Probit model score (gradient) vector
Parameters : | params : array-like
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Returns : | score : ndarray, 1-D
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Notes
\frac{\partial\ln L}{\partial\beta}=\sum_{i=1}^{n}\left[\frac{q_{i}\phi\left(q_{i}x_{i}^{\prime}\beta\right)}{\Phi\left(q_{i}x_{i}^{\prime}\beta\right)}\right]x_{i}
Where q=2y-1. This simplification comes from the fact that the normal distribution is symmetric.