Skip to contents

Changelog

Source: NEWS.md

shorts (development version)

  • For model_in_situ() function, the filtering parameters (velocity_threshold, velocity_step, and n_observations) are returned in the corrections element of the returned object. These are also returned in the CV element of the returned object for every cross-validation fold
  • velocity_threshold parameter in the model_in_situ() function is now by default NULL, in which case the velocity of the observation with the fastest acceleration is taken as the cutoff value
  • Updated the in-situ examples in the README file
  • Cleaned the theme_bw() from README and set default ggplot theme with ggdist::ggdist()
  • Fixed the error in README example for plotting the in-situ velocity-acceleration profile

shorts 3.1.0

CRAN release: 2024-01-21

  • Fixed the error in model_radar_gun() and model_laser_gun() examples that happened on r-release-macos-arm64 and r-oldrel-macos-arm64 due to the perfect model fit causing “singular gradient matrix at initial parameter estimates”. This is sorted by adding simple noise to the simulated data
  • Modified model_in_situ() function to now use minpack.lm::nlsLM() instead of stats::lm() function, which now estimates MSS and MAC parameters, and it is thus easier to read the code, and estimate the confidence intervals
  • Updated the DESCRIPTION file with better package description

shorts 3.0.0

CRAN release: 2024-01-13

Fixed Bugs and Errors

  • Fixed the sign error in the get_air_resistance() function where both head wind and tail wind returned the same resistance.
  • Fixed the error in make_FV_profile() function when estimating force-velocity profile with wind conditions.
  • Fixed the error in convert_FV() function when converting force-velocity profile back to acceleration-velocity profile with wind conditions.

Changes in names and formats

  • Renamed the make_FV_profile() function to create_FVP().
  • Renamed the convert_FV() function to convert_FVP().
  • Renamed the output of predict_kinematics() function relative_power to power_relative to be in alignment with other variables.
  • Added work, average_power, and average_power_relative to the output of predict_kinematics() function, and removed net_horizontal_force from the output to avoid confusion whether the inertia is included or not.
  • Updated internal shorts_model_fit() function to return model fit metrics in both absolute and percentage forms.
  • Added internal model_sprint() wrapper function and updated internal new_shorts_model() constructor which provide changes in the model output format ("shorts_model" class). This will create BREAK CHANGE from the previous versions of the shorts package.

New functions and updates in functions

Datasets and documentation

  • Updated examples for the model_ family of functions.
  • Added LPS_session dataset. This dataset represents a sample data provided by Local Positioning System (LPS) on a single individual performing a single basketball practice session (aprox. 90min). Sampling frequency is 20Hz.
  • Added dynaspeed dataset.
  • Added laser_gun_data dataset.
  • Updated README to cover changes.
  • Removed Jason Vescovi from the authorship in the DESCRIPTION file and CITATION. Jason is still referenced in the dataset he provided

shorts 2.5.0

CRAN release: 2023-11-21

  • Added inertia (mass; in kg) and resistance (force; in Newtons) to predict_force_, predict_power_, predict_relative_power_, as well as predict_kinematics() functions. This increases the ability of the {shorts} package to deal with external resistance, like weight vest, sled, and tether devices.
  • Modified predict_kinematics() function to include inertia (mass; in kg) and resistance (force; in Newtons) parameters, as well as to allow the user to either provide the shorts_model object, or provide MSS and MAC parameters. Additional parameter add_inertia_to_vertical to allow user to decided whether inertia should be added to bodymass when calculating vertical force.
  • Added predict_force_at_velocity() function
  • Modified find_max_power_ and find_power_critial_ function to add support for the inertia (mass; in kg) and resistance (force; in Newtons) parameters
  • Simplified make_FV_profile() function and added support for the inertia (mass; in kg) and resistance (force; in Newtons) parameters. Removed the S3 print and plot methods, as well as the class type involving this function
  • Made internal convert_FV() function available for the users and added support for the inertia (mass; in kg) and resistance (force; in Newtons) parameters
  • Updated predict_time_at_distance_FV() function to use simpler convert_FV() function
  • Renamed find_max_power functions to find_peak_power to avoid confusion between Pmax and Ppeak
  • Implemented inertia (mass; in kg) and resistance (force; in Newtons) to optimal_FV() function
  • Implemented inertia (mass; in kg) and resistance (force; in Newtons) to probe_FV() function
  • I added the model_tether_DC() function with an additional distance correction (DC) parameter estimate. DC parameter serves as an intercept when distance doesn’t start at zero

shorts 2.4.0

CRAN release: 2023-03-13

  • Removed “Suggest” from the package DESCRIPTION file
  • Added use_observed_MSS parameter to model_radar_gun() and model_tether() functions. This allows user to estimate MSS parameter from the observed data
  • Add FD parameter to model_timing_gates_FD() and model_timing_gates_FD_TC() functions, which allow user to provide fixed FD parameter value.
  • Updated README.Rmd file with the example on how to use these feature. Also added a reference to a published article that was previously available only as pre-print.
  • Changed generic predict() function not to return fitted values, but to use generic predict method on the object. This allows user to use newdata parameter. Added fitted() method to return fitted values instead

