R package references
Prabhanjan Tattar (2015). ACSWR: A Companion Package for the Book "A
Course in Statistics with R". R package version 1.0.
https://CRAN.R-project.org/package=ACSWR
Alfaro, E., Gamez, M. Garcia, N.(2013). adabag: An R Package for
Classification with Boosting and Bagging. Journal of Statistical
Software, 54(2), 1-35. URL http://www.jstatsoft.org/v54/i02/.
Angelo Canty and Brian Ripley (2017). boot: Bootstrap R (S-Plus)
Functions. R package version 1.3-19.
John Fox and Sanford Weisberg (2011). An {R} Companion to Applied
Regression, Second Edition. Thousand Oaks CA: Sage. URL:
http://socserv.socsci.mcmaster.ca/jfox/Books/Companion car
Max Kuhn. Contributions from Jed Wing, Steve Weston, Andre Williams,
Chris Keefer, Allan Engelhardt, Tony Cooper, Zachary Mayer, Brenton
Kenkel, the R Core Team, Michael Benesty, Reynald Lescarbeau, Andrew
Ziem, Luca Scrucca, Yuan Tang, Can Candan and Tyler Hunt. (2017).
caret: Classification and Regression Training. R package version
6.0-77. https://CRAN.R-project.org/package=caret
Zachary A. Deane-Mayer and Jared E. Knowles (2016). caretEnsemble:
Ensembles of Caret Models. R package version 2.0.0.
https://CRAN.R-project.org/package=caretEnsemble Venables, W. N. & Ripley, B. D. (2002) Modern Applied Statistics
with S. Fourth Edition. Springer, New York. ISBN 0-387-95457-0 class
Marie Chavent, Vanessa Kuentz, Benoit Liquet and Jerome Saracco
(2017). ClustOfVar: Clustering of Variables. R package version 1.1.
https://CRAN.R-project.org/package=ClustOfVar David Meyer, Evgenia Dimitriadou, Kurt Hornik, Andreas Weingessel
and Friedrich Leisch (2017). e1071: Misc Functions of the Department
of Statistics, Probability Theory Group (Formerly: E1071), TU Wien.
R package version 1.6-8. https://CRAN.R-project.org/package=e1071 Alboukadel Kassambara and Fabian Mundt (2017). factoextra: Extract
and Visualize the Results of Multivariate Data Analyses. R package
version 1.0.5. https://CRAN.R-project.org/package=factoextra
Sebastien Le, Julie Josse, Francois Husson (2008). FactoMineR: An R
Package for Multivariate Analysis. Journal of Statistical Software,
25(1), 1-18. 10.18637/jss.v025.i01
Alina Beygelzimer, Sham Kakadet, John Langford, Sunil Arya, David
Mount and Shengqiao Li (2013). FNN: Fast Nearest Neighbor Search
Algorithms and Applications. R package version 1.1.
https://CRAN.R-project.org/package=FNN
Hyndman RJ (2017). _forecast: Forecasting functions for time series
and linear models_. R package version 8.2, <URL:
http://pkg.robjhyndman.com/forecast>.
David Shaub and Peter Ellis (2018). forecastHybrid: Convenient
Functions for Ensemble Time Series Forecasts. R package version
2.0.10. https://CRAN.R-project.org/package=forecastHybrid
Greg Ridgeway with contributions from others (2017). gbm:
Generalized Boosted Regression Models. R package version 2.1.3.
https://CRAN.R-project.org/package=gbm
Vincent J Carey. Ported to R by Thomas Lumley and Brian Ripley. Note
that maintainers are not available to give advice on using a package
they did not author. (2015). gee: Generalized Estimation Equation
Solver. R package version 4.13-19.
https://CRAN.R-project.org/package=gee
The H2O.ai team (2017). h2o: R Interface for H2O. R package version
3.16.0.2. https://CRAN.R-project.org/package=h2o
Andrea Peters and Torsten Hothorn (2017). ipred: Improved
Predictors. R package version 0.9-6.
https://CRAN.R-project.org/package=ipred
Alexandros Karatzoglou, Alex Smola, Kurt Hornik, Achim Zeileis
(2004). kernlab - An S4 Package for Kernel Methods in R. Journal of
Statistical Software 11(9), 1-20. URL
http://www.jstatsoft.org/v11/i09/
Friedrich Leisch & Evgenia Dimitriadou (2010). mlbench: Machine
Learning Benchmark Problems. R package version 2.1-1.
Daniel J. Stekhoven (2013). missForest: Nonparametric Missing Value
Imputation using Random Forest. R package version 1.4.
Alan Genz, Frank Bretz, Tetsuhisa Miwa, Xuefei Mi, Friedrich Leisch,
Fabian Scheipl, Torsten Hothorn (2017). mvtnorm: Multivariate Normal
and t Distributions. R package version 1.0-6. URL
http://CRAN.R-project.org/package=mvtnorm
Beck M (2016). _NeuralNetTools: Visualization and Analysis Tools for
Neural Networks_. R package version 1.5.0, <URL:
https://CRAN.R-project.org/package=NeuralNetTools>.
Venables, W. N. & Ripley, B. D. (2002) Modern Applied Statistics
with S. Fourth Edition. Springer, New York. ISBN 0-387-95457-0 nnet
Michael P. Fay, Pamela A. Shaw (2010). Exact and Asymptotic Weighted
Logrank Tests for Interval Censored Data: The interval R Package.
Journal of Statistical Software, 36(2), 1-34. URL
http://www.jstatsoft.org/v36/i02/. perm
Hadley Wickham (2011). The Split-Apply-Combine Strategy for Data
Analysis. Journal of Statistical Software, 40(1), 1-29. URL
http://www.jstatsoft.org/v40/i01/. plyr
Xavier Robin, Natacha Turck, Alexandre Hainard, Natalia Tiberti,
Frédérique Lisacek, Jean-Charles Sanchez and Markus Müller (2011).
pROC: an open-source package for R and S+ to analyze and compare ROC
curves. BMC Bioinformatics, 12, p. 77. DOI: 10.1186/1471-2105-12-77
http://www.biomedcentral.com/1471-2105/12/77/
Maja Pohar Perme and Mette Gerster (2017). pseudo: Computes
Pseudo-Observations for Modeling. R package version 1.4.3.
https://CRAN.R-project.org/package=pseudo
A. Liaw and M. Wiener (2002). Classification and Regression by
randomForest. R News 2(3), 18--22.
Aleksandra Paluszynska and Przemyslaw Biecek (2017).
randomForestExplainer: Explaining and Visualizing Random Forests in
Terms of Variable Importance. R package version 0.9.
https://CRAN.R-project.org/package=randomForestExplainer
Terry Therneau, Beth Atkinson and Brian Ripley (2017). rpart:
Recursive Partitioning and Regression Trees. R package version
4.1-11. https://CRAN.R-project.org/package=rpart
Prabhanjan Tattar (2013). RSADBE: Data related to the book "R
Statistical Application Development by Example". R package version
1.0. https://CRAN.R-project.org/package=RSADBE
Therneau T (2015). _A Package for Survival Analysis in S_. version
2.38, <URL: https://CRAN.R-project.org/package=survival>. survival
Terry M. Therneau and Patricia M. Grambsch (2000). _Modeling Survival
Data: Extending the Cox Model_. Springer, New York. ISBN
0-387-98784-3.
Tianqi Chen, Tong He, Michael Benesty, Vadim Khotilovich and Yuan
Tang (2018). xgboost: Extreme Gradient Boosting. R package version
