dot-slopes: Regression Slopes \boldsymbol{beta}_{2, \cdots, k}
In jeksterslabds/jeksterslabRlinreg: Jekster's Lab Linear Regression Utilities

Derives the slopes \boldsymbol{β}_{2, \cdots, k} of a linear regression model (\boldsymbol{β} minus the intercept) as a function of covariances.

1	.slopes(SigmaX = NULL, sigmayX = NULL, X, y)

`SigmaX`	`p` by `p` numeric matrix. p \times p matrix of variances and covariances between regressor variables {X}_{2}, {X}_{3}, \cdots, {X}_{k} ≤ft( \boldsymbol{Σ}_{\mathbf{X}} \right).
`sigmayX`	Numeric vector of length `p` or `p` by `1` matrix. p \times 1 vector of covariances between the regressand y variable and regressor variables X_2, X_3, \cdots, X_k ≤ft( \boldsymbol{σ}_{\mathbf{y}, \mathbf{X}} = ≤ft\{ σ_{y, X_2}, σ_{y, X_3}, \cdots, σ_{y, X_k} \right\}^{T} \right).
`X`	`n` by `k` numeric matrix. The data matrix \mathbf{X} (also known as design matrix, model matrix or regressor matrix) is an n \times k matrix of n observations of k regressors, which includes a regressor whose value is 1 for each observation on the first column.
`y`	Numeric vector of length `n` or `n` by `1` matrix. The vector \mathbf{y} is an n \times 1 vector of observations on the regressand variable.

The linear regression slopes are calculated using

\boldsymbol{β}_{2, \cdots, k} = \boldsymbol{Σ}_{\mathbf{X}}^{T} \boldsymbol{σ}_{\mathbf{y}, \mathbf{X}}

where

\boldsymbol{Σ}_{\mathbf{X}} is the p \times p covariance matrix of the regressor variables X_2, X_3, \cdots, X_k and
\boldsymbol{σ}_{\mathbf{y}, \mathbf{X}} is the p \times 1 column vector of the covariances between the regressand y variable and regressor variables X_2, X_3, \cdots, X_k