodels supports specifying models using R-style formulas and pandas DataFrames. Have a look at dir(results) to see available results. Attributes are described in results.__doc__ and results methods have their own docstrings. You can also use numpy arrays instead of formulas. The easiest way to install statsmodels is to install it as part of the Anaconda distribution, a cross-platform distribution for data analysis and scientific computing. This is the recommended installation method for most users.