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bsitar 0.2.0

New feature

• Added add_model_criterion() function to compute fit criteria such as “loo”, “waic”, “kfold”, “loo_subsample”, “bayes_R2” (Bayesian R-squared), “loo_R2” (LOO-adjusted R-squared), and “marglik” (log marginal likelihood). The computed fit criteria are added to the model object for later use including comparison of models. The add_model_criterion() is a wrapper around the add_criterion() function available from the brms package

Bugfixes

• bsitar(): The ‘sigma_cov_init_beta = random’ argument was setting wrong initial values for the covariates
  included in the ‘sigma’ formula. The initials for Intercept (‘sigma_init_beta’) were used for covariates too.

Miscellaneous

• Replaced Bayesian SITAR model fit shown as example from ‘berkeley_mfit’ applied to 20 randomly selected males between 6 and 20 years of age (‘berkeley_mdata’) with ‘berkeley_exfit’ that is fit to 70 females between 8 and 20 years of age (‘berkeley_exdata’). This is done to use the example model (‘berkeley_exfit’) in vignette that provide a detailed comparison between non Bayesian SITAR model fit (using the ‘sitar’ package) and Bayesian SITAR model fit (using the ‘bsitar’ package). The vignette included in the ‘sitar’ package analysed the exact same data (70 females between 8 and 20 years of age).
• The bsitar::bsitar()```received options ```file```,file_refit, and ``file_compress to save and retreive fitted objects. See brms::brm help file for details.
• Minor corrections/changes to make R code more efficient.

bsitar 0.1.1

New feature

• Added ‘optimize_model’ function to perform model optimization by fitting model with varying degree of freedom and by transforming ‘x’ (predictor) and ‘y’ (outcome) varibales. The allowed transformations for ‘x’ and ‘y’ variables are ‘log’ (logarithmic) and ‘sqrt’ (square root) transformation. The ‘optimize_model’ performs comparison of resulting model fits based on user specified criteria such as the Watanabe–Akaike information criterion (‘waic’), leave-one-out cross-validation (‘loo’) and the Bayesian R square (‘bayes_R2’). Please see help file of ‘optimize_model’ to see documentation.

Minor changes

• Updated documentation
• Added vignette about the Bayesian SITAR model fit to height data (univariate model)
• Added issue tracker url: https://github.com/Sandhu-SS/bsitar/issues

Bugfixes

• plot_curves(): Fixed bug to remove warning “Duplicated aesthetics after name standardisation: group in when plotting together unadjusted and adjusted curves.”
• plot_curves(): Fixed bug to remove palettes error when tried plotting all four curves together (distance, velocity, adjusted and unadjusted)
• plot_curves(): and growthparameters() fixed issues relating to not using ‘dplyr::all_of()’ when within the ‘dplyr::select()’

Miscellaneous

• Added more utility functions for internal use (for consistency and efficiency)
• Minor corrections/changes to make R code more efficient and consistent across sub modules.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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