БИБЛИОГРАФИЯ И ИНТЕРНЕТ-РЕСУРСЫ

Основные литературные ссылки по тексту книги

Рекомендуемые пособия по изучению R

Зарядов И.С. Введение в статистический пакетR: типы переменных, структуры данных, чтение и запись информации, графика. Москва: Изд-во РУДНБ, 2010а. 207 с. PDF

Зарядов И.С. Статистический пакет R: теория вероятностей и математическая статистика. Москва: Изд-во РУДНБ, 2010б. 141 с. PDF

биологии и экологии с использованием R. Тольятти : Кассандра, 2014. 314 с. (PDF, данные и скрипты доступны на сайте авторов ievbras.ru/ecostat).

Altham P.M. Introduction to Statistical Modelling in R. University of Cambridge, 2012. 115 p. Bolker B.M. Ecological Models and Data in R. Princeton University Press, 2008. 517 p. Borcard D., Gillet F., Legendre P. Numerical Ecology with R. Springer, 2011. 319 p.

Chernick M.R., LaBudde R.A. An Introduction to Bootstrap Methods with Applications to R. Wiley, 2011. 236 p.

Cleveland W. Visualizing Data. At&T Bell Laboratories, 1993. 360 р. Crawley M.J. The R Book. 2nd ed. Wiley, 2013. 1076 p.

Dalgaard P. Introductory Statistics with R. 2nd ed. Springer, 2008. 364 p.

Everitt B.S., Hothorn T. A Handbook of Statistical Analyses Using R. 2nd ed. Chapman, Hall/CRC, 2010. 376 p.

Faraway J.J. Practical Regression and Anova using R. University of Bath, 2002. PDF (данные и скрипты доступны также на сайте автора)

Faraway J.J. Extending the Linear Model with R: Generalized Linear, Mixed E ects and Nonparametric Regression Models. Chapman, Hall/CRC, 2006. 345 p.

Fox J. Applied Regression Analysis and Generalized Linear Models, 2nd ed. Sage Publications, 2008.

Fox J., Weisberg S. An R Companion to Applied Regression, 2nd ed. Sage Publications, 2011. Gelman A., Hill J. Data Analysis Using Regression and Multilevel/Hierarchical Models.

Cambridge University Press, 2006. 651 p.

Kabaco R. R in Action: Data Analysis and Graphics With R. Manning Publications, 2011. 447 p. (Рус. пер.: Кабаков Р.И. R в действии: Анализ и визуализация данных в программе R / пер. с англ. П.А. Волкова. Москва: ДМК Пресс, 2014. 580 с.)

Karp N. R Commander: An Introduction. 2014. 52 p. PDF

Lam L. An Introduction to R. Vrije Universiteit Amsterdam, 2010. 212 p. PDF

Larson-Hall J. A Guide to Doing Statistics in Second Language Research Using R . New York, NY: Routledge, 2009. 320 р. PDF

Logan M. Biostatistical Design and Analysis Using R: A Practical Guide. Wiley-Blackwell, 2010. 576 p.

Maindonald J.H., Braun W.J. Data Analysis and Graphics Using R: An Example-Based Approach. 3rd ed. Cambridge University Press, 2010. 565 p.

Meys J., Vries A. R For Dummies. For Dummies, 2012. 406 p.

Ricci V. Fitting Distributions with R. 2005. 24 p. (PDF на сайте CRAN).

Sarkar D. Lattice: Multivariate Data Visualization with R. Springer, 2008. 268 p.

Venables W.N., Ripley B.D. Modern Applied Statistics with S. 4th ed. Springer, 2002. 505 p.

388

Venables W.N., Smith D.M. An Introduction to R. R Development Core Team, 2014. 105 p. (PDF на сайте CRAN). (Рус. пер.: Венэбльз У.Н., Смит Д.М. Введение в R: Заметки по R: среда программирования для анализа данных и графики: пер. с англ. Вер. 3.1.0. Москва, 2014. 109 с. PDF)

Wickham H. Advanced R. Chapman & Hall/CRC. The R Series, 2014. 476 p. (официальная бесплатная версия книги доступна по адресу http://adv-r.had.co.nz/)

Wood S.N. Generalized Additive Models: An Introduction with R. Chapman, Hall/CRC, 2006. 410 p.

Zie er A.S., Harring J.R., Long J.D. Comparing Groups: Randomization and Bootstrap Methods Using R. Wiley, 2011. 332 p.

