New {shorts} Package and Two New Pre-Prints - Complementary Training
New {shorts} Package and Two New Pre-Prints

New {shorts} Package and Two New Pre-Prints

I have recently updated the {shorts}package to the version 2.0.0, which is now available at CRAN. If you have been using previous version of {shorts}package, please note that with version 2.0.0 you will lose compatibility and will need to rewrite some code. Here are the changes were done:

This is NEW version of the shorts package INCOMPATIBLE with the previous due to drastic changes in functions. Here are the changes utilized:

  1. Removed the mixed-effects function due to their small usage in practice.
  2. In predict_ functions, time_correction and distance_correction are no longer used since, due to novel estimation models, it is hard to neatly implement them into functions. Now the predict_ functions predict on a scale where sprint starts at t=0 and d=0, rather than on the original (data) scale. This will also remove the confusion for the user.
  3. In predict_ functions, the user now uses MSS and MAC parameters
  4. Changed the non-linear regression estimation function from stats::nls() to minpack.lm::nlsLM() in model_ functions. This is done to avoid “singular gradient” error and inability of the stats::nls() to estimate when there are zero residuals. Please make note that now when you use … in model_ function, it will be forwarded to minpack.lm::nlsLM(). If you have been using control = stats::nls.control(warnOnly = TRUE) to avoid stats::nls() to throw error when fitting when there are zero residuals, now you can remove it. If needed use control = minpack.lm::nls.lm.control() instead.
  5. Added create_timing_gates_splits() function to generate timing gates splits
  6. For modeling timing gates, the following functions are now available: model_timing_gates(), model_timing_gates_TC(), model_timing_gates_FD(), and model_timing_gates_FD_TC(). All other functions have been removed
  7. For modeling radar gun data, there is now only one function model_radar_gun() which also estimates the time correction (TC) parameter.
  8. Function model_radar_gun() feature n-folds *cross-validation*, as opposed to model_timing_gates() family of functions, which features leave-one-out cross-validation (LOOCV) due to small number of observations. Using the CV parameter, set n-fold cross-validations for the model_radar_gun() function.
  9. Renamed the element LOOCV in the shorts_model object to CV to reflect above changes in model_radar_gun() function
  10. Removed vignettes. I am working on a better pre-print as well as one peer-reviewed simulation paper and will reference those instead

With all these changes and clean up, I have rewritten the “user-manual” which is published as a pre-print at SportRxiv (also available at Researchgate) and accepted for publication in International Journal of Strength and Conditioning (IJSC).

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The Github repository page provides another short and sweet intro to the {shorts} package.

I have also just submitted a new pre-print at SportRxiv (also available at Researchgate) which feature simulation study of the estimation effects of the three models.


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I am currently in the process of collecting the data to validate two new estimation models using the {shorts} package using real athletes. Will keep you posted here regarding the process. I am hoping to finish the damn Ph.D. next year 😉

I am a physical preparation coach from Belgrade, Serbia, grew up in Pula, Croatia (which I consider my home town). I was involved in physical preparation of professional, amateur and recreational athletes of various ages in sports such as basketball, soccer, volleyball, martial arts and tennis. Read More »

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