burdukiewicz.bib

@manual{R,
	address = {Vienna, Austria},
	author = {{R Core Team}},
	organization = {R Foundation for Statistical Computing},
	title = {R: A Language and Environment for Statistical Computing},
	url = {http://www.R-project.org/},
	year = {2015}
}

@misc{xie_xaringan:_2018,
	abstract = {Create HTML5 slides with R Markdown and the JavaScript library 'remark.js{\rq} ({\textless}https://remarkjs.com{\textgreater}).},
	author = {Yihui Xie and Claus Thorn Ekstr{\o}m and Dawei Lang and Garrick Aden-Buie and Ole Petter Bang (CSS in rmarkdown/templates/xaringan/resources/default.css) and Patrick Schratz and Sean Lopp},
	copyright = {MIT + file LICENSE},
	month = feb,
	shorttitle = {xaringan},
	title = {xaringan: {Presentation} {Ninja}},
	url = {https://CRAN.R-project.org/package=xaringan},
	urldate = {2018-07-06},
	year = {2018}
}

@article{mlr,
	author = {Bernd Bischl and Michel Lang and Lars Kotthoff and Julia Schiffner and Jakob Richter and Erich Studerus and Giuseppe Casalicchio and Zachary M. Jones},
	journal = {Journal of Machine Learning Research},
	number = {170},
	pages = {1--5},
	title = {{mlr}: Machine Learning in R},
	url = {http://jmlr.org/papers/v17/15-066.html},
	volume = {17},
	year = {2016}
}

@article{archivist,
    title = {{archivist}: An {R} Package for Managing, Recording and Restoring Data Analysis Results},
    author = {Przemyslaw Biecek and Marcin Kosinski},
    journal = {Journal of Statistical Software},
    year = {2017},
    volume = {82},
    number = {11},
    pages = {1--28},
    doi = {10.18637/jss.v082.i11}
  }

@misc{biecek_dalex:_2018,
	abstract = {Machine Learning (ML) models are widely used and have various applications in classification or regression. Models created with boosting, bagging, stacking or similar techniques are often used due to their high performance, but such black-box models usually lack of interpretability. DALEX package contains various explainers that help to understand the link between input variables and model output. The single\_variable() explainer extracts conditional response of a model as a function of a single selected variable. It is a wrapper over packages 'pdp' and 'ALEPlot'. The single\_prediction() explainer attributes parts of a model prediction to particular variables used in the model. It is a wrapper over 'breakDown' package. The variable\_dropout() explainer calculates variable importance scores based on variable shuffling. All these explainers can be plotted with generic plot() function and compared across different models.},
	author = {Przemyslaw Biecek},
	copyright = {GPL-2 {\textbar} GPL-3 [expanded from: GPL]},
	month = jun,
	shorttitle = {{DALEX}},
	title = {{DALEX}: {Descriptive} {mAchine} {Learning} {EXplanations}},
	url = {https://CRAN.R-project.org/package=DALEX},
	urldate = {2018-07-06},
	year = {2018}
}

@article{live,
	archiveprefix = {arXiv},
	author = {Mateusz Staniak and Przemyslaw Biecek},
	eprint = {1804.01955},
	journal = {ArXiv e-prints},
	month = {Apr},
	primaryclass = {stat.ML},
	title = {Explanations of Model Predictions with {live} and {breakDown} Packages},
	url = {https://arxiv.org/abs/1804.01955},
	year = {2018}
}

@manual{FactorMerger,
	author = {Agnieszka Sitko and Przemyslaw Biecek},
	title = {{The Merging Path Plot}: adaptive fusing of k-groups with likelihood-based model selection},
	url = {https://arxiv.org/abs/1709.04412},
	year = {2017}
}

@article{pdp,
	author = {Brandon M. Greenwell},
	journal = {The R Journal},
	number = {1},
	pages = {421--436},
	title = {pdp: An R Package for Constructing Partial Dependence Plots},
	url = {https://journal.r-project.org/archive/2017/RJ-2017-016/index.html},
	volume = {9},
	year = {2017}
}

@misc{apley_aleplot:_2018,
	abstract = {Visualizes the main effects of individual predictor variables and their second-order interaction effects in black-box supervised learning models. The package creates either Accumulated Local Effects (ALE) plots and/or Partial Dependence (PD) plots, given a fitted supervised learning model.},
	author = {Dan Apley},
	copyright = {GPL-2},
	month = may,
	shorttitle = {{ALEPlot}},
	title = {{ALEPlot}: {Accumulated} {Local} {Effects} ({ALE}) {Plots} and {Partial} {Dependence} ({PD}) {Plots}},
	url = {https://CRAN.R-project.org/package=ALEPlot},
	urldate = {2018-07-06},
	year = {2018}
}

