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merge pr #81: v0.2.0
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simonpcouch authored Apr 21, 2021
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2 changes: 1 addition & 1 deletion DESCRIPTION
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Package: stacks
Title: Tidy Model Stacking
Version: 0.1.0.9000
Version: 0.2.0
Authors@R: c(
person(given = "Simon",
family = "Couch",
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4 changes: 1 addition & 3 deletions NEWS.md
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# stacks

### v0.1.0.9000

Developmental version, to be released as v0.2.0.
### v0.2.0

### Breaking changes

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3 changes: 1 addition & 2 deletions R/example_data.R
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#'
#' @source
#' Julie Jung et al. (2020) Multimodal mechanosensing enables treefrog
#' embryos to escape egg-predators.
#' \url{https://doi.org/10.1242/jeb.236141}
#' embryos to escape egg-predators. \doi{10.1242/jeb.236141}
#'
#' @name example_data
NULL
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3 changes: 1 addition & 2 deletions R/tree_frogs.R
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#' @source
#'
#' Julie Jung et al. (2020) Multimodal mechanosensing enables treefrog
#' embryos to escape egg-predators.
#' \url{https://doi.org/10.1242/jeb.236141}
#' embryos to escape egg-predators. \doi{10.1242/jeb.236141}
"tree_frogs"
2 changes: 1 addition & 1 deletion README.Rmd
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Expand Up @@ -43,7 +43,7 @@ stacks is generalized with respect to:
* Cross-validation scheme: Any resampling algorithm implemented in [rsample](https://rsample.tidymodels.org/) or adjacent packages is fair game for resampling data for use in training a model stack.
* Error metric: Any metric function implemented in [yardstick](https://yardstick.tidymodels.org/) or adjacent packages is fair game for evaluating model stacks and their members. That package provides some infrastructure for creating your own metric functions as well!

stacks uses a regularized linear model to combine predictions from ensemble members, though this model type is only one of many possible learning algorithms that could be used to fit a stacked ensemble model. For implementations of additional ensemble learning algorithms, check out [h2o](http://docs.h2o.ai/h2o/latest-stable/h2o-r/docs/reference/h2o.stackedEnsemble.html) and [SuperLearner](https://cran.r-project.org/web/packages/SuperLearner/SuperLearner.pdf).
stacks uses a regularized linear model to combine predictions from ensemble members, though this model type is only one of many possible learning algorithms that could be used to fit a stacked ensemble model. For implementations of additional ensemble learning algorithms, check out [h2o](http://docs.h2o.ai/h2o/latest-stable/h2o-r/docs/reference/h2o.stackedEnsemble.html) and [SuperLearner](https://CRAN.R-project.org/package=SuperLearner).

Rather than diving right into the implementation, we'll focus here on how the pieces fit together, conceptually, in building an ensemble with `stacks`. See the `basics` vignette for an example of the API in action!

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2 changes: 1 addition & 1 deletion README.md
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Expand Up @@ -71,7 +71,7 @@ For implementations of additional ensemble learning algorithms, check
out
[h2o](http://docs.h2o.ai/h2o/latest-stable/h2o-r/docs/reference/h2o.stackedEnsemble.html)
and
[SuperLearner](https://cran.r-project.org/web/packages/SuperLearner/SuperLearner.pdf).
[SuperLearner](https://CRAN.R-project.org/package=SuperLearner).

Rather than diving right into the implementation, we’ll focus here on
how the pieces fit together, conceptually, in building an ensemble with
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17 changes: 3 additions & 14 deletions cran-comments.md
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# stacks 0.1.0

This submission is a resubmission following a request to adjust a URL with
200 status.
# stacks 0.2.0

## Test environments

* local macOS install, R 3.6.3
* local macOS install, R 4.0.3
* macOS (on github actions), release
* ubuntu 16.04 (on github actions), release, devel
* windows (on github actions), R 3.6.3, release
* windows (on win-builder), devel

## R CMD check results

There were 0 ERRORs, 0 WARNINGs, and 0 NOTEs on all systems except
the following NOTE on win-builder:

```
* checking CRAN incoming feasibility ... NOTE
Maintainer: 'Simon Couch <[email protected]>'
New submission
```
There were no ERRORs, WARNINGs, or NOTEs.

## Reverse dependencies

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