Zuur A. F., Ieno E.N., Walker N. et al. Mixed Effects, Models and Extensions in Ecology with R. Berlin: Springer Sci., 2009. 574 p.

Методическая литература по статистическому анализу

Айвазян С.А., Мхитарян В.С. Прикладная статистика и основы эконометрии. М.: ЮНИТИ, 1998. 1022 с.

Афифи А., Эйзен С. Статистический анализ: Подход с использованием ЭВМ. М.: Мир, 1982. 488 с.

Гланц С. Медико-биологическая статистика. М.: Практика, 1999. 459 с.

Дрейпер Н., Смит Г. Прикладной регрессионный анализ. М.: Финансы и статистика. Кн. 1, 1986. 366 с. Кн. 2, 1987. 352 с.

Монтгомери Д.К. Планирование эксперимента и анализ данных. Л.: Судостроение, 1980. 384 с.

Справочник по прикладной статистике. В 2-х т. Пер. с англ. / Под ред. Э. Ллойда, У. Ледермана, Ю. Н. Тюрина. М.: Финансы и статистика. Т. 1. 1989. 510 с., Т. 2. 1990. 526 с.

Фишер Р.А. Статистические методы для исследователей. М.: Госстатиздат, 1958. 268 с.

Chambers J., Cleveland W., Kleiner B., Tukey P. Graphical Methods for Data Analysis (Statistics). Chapman and Hall/CRC, 1983. 336 p.

Hastie T., Tibshirani R., Friedman J. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. 2nd ed. Springer, 2009. 745 p.

Legendre P., Legendre L. Numerical Ecology. 3rd ed. Amsterdam: Elsevier Sci. BV, 2012. 990 p. Sokal R.R., Rohlf F.J. Biometry: The principles and practice of statistics in biological research.

3nd ed. N.Y.: Freeman, 1995. 887 p.

Zar J.H. Biostatistical Analysis. 5th ed. London: Pearson Prentice Hall, 2010. 945 p.

389

Библиографический указатель литературы по использованию R

Analyzing Spatial Models of Choice and Judgment with R / D. A. Armstrong II [et al.]. Chapman, Hall/CRC, 2014. 351 p.

390

Ando T. Bayesian Model Selection and Statistical Modeling. Chapman, Hall/CRC, 2010. 300 p. Aragón T.J. Applied Epidemiology Using R. 2013. 240 p.

Arratia A. Computational Finance: An Introductory Course with R. Atlantis Press, 2014. 305 p. Azzalini A., Capitanio A. The Skew-Normal and Related Families. Cambridge University Press,

2014. 272 p.

Baayen R.H. Analyzing Linguistic Data: A Practical Introduction to Statistics using R. Cambridge University Press, 2008. 369 p.

Barker T. Pro Data Visualization using R and JavaScript. Apress, 2013. 207 p.

Baron J., Li Y. Notes on the use of R for psychology experiments and questionnaires. University of Pennsylvania, 2003. 46 p. (PDF на сайте авторов).

Basic R for Finance / D. Würtz [et al.]. Zurich: Rmetrics Association & Finance Online Publishing, 2010. 312 p.

Beaujean A.A. Latent Variable Modeling Using R: A Step-by-Step Guide. Routledge, 2014. 218 p.

Beckerman A.P., Petchey O.L. Getting Started with R: An introduction for Biologists. Oxford University Press, 2012. 128 p.

Beeley C. Web Application Development with R Using Shiny. Packt Publishing, 2013. 110 p. Berridge D. M., Crouchley R. Multivariate Generalized Linear Mixed Models Using R. CRC

Press, 2011. 284 p.

Beyersmann J., Allignol A., Schumacher M. Competing Risks and Multistate Models with R. Springer, 2012. 249 p.

Bioconductor Case Studies / F. Hahne [et al.]. Springer, 2008. 296 p.

Bioinformatics and Computational Biology Solutions Using R and Bioconductor / ed. by R. Gentleman [et al.]. Springer, 2005. 492 p.

Bivand R. S., Pebesma E., Gómez-Rubio V. Applied Spatial Data Analysis with R. 2nd ed. Springer, 2013. 414 p.

Bliese P. Multilevel Modeling in R. R Development Core Team, 2013. 88 p. (PDF на сайте CRAN).

Bloom eld V.A. Computer Simulation and Data Analysis in Molecular Biology and Biophysics: An Introduction Using R. Springer, 2009. 321 p.

