Upcoming package features

[matcasti]

For v1.1.0

• New function to perform a refit on fitted models, choosing the model with smallest hubber loss or similar loss function from bootstraped parameters or something similar. This would be useful on RRi signals where the default estimate_RRi_curve() performs poorly.
• Add an object-specific class for imported RRi signal data (and the internal example dataset), kinda "RRi_data" or similar.
• Add a print(), summary() and plot() methods por the previously mentioned object-specific class for imported RRi signal data.
• Add a predict(), coef() and coefficients() method for "RRi_fit" objects.

For v1.2.0

• New argument method = "bayesian" in estimate_RRi_curve() function using HMC-NUTS in the estimating process.
• New argument priors = [using brms style] in estimate_RRi_curve() for method = "bayesian".
• Add more methods to analyze residuals? (ACF, FFT, others?)