STools contains a variety of simple tools for spectroscopy, such as reading an IRAF-formatted (multispec) echelle spectrum in FITS, measuring the wavelength of the center of a line, Gaussian convolution, deriving synthetic photometry from an input spectrum, and extracting and interpolating a MARCS model atmosphere (standard composition).

redmonster performs automated redshift measurement, physical‑parameter estimation, and spectral classification of 1D astronomical spectra. This set of Python utilities outputs the best‑fit model, redshift, classification, and derived parameters in standard formats for downstream analysis. The repository includes templates, configuration files, and documentation to enable flexible redshift and parameter estimation workflows.

FERRE matches physical models to observed data, taking a set of observations and identifying the model parameters that best reproduce the data, in a chi-squared sense. It solves the common problem of having numerical parametric models that are costly to evaluate and need to be used to interpret large data sets. FERRE provides flexibility to search for all model parameters, or hold constant some of them while searching for others. The code is written to be truly N-dimensional and fast. Model predictions are to be given as an array whose values are a function of the model parameters, i.e., numerically. FERRE holds this array in memory, or in a direct-access binary file, and interpolates in it. The code returns, in addition to the optimal set of parameters, their error covariance, and the corresponding model prediction. The code is written in FORTRAN90.