# shorts 3.2.0

* Fixed error in `predict_work_till_distance()` function
* Added `model_timing_gates_FD_fixed()` and `model_time_distance_FD_fixed()` which allow user to set the fixed `FD` parameter. This was before done using additional parameter in `model_timing_gates_FD()` and `model_time_distance_FD()` functions, but now these functions only provide estimated `FD`. This is done to allow confidence intervals estimation, which was impossible to do since fixed `FD` was also *estimated*, but it was constrained by the optimization function to user-provided value. 

# shorts 3.1.1

* 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 
* Fixed error in the `find_optimal_FV_scalar()` function and all upper-level calling functions which happens when `resistance` parameter is below zero
* Added new publication in README and DESCRIPTION
* Rebuilt `split_times` since it has `AsIs` object, causing issues with `ggplot2` plotting in the README file

# shorts 3.1.0

* 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

## 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

* Added `predict_work_till_time()` and `predict_work_till_distance()` to calculate horizontal work performance.
* Rewritten `model_radar_gun()` and added alias `model_laser_gun()` function.
* Rewritten `model_timing_gates()`, `model_timing_gates_TC()`, `model_timing_gates_FD()`, and `model_timing_gaes_FD_TC()`, functions. Now the `TC` parameter has positive sign.
* Rewritten `model_tether()` and `model_tether_DC()` functions. Changed the sign of the `DC` parameter.
* Rewritten `model_` family of functions to include `predict_` function in the model definition. Now `MSS` and `MAC` are estimated, rather than `MSS` and `TAU`. This improves readability of the model definition. Also moved `MAC` in the output after `MSS` parameter.
* Removed `model_timing_gates_FD_TC()` function.
* Added `model_timing_gates_DC()` and `model_timing_gates_TC_DC()` functions.
* Added `model_in_situ()` function which performs *embedded* (i.e., *in-situ*) analysis of the session monitoring data using GPS or LPS systems which provide instantaneous acceleration and velocity. 
* Added `model_time_distance()`, `model_time_distance_TC()`, `model_time_distance_DC()`, `model_time_distance_TC_DC()`, and `model_time_distance_FD()` functions. These are exactly the same as `model_timing_gates` family of functions, but with reverse predictor and outcome variables.
* Added `create_sprint_trace()` function for quickly creating sprint traces with time- and distance-shifts, as well as flying starts. Useful for creating examples for issues in modeling sprint traces.
* Updated S3 print and plot methods for the `"shorts_model"` class. The plot method now plots four different types of plots, depending on the `type` argument: "model" (default), "kinematics-time", "kinematics-distance", or "residuals".
* Added `confint` S3 method to provide confidence intervals of the parameters.
* Changed `coef` S3 method to return all model parameters, not only sprint parameters.

## 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

* 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

* 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

* 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

* 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

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

* 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

* 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

* 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

* Added sample radar gun data from Jean-Benoît Morin Microsoft Excel spreadsheet, freely available at his [website](https://jbmorin.net/2017/12/13/a-spreadsheet-for-sprint-acceleration-force-velocity-power-profiling/) (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

* 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

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


# shorts 1.1.0

* 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

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


# shorts 1.0.0

* 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

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