  A new package for nonlinear least squares fitting is introduced in this
paper. This package implements a recently developed algorithm that, for certain
types of nonlinear curve fitting, reduces the number of nonlinear parameters to
be fitted. One notable feature of this method is the absence of initialization
which is typically necessary for nonlinear fitting gradient-based algorithms.
Instead, just some bounds for the nonlinear parameters are required. Even
though convergence for this method is guaranteed for exponential decay using
the max-norm, the algorithm exhibits remarkable robustness, and its use has
been extended to a wide range of functions using the Euclidean norm.
Furthermore, this data-fitting package can also serve as a valuable resource
for providing accurate initial parameters to other algorithms that rely on
them.
