@. In and R. , 13, the str_match function extracts capture groups using R code. So it usually runs much slower than the C implementation we propose in the next section

. Akaike, Information theory as an extension of the maximum likelihood principle, Second International Symposium on Information Theory, pp.267-281, 1973.

S. Arlot, V-fold cross-validation improved: V-fold penalization. Arxiv preprint, 2008.
URL : https://hal.archives-ouvertes.fr/hal-00239182

S. Arlot and A. Celisse, A survey of cross-validation procedures for model selection, Statistics Surveys, vol.4, issue.0, pp.40-79, 2010.
DOI : 10.1214/09-SS054

URL : https://hal.archives-ouvertes.fr/hal-00407906

S. Arlot and P. Massart, Data-driven calibration of penalties for leastsquares regression, J. Mach. Learn. Res, vol.10, pp.245-279, 2009.
URL : https://hal.archives-ouvertes.fr/inria-00287631

F. Bach and Z. Harchoui, DIFFRAC: a discriminative and flexible framework for clustering, Adv. NIPS, 2008.

F. Bach, R. Jenatton, J. Mairal, and G. Obozinski, Optimization with Sparsity-Inducing Penalties, Machine Learning, pp.1-106, 2012.
DOI : 10.1561/2200000015

URL : https://hal.archives-ouvertes.fr/hal-00613125

Y. Baraud, C. Giraud, and S. Huet, Gaussian model selection with an unknown variance, The Annals of Statistics, vol.37, issue.2, pp.630-672, 2009.
DOI : 10.1214/07-AOS573

URL : https://hal.archives-ouvertes.fr/hal-00756074

A. Beck and M. Teboulle, A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems, SIAM Journal on Imaging Sciences, vol.2, issue.1, pp.183-202, 2009.
DOI : 10.1137/080716542

E. Ben-yaacov and Y. C. Eldar, A fast and flexible method for the segmentation of aCGH data, Bioinformatics, vol.24, issue.16, pp.139-145, 2008.
DOI : 10.1093/bioinformatics/btn272

H. Bengtsson, The R.oo package -object-oriented programming with references using standard R code, Proceedings of the 3rd International Workshop on Distributed Statistical Computing, 2003.

H. Bengtsson, Aroma project developers' corner, 2010. URL http

L. Birgé and P. Massart, Minimal penalties for gaussian model selection. Probability Th. and Related Fields, pp.33-73, 2007.

M. Bostock, V. Oglevetsky, and J. Heer, D³ Data-Driven Documents, IEEE Transactions on Visualization and Computer Graphics, vol.17, issue.12, pp.2301-2309, 2011.
DOI : 10.1109/TVCG.2011.185

S. Boyd and L. Vandenberghe, Convex Optimization, The Edinburgh Building, 2004.

E. J. Candès and T. Tao, The Power of Convex Relaxation: Near-Optimal Matrix Completion, IEEE Transactions on Information Theory, vol.56, issue.5, pp.2053-2080, 2009.
DOI : 10.1109/TIT.2010.2044061

X. Chen, S. Kim, Q. Lin, J. G. Carbonell, and E. P. Xing, Graph-structured multi-task regression and an efficient optimization method for general fused lasso, 2010.

T. H. Cormen, C. E. Leiserson, R. L. Rivest, and C. Stein, Introduction to algorithms, 1990.

T. H. Cormen, C. E. Leiserson, R. L. Rivest, and C. Stein, Introduction to Algorithms, chapter 26, 2001.

P. Danenberg, Roxygen vignette, 2009.

R. Development and C. Team, R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing

B. Efron, T. Hastie, I. Johnstone, and R. Tibshirani, Least angle regression, Annals of statistics, vol.32, issue.2, pp.40-99, 2004.

