# Main functions¶

The main function in the package is called `hrf_estimation.glm()` and will extract the HRF and activation coefficients from BOLD signal.

This function takes as input a vector of conditions, a vector of onsets, the TR (float) and the matrix of BOLD measurements (of size n_timecourse * n_voxels). The return values of hrf_estimation.glm are the estimated HRF, the activation coefficients (a.k.a beta-maps). Note that the estimated HRFs are given as the coefficients in the basis of choice. To obtain the full HRF you must multiply by the basis (the example below shows an example of this)

The function `hrf_estimation.glm()` is meant to be used whenever the conditions and onsets are available. It will compute internally the design matrix without the possibility to modify it.

However, if you have already formed the design matrix and only want to estimate a R1-GLM model from that design matrix and a matrix fo BOLD measurements, then the function `hrf_estimation.rank_one()` can be used.