New {shorts} Package and Two New Pre-Prints

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