Bloom eld V.A. Using R for Numerical Analysis in Science and Engineering. Chapman, Hall/CRC, 2014. 352 p.

Bogetoft P., Otto L. Benchmarking with DEA, SFA, and R. Springer, 2011. 368 p.

Boogaart K.G. van den, Tolosana-Delgado R. Analyzing Compositional Data with R. Springer, 2013. 269 p.

Boos D.D., Stefanski L. A. Essential Statistical Inference: Theory and Methods. Springer, 2013. 567 p.

Braun W.J., Murdoch D.J. A First Course in Statistical Programming with R. Cambridge University Press, 2007.

Bretz F., Hothorn T., Westfall P. Multiple Comparisons Using R. Chapman, Hall/CRC, 2011. 194 p.

Broman K.W., Sen S. A Guide to QTL Mapping with R/qtl. Springer, 2009. 412 p. Broström G. Event History Analysis with R. CRC Press, 2012. 234 p.

Burns P. The R Inferno. 2011. 126 p. (PDF на сайте CRAN).

Buuren S. van. Flexible Imputation of Missing Data. Chapman, Hall/CRC, 2012. 342 p.

Cano E.L., Moguerza J.M., Redchuk A. Six Sigma with R: Statistical Engineering for Process Improvement. Springer, 2012. 296 p.

Carmona R. Statistical Analysis of Financial Data in R. 2nd ed. Springer, 2014. 595 p. Chambers J.M. Programming with Data: A Guide to the S Language. Springer, 1998. 486 p. Chambers J. M. Software for Data Analysis: Programming with R. Springer, 2008. Chambers J. M., Hastie T.J. Statistical models in S. Chapman, Hall/CRC, 1991. 608 p.

Chan N.H. Time Series: Applications to Finance with R and S-Plus. 2nd ed. Wiley, 2010. 316 p.

391

Chang M. Adaptive design theory and implementation using SAS and R. Chapman, Hall/CRC, 2008. 441 p.

Chang S.S. Exploring Everyday Things with R and Ruby: Learning About Everyday Things. O’Reilly Media, 2012.

Chang W.R. Graphics Cookbook. O’Reilly Media, 2012. 413 p.

Chatterjee S., Simono J.S. Handbook of Regression Analysis. Wiley, 2013. 237 p. Chen D.-G., Peace K.E. Clinical Trial Data Analysis Using R. CRC Press, 2011. 381 p. Chen D.-G., Peace K.E. Applied Meta-Analysis with R. Chapman, Hall/CRC, 2013.

Cheung Y.K. Dose Finding by the Continual Reassessment Method. Chapman, Hall/CRC, 2011. 200 p.

Chihara L.M., Hesterberg T.C. Mathematical Statistics with Resampling and R. Wiley, 2011. 434 p.

Clark M. Introduction to R: A First Course in R. Center for Social Research University of Notre Dame, 2013. 29 p.

Claude J. Morphometrics with R. Springer, 2008. 316 p.

Coghlan A. A Little Book of R for Biomedical Statistics. University College Cork, 2013. 31 p. Coghlan A. A Little Book of R for Multivariate Analysis. University College Cork, 2013. 47 p. Coghlan A. A Little Book of R for Time Series. University College Cork, 2014. 71 p.

Cohen Y., Cohen J.Y. Statistics and Data with R: An Applied Approach Through Examples. Wiley, 2008. 603 p.

Computational Actuarial Science with R / ed. by A. Charpentier. Chapman, Hall/CRC, 2014. 604 p.

Cook D., Swayne D.F. Interactive and Dynamic Graphics for Data Analysis: With R and Ggobi. Springer, 2007. 188 p.

Cotton R. Learning R. O’Reilly Media, 2013. 400 p.

Cowles M.K. Applied Bayesian Statistics: With R and OpenBUGS Examples. Springer, 2013. 240 p.

Cowpertwait P.S., Metcalfe A. Introductory Time Series with R. Springer, 2009. 262 p. Crawley M.J. Statistics: An Introduction Using R. 2nd ed. Wiley, 2014. 336 p.

Crowder M.J. Multivariate Survival Analysis and Competing Risks. Chapman, Hall/CRC, 2012. 396 p.

Cryer J.D., Chan K.-S. Time Series Analysis: With Applications in R. 2nd ed. Springer, 2008. 491 p.

