Major release
Date: 2020.12.04 Version 2.0.0 features many fundamental improvements both relating to the procedure but as well to the package itself. It introduces weighted percentiles and thus helps in correcting violations of representativeness in the norm sample. There is a new main function 'cnorm()' that returns a cnorm object. Most functions now accept this cnorm object and do not require separate data objects and statistical models. And finally S3 methods plot(), summary() and print() have been introduced.
Changes:
- complete redesign of S3 method structure and percentile weighting
- New function cnorm() that does all the data preparation and modelling in one step It returns a cnorm object, which can be used in all model check, plotting and prediction functions
- New S3 functions: print, plot, summary
- Vignette revised
- All functions have been extended to accept a cnorm object instead of data and / or model
- prepareData, rankByGroup and rankBySlidingWindow no have the option to provide a weighting parameter to compensate for imbalances. The percentiles are weighted accordingly. The weighted ranking is based on an adaption of wtd.rank of the Hmisc package, provided by the courtesy of Frank Harrell
- bestModel automatically uses the weighting parameter from the ranking (if applied)
- prepareData, rankByGroup and rankBySlidingWindow can now directly handle vectors instead of a data frame, e. g. rankByGroup(raw = elfe$raw, group = elfe$group)
- If no group is provided and only a raw vector is present e.g. ranByGroup(raw=elfe$raw), traditional ranking of a single group is done
- Power parameter k added to prepareData
- New convenience function modelSummary
- New method getNormScoreSE added: Compute SE for regression based norm scores sensu Oosterhuis van der Ark & Sijtsma (2016)