# traumar 1.2.5

- An update was made to `traumar::seqic_indicator_7()` in the portion of the code where the `denominator_7` is calculated. The denominator is no longer calculated as `dplyr::n()`, and instead uses the following in order to accurately calculate the denominator that reflects the definitive care population:

`denominator_7 = sum({{ transfer_out_indicator }}} %in% c(FALSE, "No"), na.rm = TRUE)`

# traumar 1.2.4

- Added the functions to perform all data validation. These functions take the form of `validate_*()`, such as `validate_character_factor()`. After exploring options from other packages to lean on existing methods to validate data via functional programming, the decision was made to create functions "in-house" to avoid issues with adding another dependency. The `validate_*()` family of functions is not exported from `traumar`.

- Updated unit tests throughout the package given the addition of new data validation methodology.

- Ensured that plenty of comments are provided throughout the functions in the package to enhance understanding of the code and collaboration.

- `pretty_number()` now uses a `digits` argument instead of `n_decimal`. `n_decimal` has been deprecated and will now issue a warning, but the function will still work using the `n_decimal` argument. In a later release, `n_decimal` will be fully deprecated.

- `rmm()` and `rm_bin_summary()` will now fail if the `seed` argument is not of class numeric or integer.

- Added section headers throughout the code base to make navigation within most modern IDEs easier.

- Error/warning messages will now directly name the function in the call stack that is most helpful. For example, if some data validation triggers an error within `traumar::seqic_indicator1()`, then `traumar::seqic_indicator1()` will show up in the error message, instead of some other function such as a `cli` function or one of the new `validate_*()` functions.

- Improved `small_count_label()` code readability in the control flow chunk where values less than the cutoff are manipulated depending on user input.

- `trauma_performance()` now acts as a wrapper for `trauma_case_mix()` as `trauma_performance()` now calls `trauma_case_mix()` under the hood instead of explicitly performing the same operations. This makes the functionality more maintainable for any future iterations.

# traumar 1.2.3

- `probability_of_survival()` was updated to enhance code readability and leverage mathematical notation in the calculation of predicted survival probabilities. The function now aligns with the coefficients published in Norouzi et al. (2013) and Merchant et al. (2023). Consistent with Boyd et al. (1987), the function does not treat patients under 15 years of age differently and accounts for penetrating injuries similarly to other age groups. This update ensures a standardized approach to calculating survival probabilities for both blunt and penetrating traumas.

- From this release forward, all development of `traumar` will be done in the IDE Positron!

# traumar 1.2.2

- `rmm()`, `rm_bin_summary()`, and `nonlinear_bins()`: Updated the documentation for the threshold arguments along with the divisor arguments. These were updated along with the Details section of the `nonlinear_bins()` function documentation to provide a better explanation of the binning algorithm under the hood.

- `probability_of_survival()`: Updated the Return section documentation to be more accurate that the output is not a percentage, it is the predicted probability of survival on a scale from 0 to 1. A previous version of this function was multiplied by 100 to seem more like a percentage.

# traumar 1.2.1

- Within the `trauma_performance()` function, renamed the variable `predicted_prob_death` to `scale_factor` which is commensurate with the source literature.

- updated comments in `trauma_performance()` for `z_method` method of the `Z_score` to reflect the right text.

- In `trauma_performance()`, completed the comment where the `scale_factor` is created so that it is complete and clear.

- Corrected a test error at CRAN from using bootstrap CI process in testing with 100,000 observations and 100 bootstrap samples to make sure `rmm()` and `rm_bin_summary()` ran in under 60 sec. That test now does not use the bootstrap process so the core function can be tested and will always run in under a minute with 100,000 observations.

- Cleaned up other tests within for relative_mortality.R that were checking for correct error / warning handling where multiple lines of output were sent to the console. Built a custom function to deal with those scenarios and correctly perform those unit tests.

# traumar 1.2.0

- This minor release introduces functionality to demonstrate how the Iowa trauma system currently engages in the quality improvement process using the System Evaluation and Quality Improvement Committee (SEQIC) Indicators.

- Added a [Contributor Code of Conduct](https://bemts-hhs.github.io/traumar/CODE_OF_CONDUCT.html) and support information.

- Additionally, a convenience function `is_it_normal()` provides the ability for users of `traumar` to get descriptive statistics on one or more numeric variables, with optional normality tests, and diagnostic plots (for one variable only). Grouping functionality is also supported in `is_it_normal()` to conduct exploratory data analysis of one or more variables within zero or more groups.

