NEWS (original) (raw)
sclr 0.3.1
- 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
- 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
- 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
tosclr
andsclr_fit
sets the number of times the algorithm has to converge. Best set (the one with maximum likelihood) is chosen out ofn_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
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.