shorts 2.3.0

CRAN release: 2022-10-29

  • Moved optimal profile functions to “optional-functions.R” file, with accompanying documentation name
  • Renamed the FV_slope_perc to profile_imb in the output of the optimal_ functions
  • The functions find_optimal_FV() and find_optimal_FV_peak() are now wrapped inside the optimal_FV, but the method is selected using method parameter (“max” for find_optimal_FV() and “peak” for find_optimal_FV_peak()). Please refer to examples
  • Probing analysis is now removed from optimal_ functions and moved to probe_FV() and probe_MSS_MAC() functions. Please refer to examples
  • Finding for the distance where the profile is optimal is now done using the find_optimal_distance() function that can take other optimal and probe functions as parameter. Please refer to examples
  • Removed a bug in find_optimal_FV_peak() (now implemented in find_optimal_FV() function using method="peak" parameter) where information needed to calculate air resistance was not forwarded

shorts 2.2.0

  • Expanded the results output of the find_optimal_MSS_MAC() and find_optimal_FV() functions. Some of the columns are renamed to better clarity
  • Added internal function convert_FV() to convert from FV profile to sprint profile. This is used to calculate Peak Power (Ppeak) metric and its location
  • Added min_func parameter to find_optimal_MSS_MAC_distance() and find_optimal_FV_distance(). This allows to optimize by using any other metric
  • Added find_optimal_FV_peak() function, which optimize the profile while keeping the Peak Power the same. Accompanying find_optimal_FV_peak_distance() is also added
  • Added extra reference to DESCRIPTION

shorts 2.1.0

CRAN release: 2022-09-26

  • Added mean acceleration over distance to format_splits()
  • Added model_tether() function for modeling data output from tether devices, which involve velocity at distance
  • Change the Pmax_relative to Pmax_rel in the output of the make_FV_profile() function
  • Added analytically/polynomially estimated F0_poly, F0_poly_rel, V0_poly, Pmax_poly, Pmax_poly_rel, and FV_slope_poly to the output of the make_FV_profile() function using the method outlined in Pierre Samozino and Nicolas Peyror, et al (2021) <doi: 10.1111/sms.14097>
  • Added predict_time_at_distance_FV() which uses FV profile’s F0 and V0 to predict time at distance. For more info see Pierre Samozino and Nicolas Peyror, et al (2021) <doi: 10.1111/sms.14097>
  • Added optimization functions: find_optimal_MSS_MAC(), find_optimal_MSS_MAC_distance(), find_optimal_FV(), find_optimal_FV_distance(). For more info on the FV optimization see Pierre Samozino and Nicolas Peyror, et al (2021) <doi: 10.1111/sms.14097>
  • Removed DOI from citation info

shorts 2.0.0

CRAN release: 2022-07-07

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

  • Removed the mixed-effects function due to their small usage in practice.
  • In predict_ functions, time_correction and distance_correction are no longer used, since due to novel models of estimation, 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.
  • In predict_ functions, the user now uses MSS and MAC parameters
  • 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.
  • Added create_timing_gates_splits() function to generate timing gates splits
  • 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
  • For modeling radar gun data, there is now only one function model_radar_gun() which also estimates time correction (TC) parameter.
  • 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.
  • Renamed the element LOOCV in the shorts_model object to CV to reflect above changes in model_radar_gun() function
  • Removed vignettes. I am working on a better pre-print as well as one peer-reviewed simulation paper and will reference those instead

shorts 1.1.6

CRAN release: 2021-07-19

  • Updated documentation regarding the below mentioned potential issues
  • IMPORTANT: For the model_using_splits_with_distance_correction() function the predict_XXX_at_distance() family of functions doesn’t work correctly if distance_correction is used as parameter (i.e., different than zero). This is because the model definition is completely different, and predicting on the same distance scale is not possible. Please refer to Jovanović, M., Vescovi, J.D. (2020) for more information.
  • Jovanović, M., Vescovi, J.D. (2020). shorts: An R Package for Modeling Short Sprints. Preprint available at SportRxiv. https://doi.org/10.31236/osf.io/4jw62

shorts 1.1.5

CRAN release: 2021-07-02

  • Fixed wrong error message from the plot.shorts_fv_profile() function
  • Minor model print aesthetic changes
  • Fixed calculation of the residuals, which is now correctly calculated using observed - predicted. This calculation is implemented in the residuals() S3 method, as well as with internal function shorts_model_fit() that provides model fit estimates (i.e. RMSE, MAE, MAPE)
  • Fixed calculation of the residuals in the vignette
  • Added model_using_splits_with_distance_correction() function that implements novel model definition to estimate flying start distance
  • Added mixed_model_using_splits_with_distance_correction() function that implements novel model definition to estimate flying start distance

shorts 1.1.4

CRAN release: 2020-11-29

  • Added ggplot2 and tidyr package dependency and implemented it in S3 plotting functions
  • get_FV_profile now return a object class shorts_fv_profile
  • Added S3 print and plot methods for shorts_fv_profile object
  • Renamed column force and relative_force to horizontal_force and relative_horizontal_force in the shorts_fv_profile object and data frame returned by the predict_kinematics
  • Added acceleration, bodymass, net_horizontal_force, air_resistance, and vertical_force, resultant_force_relative, power, and force_angle in the shorts_fv_profile object and data frame returned by the predict_kinematics
  • Improved examples and README.Rmd
  • Added S3 plot method for shorts_model and shorts_mixed_model objects

shorts 1.1.3

CRAN release: 2020-10-29

  • Added sample radar gun data from Jean-Benoît Morin Microsoft Excel spreadsheet, freely available at his website (accessed October 27, 2020)). For more information ?jb_morin