- Added the following functions:

  - `seqic_indicator_1()`
  - `seqic_indicator_2()`
  - `seqic_indicator_3()`
  - `seqic_indicator_4()`
  - `seqic_indicator_5()`
  - `seqic_indicator_6()`
  - `seqic_indicator_7()`
  - `seqic_indicator_8()`
  - `seqic_indicator_9()`
  - `seqic_indicator_10()`
  - `seqic_indicator_11()`
  - `seqic_indicator_12()`
  - `seqic_indicator_13()`
  - `is_it_normal()`

- A fix was applied to `nonlinear_bins()` to make the `percent` column calculate correctly when groups were not introduced.

- Removed hard-coded rounding from most calculations within the package where possible.

- Improved examples for the the package's README, `probability_of_survival()`, `nonlinear_bins()`, `rmm()`, and `rm_bin_summary()` using more helpful data.

- Improved error messages coming from `nonlinear_bins()` when the argument `Ps_col` does not follow the expected distribution of the calculated probability of survival, and/or a sample size too small to calculate bins is passed to the function, including when passed to `rmm()` and `rm_bin_summary()`.

- Code formatting changed to the `air` package through the RStudio IDE.

- Updated data validation for `trauma_case_mix()`, `trauma_performance()`, `nonlinear_bins()`, `rmm()`, and `rm_bin_summary()` to provide improved messaging related to missings in `Ps_col` and `outcome_col` .

- Across functions using the probability of survival calculation, it is expected that Ps values have a range of \[0, 1\]. Functions will no longer handle values in percentage format (e.g. 10, 50, 98).

- The `outcome` argument was removed from `trauma_performance()` to remove ambiguity in the nature of the `outcome_col` values. Only values of `TRUE/FALSE` and `1/0` are accepted.

- The `diagnostics` argument was removed from `trauma_performance()` to make the user interface smoother. Instead of providing guidance via outputs to the console, users are encouraged to seek assistance with interpreting results via the source academic literature and the package documentation.

- `trauma_performance()` will no longer provide a pivoted output as a default. Users can elect to pivot the outputs as needed in their workflows.

- `rmm()` and `rm_bin_summary()` now have a new argument `bootstrap_ci` that allows a user to elect to use the bootstrap CIs, or not. `bootstrap_ci` defaults to `TRUE` in order to better support backward compatibility.

# traumar 1.1.0

## New Features

- Added optional grouping functionality to `nonlinear_bins()`, `rmm()`, and `rm_bin_summary()`.

  - Setting `group_vars = NULL` applies the functions to the entire dataset without subgrouping.

  - For pivoting the `rmm()` outputs longer, setting `pivot = TRUE` will work when `group_vars` is invoked by pivoting longer with the grouping context.

## Enhancements

- Improved `NA` handling in `rmm()` and `rm_bin_summary()`.

- Ensured RMM calculations remain within the expected range of \[-1 to 1\], including their 95% confidence intervals.

- Optimized `nonlinear_bins()` by replacing its internal `for` loop with `dplyr` functions, enhancing accuracy and efficiency without introducing breaking changes.

- Improved command line messaging and documentation within `rmm()` and `rm_bin_summary()` regarding probability of survival values `Ps_col < 0` and `Ps_col > 1`. Now, these functions will throw an error if probability of survival values are `Ps_col < 0` or `Ps_col > 1`.

- The `nonlinear_bins()` function has improved data validation for the `Ps_col` variable.

------------------------------------------------------------------------

# traumar 1.0.0

- Initial release to CRAN.\
- Achieved comprehensive test coverage (\>90%).

------------------------------------------------------------------------

# traumar 0.0.1.9000

- Introduced `probability_of_survival()` function.\
- Expanded outputs for:
  - `rmm()`\
  - `rm_bin_summary()`\
  - `nonlinear_bins()`\
- Updated existing tests and added new test cases.\
- Began test coverage improvements.

------------------------------------------------------------------------

# traumar 0.0.1

- Introduced core package functions:
  - `trauma_case_mix()`\
  - `trauma_performance()`\
  - `rmm()`\
  - `rm_bin_summary()`\
  - `nonlinear_bins()`\
  - `impute()`\
  - `normalize()`\
  - `season()`\
  - `weekend()`\
  - `pretty_number()`\
  - `pretty_percent()`\
  - `small_count_label()`\
  - `stat_sig()`\
  - `theme_cleaner()`\
  - `%not_in%`\
- Established package framework and initialization.