D. Rigby, A. Rigler, C. Valsesia, S. J. Langford, S. W. Humphray et al., Accurate and reliable highthroughput detection of copy number variation in the human genome

A. Fischer, On the number of groups in clustering, Statistics & Probability Letters, vol.81, issue.12, pp.1771-1781, 2011.
DOI : 10.1016/j.spl.2011.07.005

M. Frank and P. Wolfe, An algorithm for quadratic programming, Naval Research Logistics Quarterly, vol.3, issue.1-2, pp.95-110, 1956.
DOI : 10.1002/nav.3800030109

J. E. Friedl, Mastering Regular Expressions. O'Reilly, 1005 Gravenstein Highway North, 2006.

J. Friedman, T. Hastie, H. Hoefling, and R. Tibshirani, Pathwise coordinate optimization, The Annals of Applied Statistics, vol.1, issue.2, pp.30-32, 2007.
DOI : 10.1214/07-AOAS131

URL : http://arxiv.org/abs/0708.1485

S. Fujishige, T. Hayashi, and S. Isotani, The minimum-norm-point algorithm applied to submodular function minimization and linear programming, 2006.

R. C. Gentleman, V. J. Carey, and D. M. Bates, Bioconductor: Open software development for computational biology and bioinformatics, Genome Biology, vol.510, issue.5, pp.80-80, 2004.

D. Goldfarb and A. Idnani, A numerically stable dual method for solving strictly convex quadratic programs, Mathematical Programming, pp.1-33, 1983.
DOI : 10.1007/BF02591962

M. Grant, S. Boyd, and Y. Ye, Global Optimization: From Theory to Implementation , chapter Disciplined convex programming, 2006.

P. Hall, J. W. Kay, and D. Titterinton, Asymptotically optimal difference-based estimation of variance in nonparametric regression, Biometrika, vol.77, issue.3, pp.521-528, 1990.
DOI : 10.1093/biomet/77.3.521

T. Hastie, R. Tibshirani, and J. Friedman, The elements of statistical learning, 2009.

P. Hazel, PCRE -Perl-compatible regular expressions (man page), 2012

T. D. Hocking, A. Joulin, F. Bach, and J. Vert, Clusterpath: an algorithm for clustering using convex fusion penalties, Proceedings of the 28th International Conference on Machine Learning (ICML-11), ICML '11, pp.745-752, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00591630

T. D. Hocking, G. Schleiermacher, I. Janoueix-lerosey, O. Delattre, F. Bach et al., Learning smoothing models using breakpoint annotations, 2012.
URL : https://hal.archives-ouvertes.fr/hal-00663790

T. D. Hocking, T. Wutzler, K. Ponting, and P. Grosjean, Sustainable, Extensible Documentation Generation using inlinedocs, Journal of Statistical Software

H. Hoefling, A Path Algorithm for the Fused Lasso Signal Approximator, Journal of Computational and Graphical Statistics, vol.19, issue.4, 2009.
DOI : 10.1198/jcgs.2010.09208

L. Hubert and P. Arabie, Comparing partitions, Journal of Classification, vol.78, issue.1, pp.193-218, 1985.
DOI : 10.1007/BF01908075

P. Hupé, N. Stransky, J. Thiery, F. Radvanyi, and E. Barillot, Analysis of array CGH data: from signal ratio to gain and loss of DNA regions, Bioinformatics, vol.20, issue.18, pp.3413-3422, 2004.
DOI : 10.1093/bioinformatics/bth418

I. Janoueix-lerosey, D. Lequin, L. Brugì-eres, A. Ribeiro, L. De-pontual et al., Somatic and germline activating mutations of the ALK kinase receptor in neuroblastoma, Nature, vol.40, issue.7215, pp.967-970, 2008.
DOI : 10.1038/nature07398

I. Janoueix-lerosey, G. Schleiermacher, E. Michels, V. Mosseri, A. Ribeiro et al., Overall Genomic Pattern Is a Predictor of Outcome in Neuroblastoma, Journal of Clinical Oncology, vol.27, issue.7, pp.1026-1033, 1026.
DOI : 10.1200/JCO.2008.16.0630

URL : https://hal.archives-ouvertes.fr/inserm-00369944

. Sabatini, Scoring diverse cellular morphologies in image-based screens with iterative feedback and machine learning, Proceedings of the National Academy of Sciences, pp.1826-1831, 2009.