Curran J.M. Introduction to Data Analysis with R for Forensic Scientists. CRC Press, 2010. 317 p.

Danneman N., Heimann R. Social Media Mining with R: Deploy cutting-edge sentiment analysis techniques to real-world social media data using R. Packt Publishing, 2014.

Demidenko E. Mixed Models: Theory and Applications with R. 2nd ed. Wiley, 2013. 754 p. (Wiley Series in Probability and Statistics).

Deshmukh S. Multiple Decrement Models in Insurance: An Introduction Using R. Springer, 2012. 220 p.

Dobrow R.P. Probability: With Applications and R. Wiley, 2014. 520 p.

Douc R., Moulines E., Sto e D. Nonlinear Time Series: Theory, Methods and Applications with R Examples. Chapman, Hall/CRC, 2014. 551 p.

Dudoit S., Laan M.J. van der. Multiple Testing Procedures with Applications to Genomics. Springer, 2008. 611 p.

Eddelbuettel D. Seamless R and C++ Integration with Rcp. Springer, 2013. 220 p. Ekstrom C.T. The R Primer. Chapman, Hall/CRC, 2011. 299 p.

Ergül Ö. Guide to Programming and Algorithms Using R. Springer, 2013. 200 p.

Everitt B.S. An R and S-Plus® Companion to Multivariate Analysis. 2nd ed. Springer, 2007. 221 p.

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Everitt B.S., Hothorn T. An Introduction to Applied Multivariate Analysis with R. Springer, 2011. 283 p.

Everitt B.S., Rabe-Hesketh S. Analyzing Medical Data Using S-PLUS. Springer, 2001. 487 p. Falissard F. Analysis of Questionnaire Data with R. Chapman, Hall/CRC, 2011.

Faraway J.J. Linear Models with R. Chapman, Hall/CRC, 2004. 240 p.

Feigelson E.D., Babu G.J. Modern Statistical Methods for Astronomy: With R Applications. Cambridge University Press, 2012. 490 p.

Finch W.H., Bolin J.E., Kelley K. Multilevel Modeling Using R. CRC Press, 2014. 226 p. Foulkes A.S. Applied Statistical Genetics with R: For Population-based Association Studies.

Springer, 2009. 252 p.

Fox J.-P. An R and S-Plus® Companion to Applied Regression. SAGE Publications, Inc, 2002. 330 p. (Приложения и примеры доступны на Web Appendix)

Fox J.-P. Bayesian Item Response Modeling: Theory and Applications. Springer, 2010. 326 p. Frery A.C., Perciano T. Introduction to Image Processing Using R: Learning by Examples.

Springer, 2013. 95 p.

Gaetan C., Guyon X. Spatial Statistics and Modeling. Springer, 2010. 308 p.

Galecki A., Burzykowski T. Linear Mixed-E ects Models Using R: A Step-by-Step Approach. Springer, 2013. 625 p.

Galwey N.W. Introduction to Mixed Modelling: Beyond Regression and Analysis of Variance. Wiley, 2006. 377 p.

Gardener M. Beginning R: The Statistical Programming Language. Wrox, 2012. 504 p. Gardener M. The Essential R Reference. Wiley, 2013. 578 p.

Gelman A., Hill J. Data Analysis Using Regression and Multilevel/Hierarchical Models. Cambridge University Press, 2006. 651 p.

Gentleman R. R Programming for Bioinformatics. Chapman, Hall/CRC, 2008. 328 p.

Gilli M., Maringer D., Schumann E. Numerical Methods and Optimization in Finance. Academic Press, 2011. 584 p.

Good P.I. Introduction to Statistics Through Resampling Methods and R. 2nd ed. Wiley, 2013. 217 p.

Greenacre M. Correspondence Analysis in Practice. 2nd ed. Chapman, Hall/CRC, 2007. 296 p. Gries S. T. Quantitative Corpus Linguistics with R: A Practical Introduction. Routledge, 2009.

256 p.

Gries S. T. Statistics for Linguistics with R: A Practical Introduction. 2nd ed. De Gruyter Mouton, 2013. 375 p.

Hay-Jahans C. An R Companion to Linear Statistical Models. Chapman, Hall/CRC, 2011. 372 p.

Heiberger R.M., Holland B. Statistical Analysis and Data Display: An Intermediate Course with Examples in S-Plus, R, and SAS. Springer, 2004. 730 p.

