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Merge pull request #360 from tidymodels/0-1-3-rc
0.1.3 RC
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DESCRIPTION

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Package: parsnip
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Version: 0.1.2.9000
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Version: 0.1.3
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Title: A Common API to Modeling and Analysis Functions
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Description: A common interface is provided to allow users to specify a model without having to remember the different argument names across different functions or computational engines (e.g. 'R', 'Spark', 'Stan', etc).
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Authors@R: c(

NEWS.md

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* `glmnet` models were fitting two intercepts (#349)
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* The various `update()` methods now work with engine-specific parameters.
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# parsnip 0.1.2
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## Breaking Changes
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# parsnip 0.0.0.9003
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* `regularization` was changed to `penalty` in a few models to be consistent with [this change](https://tidymodels.github.io/model-implementation-principles/standardized-argument-names.html#tuning-parameters).
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* If a mode is not chosen in the model specification, it is assigned at the time of fit. [51](https://github.com/topepo/parsnip/issues/51)
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* If a mode is not chosen in the model specification, it is assigned at the time of fit. [51](https://github.com/tidymodels/parsnip/issues/51)
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* The underlying modeling packages now are loaded by namespace. There will be some exceptions noted in the documentation for each model. For example, in some `predict` methods, the `earth` package will need to be attached to be fully operational.
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# parsnip 0.0.0.9002
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# parsnip 0.0.0.9000
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* The `fit` interface was previously used to cover both the x/y interface as well as the formula interface. Now, `fit()` is the formula interface and [`fit_xy()` is for the x/y interface](https://github.com/topepo/parsnip/issues/33).
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* The `fit` interface was previously used to cover both the x/y interface as well as the formula interface. Now, `fit()` is the formula interface and [`fit_xy()` is for the x/y interface](https://github.com/tidymodels/parsnip/issues/33).
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* Added a `NEWS.md` file to track changes to the package.
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* `predict` methods were [overhauled](https://github.com/topepo/parsnip/issues/34) to be [consistent](https://github.com/topepo/parsnip/issues/41).
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* `predict` methods were [overhauled](https://github.com/tidymodels/parsnip/issues/34) to be [consistent](https://github.com/tidymodels/parsnip/issues/41).
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* MARS was added.

R/aaa_models.R

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#' @param name A single character value for a new symbol in the model environment.
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#' @param value A single value for a new value in the model environment.
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#' @keywords internal
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#' @references "Making a parsnip model from scratch"
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#' \url{https://tidymodels.github.io/parsnip/articles/articles/Scratch.html}
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#' @references "How to build a parsnip model"
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#' \url{https://www.tidymodels.org/learn/develop/models/}
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#' @examples
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#' # Access the model data:
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#' current_code <- get_model_env()
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#' `func` and `defaults`. See the package vignette "Making a
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#' `parsnip` model from scratch".
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#' @param pred_obj A list with elements `pre`, `post`, `func`, and `args`.
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#' See the package vignette "Making a `parsnip` model from scratch".
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#' @param type A single character value for the type of prediction. Possible
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#' values are: `class`, `conf_int`, `numeric`, `pred_int`, `prob`, `quantile`,
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#' and `raw`.
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#' _after_ `model.matrix()` is finished. This can be useful if the model
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#' function (e.g. `lm()`) automatically generates an intercept.
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#'
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#' @references "Making a parsnip model from scratch"
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#' \url{https://tidymodels.github.io/parsnip/articles/articles/Scratch.html}
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#' @references "How to build a parsnip model"
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#' \url{https://www.tidymodels.org/learn/develop/models/}
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#' @examples
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#' # set_new_model("shallow_learning_model")
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#'

man/get_model_env.Rd

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man/set_new_model.Rd

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