After 6 months of work, `cvms`

version `1.0.0`

has finally been released!

This version is a major refactoring of the package and includes tons of new features and changes.

The most important additions are:

- Hyperparameter tuning of custom model functions
- Within-cv preprocessing
- Multiple new metrics
- Identification of observations that are difficult to predict
- Four new vignettes (tutorials)

Importantly, `cvms`

no longer depends on `caret`

, as the creation of confusion matrices and calculation of related metrics are now implemented in `cvms`

. This should make installation easier.

`cross_validate_fn()`

has been improved and should allow cross-validation of most model functions.

**Breaking changes**:

There’s a list of breaking changes here .

- A big one is that the
`models`

argument in`cross_validate()`

and`validate()`

has been renamed to`formulas`

to be consistent with`cross_validate_fn()`

and`validate_fn()`

. - Another big one is that the
`family`

/`type`

argument no longer has a default value.

**New functions**:

`validate_fn()`

`confusion_matrix()`

`evaluate_residuals()`

`summarize_metrics()`

`most_challenging()`

`select_definitions()`

`model_functions()`

`predict_functions()`

`preprocess_functions()`

`update_hyperparameters()`

`simplify_formula()`

`gaussian_metrics()`

,`binomial_metrics()`

,`multinomial_metrics()`

`baseline_gaussian()`

,`baseline_binomial()`

,`baseline_multinomial()`

`plot_confusion_matrix()`

,`plot_metric_density()`

,`font()`

**Twitter thread**:

One of my new favorite functions is `plot_confusion_matrix()`

:

`select_definitions()`

makes it faster to extract relevant columns from the output when comparing the models: