So I ended up figuring this out with the help of a friend.

The covariation matrix in LRReport (C) is used to calculate the Standard Error of each of the coefficients. Getting the square root of the values at [0,0], [1,1], ... [n,n] of the matrix will give you the standard errors of each coefficient. The t-statistic of each variable can then be calculated by dividing each coefficient (subtracted by the null hypothesis of the coefficient, which in most cases is 0) by its respective standard error. We can then use the StudentTDistribution function (https://www.alglib.net/specialfunctions/distributions/student.php) to get the integral of the t distribution. This also required the degrees of freedom, which is the number of samples subtracted by the number of variables. The result of this function can be easily modified into either a one-tailed or two-tailed p-value.

Here's a slapped together C# codeblock demonstrating how to accomplish this, in case anyone else (like me) has issues understanding the process. My project requires it be output to lists, though it could very easily be modified to output arrays instead.

**Code:**

public void GetStatisticsOfCovariationMatrix(double[] coefs, double[,] covariationMatrix, int numSamples, int numVariables, out List<double> standardErrors, out List<double> tStatistics, out List<double> pValues)

{

standardErrors = new List<double>();

for (int i = 0; i <= covariationMatrix.GetUpperBound(0); i++)

{

standardErrors.Add(Math.Sqrt(covariationMatrix[i, i]));

}

double nullHypothesisCoefficient = 0; //This may need to be changed depending on what your null hypothesis is

tStatistics = new List<double>();

for (int i = 0; i < standardErrors.Count; i++)

{

tStatistics.Add((coefs[i] - nullHypothesisCoefficient) / standardErrors[i]);

}

pValues = new List<double>();

for (int i = 0; i < tStatistics.Count; i++)

{

double tIntegral = alglib.studenttdistribution(numSamples - numVariables, Math.Abs(tStatistics[i]));

//The p value for one tail of the t-distribution

double p1 = 1 - tIntegral;

//The p value for both tails of the t-distribution

double p2 = p1 * 2;

pValues.Add(p2);

}

}