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NEWS.md

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# mikropml development version
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# mikropml 1.7.0
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- mikropml now supports datasets in `TreeSummarizedExperiment` format (#349, @TuomasBorman).
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- The correlation method and threshold used to collapse correlated features can
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now be set in `preprocess_data()` (#355, @Benjamin-Valderrama).
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# mikropml 1.6.2
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- Allow `training_frac` equal to 1 (#347, @kelly-sovacool).

README.Rmd

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install.packages('mikropml')
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```
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or the development version from
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or the development version from
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[GitHub](https://github.com/SchlossLab/mikRopML):
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```{r install_github, eval = FALSE}
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```{r deps, echo = FALSE, message = FALSE, warning = FALSE}
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library(dplyr)
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description <- utils::packageDescription('mikropml',
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description <- utils::packageDescription('mikropml',
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fields = c('Imports', 'Suggests'))
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deps <- lapply(names(description),
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deps <- lapply(names(description),
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function (x) {
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paste0('- ', x, ': ',
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description[[x]] %>%
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paste0('- ', x, ': ',
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description[[x]] %>%
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gsub("\n", " ", .))}
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) %>%
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unlist() %>%
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) %>%
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unlist() %>%
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paste(., collapse = '\n')
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```
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quick start tutorial. For a more in-depth discussion, read [all the
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vignettes](http://www.schlosslab.org/mikropml/articles/index.html) and/or take a
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look at the [reference
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documentation](http://www.schlosslab.org/mikropml/reference/index.html).
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documentation](http://www.schlosslab.org/mikropml/reference/index.html).
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You can watch the Riffomonas Project series of
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[video tutorials](https://www.youtube.com/playlist?list=PLmNrK_nkqBpKpzb9-vI4V7SdXC-jXEcmg)
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You can watch the Riffomonas Project series of
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[video tutorials](https://www.youtube.com/playlist?list=PLmNrK_nkqBpKpzb9-vI4V7SdXC-jXEcmg)
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covering mikropml and other skills related to machine learning.
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We also provide a
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[Snakemake workflow](https://github.com/SchlossLab/mikropml-snakemake-workflow)
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We also provide a
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[Snakemake workflow](https://github.com/SchlossLab/mikropml-snakemake-workflow)
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for running `mikropml` locally or on an HPC.
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We highly recommend running `mikropml` with Snakemake or another workflow
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We highly recommend running `mikropml` with Snakemake or another workflow
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management system for reproducibility and scalability of ML analyses.
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<a href="https://github.com/SchlossLab/mikropml-snakemake-workflow">
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<img src="https://raw.githubusercontent.com/SchlossLab/mikropml-snakemake-workflow/main/figures/mikropml-snakemake-workflow.png"
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<img src="https://raw.githubusercontent.com/SchlossLab/mikropml-snakemake-workflow/main/figures/mikropml-snakemake-workflow.png"
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height="120" align="center" />
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</a>
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## Help & Contributing
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If you come across a bug,
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[open an issue](https://github.com/SchlossLab/mikropml/issues)
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and include a
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[minimal reproducible example](https://www.tidyverse.org/help/).
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If you come across a bug,
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[open an issue](https://github.com/SchlossLab/mikropml/issues)
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and include a
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[minimal reproducible example](https://tidyverse.org/help/).
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If you have questions, create a new post in
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If you have questions, create a new post in
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[Discussions](https://github.com/SchlossLab/mikropml/discussions).
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If you'd like to contribute, see our guidelines
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## License
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The mikropml package is licensed under
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The mikropml package is licensed under
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[the MIT license](https://github.com/SchlossLab/mikropml/blob/main/LICENSE.md).
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Text and images included in this repository, including the mikropml logo,
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Text and images included in this repository, including the mikropml logo,
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are licensed under the [CC BY 4.0 license](https://creativecommons.org/licenses/by/4.0/).
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## Citation

README.md

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### Dependencies
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- Imports: caret, dplyr, e1071, glmnet, kernlab, MLmetrics,
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randomForest, rlang, rpart, stats, tidyselect, utils, xgboost
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- Suggests: assertthat, doFuture, forcats, foreach, future,
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future.apply, furrr, ggplot2, knitr, progress, progressr, purrr,
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rmarkdown, rsample, testthat, tidyr
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randomForest, rlang, rpart, stats, S4Vectors, SingleCellExperiment,
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SummarizedExperiment, tidyselect, TreeSummarizedExperiment, utils,
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xgboost
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- Suggests: assertthat, doFuture, forcats, foreach, furrr, future,
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future.apply, ggplot2, knitr, progress, progressr, purrr, rmarkdown,
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roxygen2, rsample, styler, testthat, tidyr, usethis
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## Usage
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reproducibility and scalability of ML analyses.
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<a href="https://github.com/SchlossLab/mikropml-snakemake-workflow">
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<img src="https://raw.githubusercontent.com/SchlossLab/mikropml-snakemake-workflow/main/figures/mikropml-snakemake-workflow.png"
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<img src="https://raw.githubusercontent.com/SchlossLab/mikropml-snakemake-workflow/main/figures/mikropml-snakemake-workflow.png"
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height="120" align="center" /> </a>
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## Help & Contributing
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If you come across a bug, [open an
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issue](https://github.com/SchlossLab/mikropml/issues) and include a
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[minimal reproducible example](https://www.tidyverse.org/help/).
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[minimal reproducible example](https://tidyverse.org/help/).
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If you have questions, create a new post in
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[Discussions](https://github.com/SchlossLab/mikropml/discussions).
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To cite mikropml in publications, use:
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> <p>
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>
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> Topçuoğlu BD, Lapp Z, Sovacool KL, Snitkin E, Wiens J, Schloss PD
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> (2021). “mikropml: User-Friendly R Package for Supervised Machine
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> Learning Pipelines.” <em>Journal of Open Source Software</em>,
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pages = {3073},
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doi = {10.21105/joss.03073},
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url = {https://joss.theoj.org/papers/10.21105/joss.03073},
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}
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}
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## Why the name?
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