logreg: Logistic Regression
In jeksterslabds/jeksterslabRds: Jekster's Lab Data Science and Statistics

Estimates parameters \mathbf{b} of a logistic regression model given by \mathbf{y_{n \time 1}} = \mathbf{X_{n \times k}b_{k \times 1}} + \mathbf{e_{n \times 1}} where \mathbf{X_{n \times k}b_{k \times 1}} = \ln ≤ft( \frac{μ}{1 - μ} \right) and μ = \frac{1}{1 + \exp(-1)} = \frac{\exp≤ft( x \right)}{\exp ≤ft( x \right) + 1}.

1	logreg(X, y, ...)

`X`	The data matrix, that is an n \times k matrix of n observations of k regressors, which includes a regressor whose value is 1 for each observation.
`y`	n \times 1 vector of observations on the regressand variable.
`...`	Arguments to be passed to the optimization function specified. This is only used when `optim` is `TRUE`