W. Kent, C. Sugnet, T. Furey, K. Roskin, T. Pringle et al., The Human Genome Browser at UCSC, Genome Research, vol.12, issue.6, pp.996-1006, 2002.
DOI : 10.1101/gr.229102

B. W. Kernighan and D. M. Ritchie, The C Programming Language, 1988.

R. Killick, P. Fearnhead, and I. A. Eckley, Optimal Detection of Changepoints With a Linear Computational Cost, Journal of the American Statistical Association, vol.63, issue.500, 2011.
DOI : 10.1080/01621459.2012.737745

A. Krause and C. Guestrin, Beyond convexity: Submodularity in machine learning, IJCAI, 2009.

P. , L. Rosa, E. Viara, P. Hupé, G. Pierron et al., VAMP: Visualization and analysis of array-CGH, transcriptome and other molecular profiles, Bioinformatics, vol.222217, issue.17, pp.2066-2073, 2006.
URL : https://hal.archives-ouvertes.fr/hal-01199867

L. L. Lamport, A Document Preparation System, 1986.

M. Lavielle, Using penalized contrasts for the change-point problem, Signal Processing, vol.85, issue.8, pp.1501-1510, 2005.
DOI : 10.1016/j.sigpro.2005.01.012

URL : https://hal.archives-ouvertes.fr/inria-00070662

E. Lebarbier, Detecting multiple change-points in the mean of Gaussian process by model selection, Signal Processing, vol.85, issue.4, pp.717-736, 2005.
DOI : 10.1016/j.sigpro.2004.11.012

URL : https://hal.archives-ouvertes.fr/inria-00071847

C. Lee, Estimating the number of change points in a sequence of independent normal random variables, Statistics & Probability Letters, vol.25, issue.3, pp.241-249, 1995.
DOI : 10.1016/0167-7152(94)00227-Y

F. Leisch, Sweave, part II: Package vignettes. R News, pp.21-24, 2003.

F. Lindsten, H. Ohlsson, and L. Ljung, Clustering using sum-of-norms regularization: With application to particle filter output computation, 2011 IEEE Statistical Signal Processing Workshop (SSP), 2011.
DOI : 10.1109/SSP.2011.5967659

J. M. Maris, Recent Advances in Neuroblastoma, New England Journal of Medicine, vol.362, issue.23, pp.2202-2211, 2010.
DOI : 10.1056/NEJMra0804577

J. Mattingley and S. Boyd, CVXMOD: Convex optimization software in Python (web page and software), 2008.

D. Murdoch, Parsing Rd files, 2009.

D. Murdoch and S. Urbanek, The New R Help System, The R Journal, vol.1, issue.2 2, pp.60-65, 2009.

P. Murrell, Introduction to Data Technologies. CRC computer science and data analysis series, Broken Sound Parkway NW Boca Raton, vol.6000, issue.300, pp.33487-2742, 2009.

P. Murrell and . Graphics, The R Series, Broken Sound Parkway NW Boca Raton, vol.6000, issue.300, pp.33487-2742, 2011.

M. T. Nakao, A. Neumaier, S. M. Rump, S. P. Shary, and P. Van-hentenryck, Standardized notation in interval analysis, 2010.

P. Neuvial, H. Bengtsson, and T. P. Speed, Statistical Analysis of Single Nucleotide Polymorphism Microarrays in Cancer Studies, 2010.
DOI : 10.1007/978-3-642-16345-6_11

URL : https://hal.archives-ouvertes.fr/hal-00497273

A. Y. Ng, M. I. Jordan, and Y. Weiss, On spectral clustering: Analysis and an algorithm, Adv. NIPS, 2001.

K. Pelckmans, J. De-brabanter, and J. Suykens, Convex clustering shrinkage, Statistics and Optimization of Clustering Workshop (PASCAL), 2005.

F. Picard, S. Robin, M. Lavielle, C. Vaisse, and J. Daudin, A statistical approach for array CGH data analysis, BMC Bioinformatics, vol.6, issue.27, 2005.
URL : https://hal.archives-ouvertes.fr/hal-00427846

D. Pinkel, R. Segraves, D. Sudar, S. Clark, I. Poole et al., High resolution analysis of DNA copy number variation using comparative genomic hybridization to microarrays, Nature Genetics, vol.20, issue.2, pp.207-211, 1998.
DOI : 10.1038/2524

R. Pique-regi, J. Monso-varona, A. Ortega, R. C. Seeger, T. J. Triche et al., Sparse representation and Bayesian detection of genome copy number alterations from microarray data, Bioinformatics, vol.24, issue.3, pp.309-318, 2008.
DOI : 10.1093/bioinformatics/btm601

A. Ritz, P. Paris, M. Ittmann, C. Collins, and B. Raphael, Detection of recurrent rearrangement breakpoints from copy number data, BMC Bioinformatics, vol.12, issue.1, pp.1141471-2105, 2011.
DOI : 10.1093/bioinformatics/btn335

S. Rosset and J. Zhu, Piecewise linear regularized solution paths, The Annals of Statistics, vol.35, issue.3, pp.1012-1030, 2007.
DOI : 10.1214/009053606000001370

URL : http://arxiv.org/abs/0708.2197

A. J. Rossini, R. M. Heiberger, R. A. Sparapani, M. Maechler, and K. Hornik, Emacs Speaks Statistics: A Multiplatform, Multipackage Development Environment for Statistical Analysis, Journal of Computational and Graphical Statistics, vol.13, issue.1, pp.247-261, 2004.
DOI : 10.1198/1061860042985

B. C. Russell, A. Torralba, K. P. Murphy, and W. T. Freeman, LabelMe: A Database and Web-Based Tool for Image Annotation, International Journal of Computer Vision, vol.3, issue.1, pp.1-3157, 2008.
DOI : 10.1007/s11263-007-0090-8

D. Sarkar, Lattice: Multivariate Data Visualization with R URL http://lmdvr.r-forge.r-project.org, 2008.
DOI : 10.1007/978-0-387-75969-2

G. Schleiermacher, I. Janoueix-lerosey, A. Ribeiro, J. Klijanienko, J. Couturier et al., Accumulation of Segmental Alterations Determines Progression in Neuroblastoma, Journal of Clinical Oncology, vol.28, issue.19, pp.3122-31303122, 2010.
DOI : 10.1200/JCO.2009.26.7955

M. Schwab, H. Varmus, J. Bishop, K. Grzeschik, S. Naylor et al., Chromosome localization in normal human cells and neuroblastomas of a gene related to c-myc, Nature, vol.35, issue.5956, pp.288-291, 1984.
DOI : 10.1038/308288a0

G. Schwarz, Estimating the Dimension of a Model, The Annals of Statistics, vol.6, issue.2, pp.461-464, 1978.
DOI : 10.1214/aos/1176344136

S. P. Shah, X. Xuan, R. J. Deleeuw, M. Khojasteh, W. L. Lam et al., Integrating copy number polymorphisms into array CGH analysis using a robust HMM, Bioinformatics, vol.22, issue.14, pp.22431-439, 2006.
DOI : 10.1093/bioinformatics/btl238

X. Shen and H. Huang, Grouping Pursuit Through a Regularization Solution Surface, Journal of the American Statistical Association, vol.105, issue.490, pp.727-739, 2010.
DOI : 10.1198/jasa.2010.tm09380

B. Stroustrup, The C++ Programming Language, 1997.

S. Theußl and A. Zeileis, Collaborative Software Development Using R-Forge. The R Journal, pp.9-14, 2009.

R. Tibshirani, Regression Shrinkage and Selection Via the Lasso, J. R. Statist. Soc. B, vol.58, issue.1, pp.267-288, 1996.

