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\section{Method}  The Square Root Information Filter (SRIF) was originally developed by Bierman in 1977 [ref]. The algorithm minimizes $\chi^2$ for time series data with Gaussian errors, and was based on the Kalman filter algorithm. SRIF is more stable, more accurate, and faster than the current algorithm used in \textbf{shape}. SRIF is also more numerically stable (and, in some cases faster) than a standard Gauss-Newton routine. My implmentation of SRIF includes some changes to the original algorithm, which will be discussed in section blah.blah. ?.  \par The fundamental difference between SRIF algorithm and a classic Gauss-Newton routine is the use of matrix square roots and Householder operations for increased numerical stability.  \subsecton{Steepest Descent Routine}