  • Added get_FV_profile for generating Force-Velocity profile summary using Pierre Samozino and Jean-Benoît Morin method (for more DOI:10.1111/sms.12490)

shorts 1.1.2

CRAN release: 2020-10-13

  • Added get_air_resistance function to estimate air resistance in newtons
  • Added predict_air_resistance_at_time and predict_air_resistance_at_time functions
  • Added predict_force_at_time and predict_force_at_distance functions
  • Added predict_power_at_time and predict_power_at_distance functions
  • Now power calculations in predict_relative_power_at_distance and predict_relative_power_at_time use air resistance to do the calculations. The default bodymass is 75kg. To replicate earlier function behavior, use predict_velocity_at_ multiplied by predict_acceleration_at_ to get relative power without air resistance
  • Function predict_kinematics now uses aforementioned changes in predict_relative_power_ functions, and added force and air resistance in the output
  • Functions find_max_power_distance and find_power_critical_distance now uses aforementioned changes and returns absolute power, rather than relative, and air resistance is used in the power calculations
  • Added find_max_power_time, find_velocity_critical_time, find_acceleration_critical_time, and find_power_critical_time functions
  • Added model_using_radar_with_time_correction where time_correction parameter is estimated
  • Added mixed_model_using_radar_with_time_correction where time_correction parameter is estimated

shorts 1.1.1

CRAN release: 2020-09-11

  • Added James Vescovi contributed dataset (data("vescovi")). For more info see ?vescovi

shorts 1.1.0

CRAN release: 2020-07-28

  • Used constructor functions for all modeling functions
  • Added modular short_model_fit function with three extra estimators: MAE, MAPE, and MaxAbsErr. This function is hidden from the user, but allows easier extension and modularity as well avoidance of code repetition
  • Added time_correction and distance_correction to all model parameters output to align them
  • Added S3 predict_kinematics function that predicts 0-6s distance (100Hz), velocity, acceleration, and relative power
  • Added S3 print, coef, summary, predict, and residuals methods
  • Added LOOCV option to modeling functions which provided cross-validated predictions and estimated model parameters
  • Added random parameter to mixed_ family of functions to allow higher flexibility in model definitions, but removed corrections_as_random_effects parameter. The default behavious is to use all parameters as random effects.
  • Added ... to model_using_radar and mixed_model_using_radar to be forwarded to nlme::nlme
  • Expanded the Readme.md file
  • Expanded the sprint-corrections vignette by adding a short simulation study

shorts 1.0.1

CRAN release: 2020-05-08

  • Corrected the error in vignette that showed on Solaris system. Increased the random noise to allow nls to fit

shorts 1.0.0

CRAN release: 2020-05-05

  • Renamed time_delay to time_correction in shorts::model_using_instant_velocity and shorts::mixed_model_using_instant_velocity functions to be more consistent across functions. Also, this correction is added to time, so use negative numbers instead
  • time_correction in shorts::mixed_model_using_instant_velocity and shorts::mixed_model_using_split_times is now numeric vector, not column name
  • Implemented time_correction in shorts::model_using_split_times and shorts::mixed_model_using_split_times
  • Implemented time_correction in shorts:predict_ family of functions
  • Implemented distance_correction in shorts:predict_ family of functions
  • Implemented estimation of time_correction in shorts::model_using_split_times_with_time_correction and shorts::mixed_model_using_split_times_with_time_correction
  • Implemented estimation of distance_correction in shorts::model_using_split_times_with_corrections and shorts::mixed_model_using_split_times_with_corrections
  • Fixed error in calculating PMAX in shorts::mixed_model_ functions
  • Removed maxAbsErr from model_fit element
  • Wrote “sprint-corrections” vignette explaining the idea behind sprint time and distance corrections
  • Implemented find_ family of functions for finding max power and critical distance when velocity or acceleration reaches certain threshold
  • Implemented ... to all modeling functions so that extra parameters can be forwarded to the optimization engine
  • Data split_times recreated using distance shift behind the first timing gate. Needed to demo mixed models with corrections
  • Renamed _model_using_instant_velocity to _model_using_radar
  • Renamed _model_using_split_times_ to _model_using_splits_

shorts 0.0.1

CRAN release: 2020-04-23

  • Initial version with all core functionalities
  • This package was submitted to CRAN on 2020-04-17. (commit b59402c9c5)