R. Tibshirani and M. Saunders, Sparsity and smoothness via the fused lasso, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.99, issue.1, pp.9-17, 2005.
DOI : 10.1016/S0140-6736(02)07746-2

R. Tibshirani and P. Wang, Spatial smoothing and hot spot detection for CGH data using the fused lasso, Biostatistics, vol.9, issue.1, 2007.
DOI : 10.1093/biostatistics/kxm013

R. Tibshirani, G. Walther, and T. Hastie, Estimating the number of clusters in a data set via the gap statistic, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.63, issue.2, pp.41-64, 2001.
DOI : 10.1111/1467-9868.00293

D. Trochet, F. Bourdeaut, I. Janoueix-lerosey, A. Deville, L. De-pontual et al., Germline Mutations of the Paired???Like Homeobox 2B (PHOX2B) Gene in Neuroblastoma, The American Journal of Human Genetics, vol.74, issue.4, pp.761-764, 2004.
DOI : 10.1086/383253

E. Tufte, The Visual Display of Quantitative Information. Graphics Press LLC, Post office box 430, 2001.

E. Tufte, Beautiful Evidence. Graphics Press LLC, Post office box 430, 2006.

B. A. Turlach and A. Weingessel, quadprog: Functions to solve Quadratic Programming Problems, 2011.

V. Vapnik, S. Golowich, and A. J. Smola, Support vector method for function approximation, regression estimation, and signal processing

E. S. Venkatraman and A. B. Olshen, A faster circular binary segmentation algorithm for the analysis of array CGH data, Bioinformatics, vol.23, issue.6, pp.657-663
DOI : 10.1093/bioinformatics/btl646

J. Vert and K. Bleakley, Fast detection of multiple change-points shared by many signals using group LARS, Advances in Neural Information Processing Systems 23 (NIPS), pp.2343-2351, 2010.

R. A. Weinberg, The Biology of Cancer, Garland Science, 2006.

H. Wickham, ggplot2: elegant graphics for data analysis, 2009.

H. Wickham, stringr: modern, consistent string processing, Journal, vol.2, issue.2, pp.38-40, 2010.

. Wikipedia, Comparison of documentation generators, 2012.

. Wikipedia, Automatic label placement, 2012.

H. Willenbrock and J. Fridlyand, A comparison study: applying segmentation to array CGH data for downstream analyses, Bioinformatics, vol.21, issue.22, pp.4084-4091, 2005.
DOI : 10.1093/bioinformatics/bti677

L. Xu, J. Neufeld, B. Larson, and D. Schuurmans, Maximum margin clustering, Adv. NIPS, 2004.

Y. Yao, Estimating the number of change-points via Schwarz' criterion, Statistics & Probability Letters, vol.6, issue.3, pp.181-189, 1988.
DOI : 10.1016/0167-7152(88)90118-6

M. Yuan and Y. Lin, Model selection and estimation in regression with grouped variables, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.58, issue.1, pp.4-7, 2006.
DOI : 10.1198/016214502753479356

N. R. Zhang and D. O. Siegmund, A Modified Bayes Information Criterion with Applications to the Analysis of Comparative Genomic Hybridization Data, Biometrics, vol.6, issue.1, pp.22-32, 2007.
DOI : 10.1111/j.1541-0420.2006.00662.x

Z. Zhang, K. Lange, R. Ophoff, and C. Sabatti, Reconstructing DNA copy number by penalized estimation and imputation, The Annals of Applied Statistics, vol.4, issue.4, pp.1749-1773, 2010.
DOI : 10.1214/10-AOAS357

P. Zhao, G. Rocha, and B. Yu, The composite absolute penalties family for grouped and hierarchical variable selection, The Annals of Statistics, vol.37, issue.6A, pp.3468-3497, 2009.
DOI : 10.1214/07-AOS584