Heiberger R.M., Neuwirth E. R Through Excel: A Spreadsheet Interface for Statistics, Data Analysis, and Graphics. Springer, 2009. 344 p.

Helsel D.R. Statistics for Censored Environmental Data Using Minitab and R. 2nd ed. Wiley, 2012. 344 p.

Henry M., Stevens H. A Primer of Ecology with R. Springer, 2009. 388 p.

Hettmansperger T.P., McKean J.W. Robust Nonparametric Statistical Methods and R. 2nd ed. CRC Press, 2011. 553 p.

Hilbe J.M., Robinson A.P. Methods of Statistical Model Estimation. Chapman, Hall/CRC, 2013. 243 p.

Ho P.D. A First Course in Bayesian Statistical Methods. Springer, 2009. 270 p. Højsgaard S., Edwards D., Lauritzen S. Graphical Models with R. Springer, 2012. 182 p.

Hollander M., Wolfe D.A., Chicken E. Nonparametric Statistical Methods. 3rd ed. Wiley, 2014. 848 p.

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Horton N.J., Kleinman K. Using R for Data Management, Statistical Analysis, and Graphics. CRC Press, 2010. 296 p.

Husson F., Le S., Pages J. Exploratory Multivariate Analysis by Example Using R. CRC Press, 2010. 240 p.

Hyndman R.J., Athanasopoulos G. Forecasting: principles and practice. Otexts, 2014. 292 p.

(официальная бесплатная версия книги доступна по https://www.otexts.org/fpp).

Iacus S.M. Simulation and Inference for Stochastic Di erential Equations: With R Examples. Springer, 2008. 286 p.

Iacus S.M. Option Pricing and Estimation of Financial Models with R. Wiley, 2011. 472 p. Implementing Reproducible Research / ed. by V. Stodden, F. Leisch, R. D. Peng. Chapman,

Hall/CRC, 2014. 448 p.

Interval-Censored Time-to-Event Data: Methods and Applications / ed. by D.-G. Chen, J. Sun, K. E. Peace. Chapman, Hall/CRC, 2012. 426 p.

Introduction to R for Quantitative Finance / G. Daróczi [et al.]. Packt Publishing, 2013. 164 p. James G., Witten D., Hastie T., Tibshirani R. An Introduction to Statistical Learning: with

Applications in R. Springer, 2013. 426 p. PDF Janacek G. Unlock the Secrets of R. 2007. 72 p.

Janert P.K. Data Analysis with Open Source Tools. O’Reilly Media, 2010. 540 p. Jank W. Business Analytics for Managers. Springer, 2011. 189 p.

Jockers M.L. Text Analysis with R for Students of Literature. Springer, 2014. 199 p. Johnson P.E. R tips. Revival 2014! 2014. 72 p.

Jones O., Maillardet R., Robinson A.P. Introduction to Scienti c Programming and Simulation using R. Chapman, Hall/CRC, 2009. 472 p.

Jonge E. de, Loo M. van der. An introduction to data cleaning with R. Statistics Netherlands, 2013. 53 p. (PDF на сайте CRAN).

Jureckova J., Picek J. Robust Statistical Methods with R. Chapman, Hall/CRC, 2005. 216 p. Kaltenbach H.-M. A Concise Guide to Statistics. Springer, 2011. 111 p.

Karian Z.A., Dudewicz E.J. Handbook of Fitting Statistical Distributions with R. Chapman, Hall/CRC, 2011.

Kateri M. Contingency Table Analysis: Methods and Implementation Using R. Springer, 2014. 315 p.

Keele L. Semiparametric Regression for the Social Sciences. Wiley, 2008. 230 p.

Kenett R., Salini S. Modern Analysis of Customer Surveys: with Applications using R. Wiley, 2012. 524 p.

Kenett R., Zacks S. Modern Industrial Statistics: with applications in R, MINITAB and JMP. 2nd ed. Wiley, 2014. 586 p.

Kerns G.J. Introduction to Probability and Statistics Using R. G. Jay Kerns, 2010. 396 p. Kleiber C., Zeileis A. Applied Econometrics with R. Springer, 2008. 222 p.

Kleinman K., Horton N.J. SAS and R: Data Management, Statistical Analysis and Graphics. 2nd ed. Chapman, Hall/CRC, 2014. 425 p.

Klemelä J. Smoothing of Multivariate Data: Density Estimation and Visualization. Wiley, 2009. 603 p.

