linreg_vcov: Linear Regression (variance-covariance of OLS estimates)
In jeksterslabds/jeksterslabRds: Jekster's Lab Data Science and Statistics

Calculates the variance-covariance matrix of ordinary least squares estimates (σ^2 ≤ft(\mathbf{X}^{\prime} \mathbf{X} \right)^{-1}).

1	linreg_vcov(beta_hat = NULL, X, y, m = FALSE, s2_est = "ols")

`beta_hat`	Vector of k estimated regression parameters. If `NULL`, regression coefficients are estimated using ≤ft( \mathbf{X}^{\prime} \mathbf{X} \right)^{-1} ≤ft( \mathbf{X}^{\prime} \mathbf{y} \right) .
`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.
`m`	Logical. If `TRUE`, the function uses an alternative formula e = \mathbf{M} \mathbf{y} . See `linreg_m` for \mathbf{M}.
`s2_est`	String. Residual variance estimator. If `"both"`, returns both OLS and ML estimates as a vector. If `"ols"`, returns OLS estimate. If `"ml"`, returns ML estimate.