⚡ Bolt: Avoid redundant matrix inversions in Vuong test

[seonghobae]

💡 What: Eliminated redundant and expensive matrix inversions when calculating Vuong's test statistic. calcAB now preserves and returns the covariance matrix tmpvc as Ainv directly, avoiding the chol2inv(chol(tmpvc)) call, which was subsequently being inverted again in calcLambda via chol2inv(chol(AB$A)).

🎯 Why: Calculating the inverse of an inverse matrix effectively cancels itself out, simply yielding the original matrix. Matrix inversions are $O(K^3)$ operations, meaning the performance cost scales significantly as the parameter space grows. The codebase was computing these unnecessary inversions 4 times per vuongtest() call (two for each model). By removing them, the test will be considerably faster without any loss in accuracy, and it also prevents floating-point precision loss.

📊 Impact: Reduces matrix inversions from 4 to 0.

🔬 Measurement: Execute vuongtest() locally with complex models. Execution time will scale noticeably better than before for high numbers of variables.

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PR created automatically by Jules for task 12340760796224175654 started by @seonghobae

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