@misc{bivand_rgdal:_2018,
	abstract = {Provides bindings to the 'Geospatial' Data Abstraction Library ('GDAL') ({\textgreater}= 1.11.4) and access to projection/transformation operations from the 'PROJ.4' library. The 'GDAL' and 'PROJ.4' libraries are external to the package, and, when installing the package from source, must be correctly installed first. Both 'GDAL' raster and 'OGR' vector map data can be imported into R, and 'GDAL' raster data and 'OGR' vector data exported. Use is made of classes defined in the 'sp' package. Windows and Mac Intel OS X binaries (including 'GDAL', 'PROJ.4' and 'Expat') are provided on 'CRAN'.},
	author = {Roger Bivand and Tim Keitt and Barry Rowlingson and Edzer Pebesma and Michael Sumner and Robert Hijmans and Even Rouault and Frank Warmerdam and Jeroen Ooms and Colin Rundel},
	copyright = {GPL-2 {\textbar} GPL-3 [expanded from: GPL (≥ 2)]},
	keywords = {Spatial},
	month = jun,
	shorttitle = {rgdal},
	title = {rgdal: {Bindings} for the '{Geospatial}' {Data} {Abstraction} {Library}},
	url = {https://CRAN.R-project.org/package=rgdal},
	urldate = {2018-07-06},
	year = {2018}
}

@misc{bivand_rgeos:_2018,
	abstract = {Interface to Geometry Engine - Open Source ('GEOS') using the C 'API' for topology operations on geometries. The 'GEOS' library is external to the package, and, when installing the package from source, must be correctly installed first. Windows and Mac Intel OS X binaries are provided on 'CRAN'.},
	author = {Roger Bivand and Colin Rundel and Edzer Pebesma and Rainer Stuetz and Karl Ove Hufthammer and Patrick Giraudoux and Martin Davis and Sandro Santilli},
	copyright = {GPL-2 {\textbar} GPL-3 [expanded from: GPL (≥ 2)]},
	keywords = {Spatial},
	month = jun,
	shorttitle = {rgeos},
	title = {rgeos: {Interface} to {Geometry} {Engine} - {Open} {Source} ('{GEOS}')},
	url = {https://CRAN.R-project.org/package=rgeos},
	urldate = {2018-07-06},
	year = {2018}
}

@misc{pebesma_sf:_2018,
	abstract = {Support for simple features, a standardized way to encode spatial vector data. Binds to 'GDAL' for reading and writing data, to 'GEOS' for geometrical operations, and to 'PROJ' for projection conversions and datum transformations.},
	author = {Edzer Pebesma and Roger Bivand and Etienne Racine and Michael Sumner and Ian Cook and Tim Keitt and Robin Lovelace and Hadley Wickham and Jeroen Ooms and Kirill M{\"u}ller},
	copyright = {GPL-2 {\textbar} MIT + file LICENSE},
	keywords = {Spatial; SpatioTemporal},
	month = may,
	shorttitle = {sf},
	title = {sf: {Simple} {Features} for {R}},
	url = {https://CRAN.R-project.org/package=sf},
	urldate = {2018-07-06},
	year = {2018}
}

@misc{hijmans_raster:_2017,
	abstract = {Reading, writing, manipulating, analyzing and modeling of gridded spatial data. The package implements basic and high-level functions. Processing of very large files is supported.},
	author = {Robert J. Hijmans and Jacob van Etten and Joe Cheng and Matteo Mattiuzzi and Michael Sumner and Jonathan A. Greenberg and Oscar Perpinan Lamigueiro and Andrew Bevan and Etienne B. Racine and Ashton Shortridge and Aniruddha Ghosh},
	copyright = {GPL (≥ 3)},
	keywords = {Spatial; SpatioTemporal},
	month = nov,
	shorttitle = {raster},
	title = {raster: {Geographic} {Data} {Analysis} and {Modeling}},
	url = {https://CRAN.R-project.org/package=raster},
	urldate = {2018-07-06},
	year = {2017}
}

@misc{roussel_lidr:_2018,
	abstract = {Airborne LiDAR (Light Detection and Ranging) interface for data manipulation and visualization. Read/write 'las' and 'laz' files, computation of metrics in area based approach, point filtering, artificial point reduction, classification from geographic data, normalization, individual tree segmentation and other manipulations.},
	author = {Jean-Romain Roussel and David Auty (Reviews the documentation) and Florian De Boissieu (Fixed {and} bugs improved catalog features) and Andrew S{\'a}nchez Meador (Implemented lassnags)},
	copyright = {GPL-3},
	month = jun,
	shorttitle = {{lidR}},
	title = {{lidR}: {Airborne} {LiDAR} {Data} {Manipulation} and {Visualization} for {Forestry} {Applications}},
	url = {https://CRAN.R-project.org/package=lidR},
	urldate = {2018-07-06},
	year = {2018}
}

@misc{technology_plumber:_2018,
	abstract = {Gives the ability to automatically generate and serve an HTTP API from R functions using the annotations in the R documentation around your functions.},
	author = {Trestle Technology and LLC and Jeff Allen and Frans van Dunn{\'e} and Sebastiaan Vandewoude and SmartBear {Software (swagger-ui)}},
	copyright = {MIT + file LICENSE},
	keywords = {ModelDeployment; WebTechnologies},
	month = jun,
	shorttitle = {plumber},
	title = {plumber: {An} {API} {Generator} for {R}},
	url = {https://CRAN.R-project.org/package=plumber},
	urldate = {2018-07-06},
	year = {2018}
}

