title: Is there a Java library for better linear regression? (E.g., iteratively reweighted least squares)
body: <p>I am struggling to find a way to perform better linear regression.  I have been using the <a href="http://en.wikipedia.org/wiki/Moore%E2%80%93Penrose_pseudoinverse" rel="noreferrer">Moore-Penrose pseudoinverse</a> and <a href="http://en.wikipedia.org/wiki/Qr_decomposition" rel="noreferrer">QR decomposition</a> with <a href="http://math.nist.gov/javanumerics/jama/" rel="noreferrer">JAMA library</a>, but the results are not satisfactory.  Would <a href="http://ojalgo.org/" rel="noreferrer">ojAlgo</a> be useful?  I have been hitting accuracy limits that I know should not be there.  The algorithm should be capable of reducing the impact of an input variable to zero.  Perhaps this takes the form of iteratively reweighted least squares, but I do not know that algorithm and cannot find a library for it.  The output should be a weight matrix or vector such that matrix multiplication of the input matrix by the weight matrix will yield a prediction matrix.  My input matrix will almost always have more rows than columns.  Thank you for your help.</p>