linreg_ess: Linear Regression Explained Sum of Squares.
In jeksterslabds/jeksterslabRds: Jekster's Lab Data Science and Statistics

Calculates explained sums of squares (ESS)

\boldsymbol{\hat{β}}^{\prime} \mathbf{X}^{\prime} \mathbf{X} \boldsymbol{\hat{β}} - n \mathbf{\bar{Y}}^{2}

1	linreg_ess(beta_hat = NULL, X, y)

`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.