Klemelä J. Multivariate Nonparametric Regression and Visualization: With R and Applications to Finance. Wiley, 2014. 396 p.

Knoblauch K., Maloney L.T. Modeling Psychophysical Data in R. Springer, 2012. 367 p. Kolaczyk E.D., Csárdi G. Statistical Analysis of Network Data with R. Springer, 2014. 207 p. Krause A., Olson M. The Basics of S-PLUS. 4th ed. Springer, 2005. 455 p.

Krijnen W. Applied Statistics for Bioinformatics Using R. Hanze University, 2009. 278 p. (PDF

на сайте CRAN).

Kruschke J.K. Doing Bayesian Data Analysis: A Tutorial Introduction with R and BUGS. Academic Press, 2010. 542 p.

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Kuhn M., Johnson K. Applied Predictive Modeling. Springer, 2013. 600 p.

Kuhnert P.M., Venables W.N. An Introduction to R: Software for Statistical Modelling & Computing. CSIRO Mathematical, Information Sciences, Australia, 2005. 364 p. (PDF c

исходными данными и скриптами на сайте CRAN).

Lamigueiro O.P. Displaying Time Series, Spatial, and Space-Time Data with R. Chapman, Hall/CRC, 2014. 206 p.

Lantz B. Machine Learning with R. Packt Publishing, 2013. 396 p. Lavine M.L. Introduction to Statistical Thought. 2013. 463 p. PDF

Lawrence M.F., Verzani J. Programming Graphical User Interfaces in R. Chapman, Hall/CRC, 2012. 479 p.

Ledolter J. Data Mining and Business Analytics with R. Wiley, 2013. Leipzig J., Li X. Data Mashups in R. O’Reilly Media, 2011. 40 p.

Li Y., Baron J. Behavioral Research Data Analysis with R. Springer, 2012. 245 p.

Lindsey J. K. Statistical Analysis of Stochastic Processes in Time. Cambridge University Press, 2004. 354 p.

Link W.A., Barker R.J. Bayesian Inference: with ecological applications. Elsevier/Academic , Boston, MA. 2010. 339 p.

Loo M.P. van der, Jonge E. de. Learning RStudio for R Statistical Computing. Packt Publishing, 2012. 126 p.

Lovelace R., Cheshire J. Introduction to visualising spatial data in R. 2014. 24 p. (PDF на сайте CRAN).

Lumley T. Complex Surveys: A Guide to Analysis Using R. Wiley, 2010. 297 p.

Lumley T. R Fundamentals and Programming Techniques. R Core Development Team. 2006. 225 p. (PDF на сайте автора).

MacFarland T. W. Two-Way Analysis of Variance: Statistical Tests and Graphics Using R. Springer, 2012. 150 p.

MacFarland T.W. Introduction to Data Analysis and Graphical Presentation in Biostatistics with R: Statistics in the Large. Springer, 2014. 167 p.

Mahajan S. Street-Fighting Mathematics: The Art of Educated Guessing and Opportunistic Problem Solving. The MIT Press, 2010. 152 p.

Maindonald J.H. Using R for Data Analysis and Graphics. Introduction, Code and Commentary. Australian National University, 2008. 88 p. (PDF, данные и скрипты доступны на сайте автора ).

Marin J.-M., Robert C. Bayesian Essentials with R. 2nd ed. Springer, 2014. 312 p.

Marques de Sá J. Applied Statistics Using SPSS, STATISTICA, MATLAB and R. Springer, 2007. 520 p.

Martinussen T., Scheike T.H. Dynamic Regression Models for Survival Data. Springer, 2006. 470 p.

Mathur S.K. Statistical Bioinformatics with R. Academic Press, 2010. 336 p.

Matlo N. The Art of R Programming: A Tour of Statistical Software Design. No Starch Press, 2011. 400 p.

Matthiopoulos J. How to be a Quantitative Ecologist: The ’A to R’ of Green Mathematics and Statistics. Wiley, 2011. 490 p.

McCallum Q.E., Weston S. Parallel R. O’Reilly Media, 2011. 126 p.

McCarthy M.A. Bayesian Methods for Ecology. Cambridge University Press, 2007. 312 p. Micheaux P.L. de, Drouilhet R., Liquet B. The R Software: Fundamentals of Programming and

Statistical Analysis. Springer, 2013. 628 p.

Millard S.P. EnvStats: An R Package for Environmental Statistics. 2nd ed. Springer, 2013. 305 p.

