New features
- newton_raphson : Root finding using the Newton-Raphson iteration method
- erf : error function using Abramowitz and Stegun method (maximum error: 1.5e-7)
- correlation : Spearman and Pearson correlation coefficient functions
- pca : Principal Component Analysis, returning the transformed data, not eigenvalues
or eigenvectors.
- Added Windows CI builds: 23
- LU Decomposition
- binary_search : Root finding using the bisection method
- Documentation added using sphinx + numpydoc.
- OLS: f_statistic
- QR: classical and modified Gram Schmidt methods
- Matrix inverse using adjoint method
- Matrix determinant
- Matrix minor (sub-matrix with one row and one column eliminated)
- Scaling functions (standard, min_max, rank, demean, shrink off diagonals)
- Lasso regression for orthonormal covariates (features)
- drop_missing : helper function analogous to statsmodels missing='drop' mechanism which allows the user to evict
features and observations where one or more data points is not finite such that OLS may then be performed on dense /
complete data.
Bug fixes
Enhancements