@misc{allaire_keras:_2018,
	abstract = {Interface to 'Keras' {\textless}https://keras.io{\textgreater}, a high-level neural networks 'API'. 'Keras' was developed with a focus on enabling fast experimentation, supports both convolution based networks and recurrent networks (as well as combinations of the two), and runs seamlessly on both 'CPU' and 'GPU' devices.},
	author = {J. J. Allaire and Fran{\c c}ois Chollet and RStudio and Google and Yuan Tang and Daniel Falbel and Wouter Van Der Bijl and Martin Studer},
	copyright = {MIT + file LICENSE},
	keywords = {HighPerformanceComputing; ModelDeployment},
	month = apr,
	shorttitle = {keras},
	title = {keras: {R} {Interface} to '{Keras}'},
	url = {https://CRAN.R-project.org/package=keras},
	urldate = {2018-07-06},
	year = {2018}
}

@article{liu_r_2014,
	abstract = {It is scientifically and ethically imperative that the results of statistical analysis of biomedical research data be computationally reproducible in the sense that the reported results can be easily recapitulated from the study data. Some statistical analyses are computationally a function of many data files, program files, and other details that are updated or corrected over time. In many applications, it is infeasible to manually maintain an accurate and complete record of all these details about a particular analysis.
PMID: 24886202},
	author = {Zhifa Liu and Stan Pounds},
	copyright = {2014 Liu and Pounds; licensee BioMed Central Ltd.},
	doi = {10.1186/1471-2105-15-138},
	issn = {1471-2105},
	journal = {BMC Bioinformatics},
	language = {en},
	month = may,
	number = {1},
	pages = {138},
	pmid = {24886202},
	title = {An {R} package that automatically collects and archives details for reproducible computing},
	url = {http://www.biomedcentral.com/1471-2105/15/138/abstract},
	urldate = {2014-07-01},
	volume = {15},
	year = {2014}
}

@article{rodiger_r_2015,
	author = {Stefan R{\"o}diger and Micha{\l} Burdukiewicz and Konstantin A. Blagodatskikh and Peter Schierack},
	journal = {The R Journal},
	number = {2},
	pages = {127--150},
	title = {R as an {Environment} for the {Reproducible} {Analysis} of {DNA} {Amplification} {Experiments}},
	url = {http://journal.r-project.org/archive/2015-1/RJ-2015-1.pdf},
	volume = {7},
	year = {2015}
}

@book{gandrud_reproducible_2013,
	abstract = {Bringing together computational research tools in one accessible source, Reproducible Research with R and RStudio guides you in creating dynamic and highly reproducible research. Suitable for researchers in any quantitative empirical discipline, it presents practical tools for data collection, data analysis, and the presentation of results.  With straightforward examples, the book takes you through a reproducible research workflow, showing you how to use:   R for dynamic data gathering and automated results presentation  knitr for combining statistical analysis and results into one document LaTeX for creating PDF articles and slide shows, and Markdown and HTML for presenting results on the web  Cloud storage and versioning services that can store data, code, and presentation files; save previous versions of the files; and make the information widely available  Unix-like shell programs for compiling large projects and converting documents from one markup language to another RStudio to tightly integrate reproducible research tools in one place   Whether you{\rq}re an advanced user or just getting started with tools such as R and LaTeX, this book saves you time searching for information and helps you successfully carry out computational research. It provides a practical reproducible research workflow that you can use to gather and analyze data as well as dynamically present results in print and on the web. Supplementary files used for the examples and a reproducible research project are available on the author{\rq}s website.},
	author = {Christopher Gandrud},
	isbn = {978-1-4665-7284-3},
	language = {English},
	month = jul,
	publisher = {Chapman and Hall/CRC},
	title = {Reproducible {Research} with {R} and {RStudio}},
	year = {2013}
}

@article{leeper_archiving_2014,
	author = {Thomas J. Leeper},
	journal = {The R Journal},
	month = jun,
	number = {1},
	pages = {151--158},
	title = {Archiving {Reproducible} {Research} with {R} and {Dataverse}},
	url = {http://journal.r-project.org/archive/2014-1/leeper.pdf},
	volume = {6},
	year = {2014}
}

@article{gentleman_statistical_2007,
	author = {Robert Gentleman and Duncan {Temple Lang}},
	doi = {10.1198/106186007X178663},
	issn = {1061-8600, 1537-2715},
	journal = {Journal of Computational and Graphical Statistics},
	language = {en},
	month = mar,
	number = {1},
	pages = {1--23},
	title = {Statistical {Analyses} and {Reproducible} {Research}},
	url = {http://www.tandfonline.com/doi/abs/10.1198/106186007X178663},
	urldate = {2018-06-18},
	volume = {16},
	year = {2007}
}