Millard S.P., Neerchal N.K., Dixon P. Environmental Statistics with S-PLUS. CRC Press, 2000. 848 p.

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Mirman D. Growth Curve Analysis and Visualization Using R. Chapman, Hall/CRC, 2014. 188 p.

Mittal H.V. R Graphs Cookbook. Packt Publishing, 2011. 272 p.

Modeling Dose-Response Microarray Data in Early Drug Development Experiments Using R: Order-Restricted Analysis of Microarray Data / ed. by D. Lin [et al.]. Springer, 2012. 282 p.

Modern Actuarial Risk Theory: Using R / R. Kaas [et al.]. 2nd ed. Springer, 2008. 394 p. Muenchen R.A. R for SAS and SPSS Users. 2nd ed. Springer, 2011. 715 p.

Muenchen R.A., Hilbe J.M. R for Stata Users. Springer, 2010. 542 p.

Multiple testing problems in pharmaceutical statistics / ed. by A. Dmitrienko [et al.]. Chapman & Hall/CRC, 2010. 301 p.

Murrell P. R Graphics. Chapman, Hall/CRC, 2005. 322 p.

Murtagh F. Correspondence Analysis and Data Coding with Java and R. Chapman, Hall/CRC, 2005. 256 p.

Myers W.L., Patil G.P. Multivariate Methods of Representing Relations in R for Prioritization Purposes. Springer, 2012. 304 p.

Nagarajan R., Scutari M., Lebre S. Bayesian Networks in R: with Applications in Systems Biology. Springer, 2013. 168 p.

Nason G.P. Wavelet Methods in Statistics with R. Springer, 2008. 268 p. Navarro D.J. Learning Statistics with R. lulu.com, 2014. 562 p.

Nolan D., Temple-Lang D. XML and Web Technologies for Data Sciences with R. Springer, 2014. 677 p.

Nonparametric Hypothesis Testing: Rank and Permutation Methods with Applications in R / S. Bonnini [et al.]. Wiley, 2014. 240 p.

O’Gorman T.W. Adaptive Tests of Signi cance Using Permutations of Residuals with R and SAS. Wiley, 2012. 364 p.

Ohri A. R for Business Analytics. Springer, 2013. 319 p.

Oja H. Multivariate Nonparametric Methods with R: An approach based on spatial signs and ranks. Springer, 2010. 244 p.

Optimal Experimental Design with R / D. Rasch [et al.]. Chapman, Hall/CRC, 2011. 345 p. Owen J. The R Guide. University of Richmond, 2010. 61 p. (PDF на сайте CRAN).

Pace L. Beginning R: An Introduction to Statistical Programming. Apress, 2012. 336 p. Paradis E. R for Beginners. Universite Montpellier, 2005. 76 p. (PDF на сайте CRAN). Paradis E. Analysis of Phylogenetics and Evolution with R. 2nd ed. Springer, 2012. 404 p.

Peng R.D., Dominici F. Statistical Methods for Environmental Epidemiology with R: A Case Study in Air Pollution and Health. Springer, 2008. 154 p.

Petris G., Petrone S., Campagnoli P. Dynamic Linear Models with R. Springer, 2009. 252 p. Pfa B. Analysis of Integrated and Cointegrated Time Series with R. 2nd ed. Springer, 2008.

190 p.

Pfa B. Financial Risk Modelling and Portfolio Optimization with R. Wiley, 2013. 374 p. Pinheiro J.C., Bates D.M. Mixed-E ects Models in S and S-PLUS. Springer, 2000. 530 p. Plant R.E. Spatial Data Analysis in Ecology and Agriculture Using R. CRC Press, 2012. 637 p. Prajapati V. Big Data Analytics with R and Hadoop. Packt Publishing, 2013. 238 p.

Pruscha H. Statistical Analysis of Climate Series: Analyzing, Plotting, Modeling, and Predicting with R. Springer, 2013. 200 p.

Quick J.M. Statistical Analysis with R Beginner’s Guide. Packt Publishing, 2010. 300 p. Ramsay J. , Hooker G., Graves S. Functional Data Analysis with R and MATLAB. Springer,

2009. 213 p.

Rayner J.C., Thas O., Best D.J. Smooth Tests of Goodness of Fit: Using R. 2nd ed. Wiley, 2009. 290 p.

Ritz C., Streibig J.C., [et al.] Nonlinear Regression with R. Springer, 2008. 148 p.

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