sclr 0.3.1 Unreleased

  • Updated predict.sclr to not have named columns with the new version of tibble. fa696d9

  • Made linear predictor variance calculation faster in predict.sclr. fa696d9

sclr 0.3.0 2019-11-26

  • Reparameterised the model so that all of the parameters are unconstrained. New baseline is the logit transformation of the old baseline.

  • Added the gradient ascent algorithm to handle cases with high baseline.

  • Added a warning for a possible baseline of 1.

  • Added the ability to check for a possible baseline of 1 with check_baseline.

  • Added logLik method to access likelihood from the fit object.

  • Added a warning message when the model is fit with no covariates.

sclr 0.2.0 2019-10-24

  • Added sclr_ideal_data function to simulate ideal data for the model.

  • Made simulations in data-raw self-contained.

  • Added the ability to return parameter names that are more conventional (e.g. “(Intercept)” instead of “beta_0”). See conventional_names argument in ?sclr.

  • Made convergence stricter to avoid local maxima. Argument n_conv to sclr and sclr_fit sets the number of times the algorithm has to converge. Best set (the one with maximum likelihood) is chosen out of n_conv sets. Previously, the algorithm only converged once.

  • sclr_log_likelihood can now be called with a model matrix and a model response.

  • Minor performance optimisations.

sclr 0.1.0 2019-09-04

First release.

Main features

  • Fits the scaled logit model using the Newton-Raphson method.

  • Supports the predict method for the expected value of the linear beta X part of the model.

  • Can look for covariate values corresponding to a particular protection level.