[{"_1":2,"_162":-5,"_163":-5},"loaderData",{"_3":4,"_13":14},"root",{"_5":6,"_7":8,"_9":10,"_11":12},"isProduction",true,"domain","cran-e.com","version","2.10.1","nonce","ODJlMGRhNjAtMDZlZi00MGI4LTk5ZWYtMGNjNmYyNTg5NWRj","routes/_page.author.$authorName",{"_15":16,"_17":18,"_92":93,"_28":161},"authorName","Christos Adam","packages",[19,33,41,50,57,64,71,78,85],{"_20":21,"_30":31},"package",{"_22":23,"_24":25,"_26":27,"_28":29},"id",1047,"name","Compositional","title","Compositional Data Analysis","description","Regression, classification, contour plots, hypothesis testing and fitting of distributions for compositional data are some of the functions included. We further include functions for percentages (or proportions).\nThe standard textbook for such data is John Aitchison's (1986) \"The statistical analysis of compositional data\". Relevant papers include:\na) Tsagris M.T., Preston S. and Wood A.T.A. (2011). \"A data-based power transformation for compositional data\". Fourth International International Workshop on Compositional Data Analysis. <[doi:10.48550/arXiv.1106.1451](https://doi.org/10.48550%2FarXiv.1106.1451)>\nb) Tsagris M. (2014). \"The k-NN algorithm for compositional data: a revised approach with and without zero values present\". Journal of Data Science, 12(3): 519–534. <[doi:10.6339/JDS.201407_12(3).0008](https://doi.org/10.6339%2FJDS.201407_12%283%29.0008)>.\nc) Tsagris M. (2015). \"A novel, divergence based, regression for compositional data\". Proceedings of the 28th Panhellenic Statistics Conference, 15-18 April 2015, Athens, Greece, 430–444. <[doi:10.48550/arXiv.1511.07600](https://doi.org/10.48550%2FarXiv.1511.07600)>.\nd) Tsagris M. (2015). \"Regression analysis with compositional data containing zero values\". Chilean Journal of Statistics, 6(2): 47–57. <[https://soche.cl/chjs/volumes/06/02/Tsagris(2015).pdf](https://soche.cl/chjs/volumes/06/02/Tsagris(2015).pdf)>.\ne) Tsagris M., Preston S. and Wood A.T.A. (2016). \"Improved supervised classification for compositional data using the alpha-transformation\". Journal of Classification, 33(2): 243–261. <[doi:10.1007/s00357-016-9207-5](https://doi.org/10.1007%2Fs00357-016-9207-5)>.\nf) Tsagris M., Preston S. and Wood A.T.A. (2017). \"Nonparametric hypothesis testing for equality of means on the simplex\". Journal of Statistical Computation and Simulation, 87(2): 406–422. <[doi:10.1080/00949655.2016.1216554](https://doi.org/10.1080%2F00949655.2016.1216554)>.\ng) Tsagris M. and Stewart C. (2018). \"A Dirichlet regression model for compositional data with zeros\". Lobachevskii Journal of Mathematics, 39(3): 398–412. <[doi:10.1134/S1995080218030198](https://doi.org/10.1134%2FS1995080218030198)>.\nh) Alenazi A. (2019). \"Regression for compositional data with compositional data as predictor variables with or without zero values\". Journal of Data Science, 17(1): 219–238. <[doi:10.6339/JDS.201901_17(1).0010](https://doi.org/10.6339%2FJDS.201901_17%281%29.0010)>.\ni) Tsagris M. and Stewart C. (2020). \"A folded model for compositional data analysis\". Australian and New Zealand Journal of Statistics, 62(2): 249–277. <[doi:10.1111/anzs.12289](https://doi.org/10.1111%2Fanzs.12289)>.\nj) Alenazi A.A. (2022). \"f-divergence regression models for compositional data\". Pakistan Journal of Statistics and Operation Research, 18(4): 867–882. <[doi:10.18187/pjsor.v18i4.3969](https://doi.org/10.18187%2Fpjsor.v18i4.3969)>.\nk) Tsagris M. and Stewart C. (2022). \"A Review of Flexible Transformations for Modeling Compositional Data\". In Advances and Innovations in Statistics and Data Science, pp. 225–234. <[doi:10.1007/978-3-031-08329-7_10](https://doi.org/10.1007%2F978-3-031-08329-7_10)>.\nl) Alenazi A. (2023). \"A review of compositional data analysis and recent advances\". Communications in Statistics–Theory and Methods, 52(16): 5535–5567. <[doi:10.1080/03610926.2021.2014890](https://doi.org/10.1080%2F03610926.2021.2014890)>.\nm) Tsagris M., Alenazi A. and Stewart C. (2023). \"Flexible non-parametric regression models for compositional response data with zeros\". Statistics and Computing, 33(106). <[doi:10.1007/s11222-023-10277-5](https://doi.org/10.1007%2Fs11222-023-10277-5)>.\nn) Tsagris. M. (2025). \"Constrained least squares simplicial-simplicial regression\". Statistics and Computing, 35(27). <[doi:10.1007/s11222-024-10560-z](https://doi.org/10.1007%2Fs11222-024-10560-z)>.\no) Sevinc V. and Tsagris. M. (2024). \"Energy Based Equality of Distributions Testing for Compositional Data\". <[doi:10.48550/arXiv.2412.05199](https://doi.org/10.48550%2FarXiv.2412.05199)>.","roles",[32],"ctb",{"_20":34,"_30":39},{"_22":35,"_24":36,"_26":37,"_28":38},1448,"Directional","A Collection of Functions for Directional Data Analysis","A collection of functions for directional data (including massive data, with millions of observations) analysis. Hypothesis testing, discriminant and regression analysis, MLE of distributions and more are included. The standard textbook for such data is the \"Directional Statistics\" by Mardia, K. V. and Jupp, P. E. (2000). Other references include a) Phillip J. Paine, Simon P. Preston, Michail Tsagris and Andrew T. A. Wood (2018). \"An elliptically symmetric angular Gaussian distribution\". Statistics and Computing 28(3): 689-697. [doi:10.1007/s11222-017-9756-4](https://doi.org/10.1007%2Fs11222-017-9756-4). b) Tsagris M. and Alenazi A. (2019). \"Comparison of discriminant analysis methods on the sphere\". Communications in Statistics: Case Studies, Data Analysis and Applications 5(4):467–491. [doi:10.1080/23737484.2019.1684854](https://doi.org/10.1080%2F23737484.2019.1684854). c) P. J. Paine, S. P. Preston, M. Tsagris and Andrew T. A. Wood (2020). \"Spherical regression models with general covariates and anisotropic errors\". Statistics and Computing 30(1): 153–165. [doi:10.1007/s11222-019-09872-2](https://doi.org/10.1007%2Fs11222-019-09872-2). d) Tsagris M. and Alenazi A. (2024). \"An investigation of hypothesis testing procedures for circular and spherical mean vectors\". Communications in Statistics-Simulation and Computation, 53(3): 1387–1408. [doi:10.1080/03610918.2022.2045499](https://doi.org/10.1080%2F03610918.2022.2045499). e) Zehao Yu and Xianzheng Huang (2024). A new parameterization for elliptically symmetric angular Gaussian distributions of arbitrary dimension. Electronic Journal of Statistics, 18(1): 301–334. [doi:10.1214/23-EJS2210](https://doi.org/10.1214%2F23-EJS2210). f) Tsagris M. and Alzeley O. (2024). \"Circular and spherical projected Cauchy distributions: A Novel Framework for Circular and Directional Data Modeling\". [doi:10.48550/arXiv.2302.02468](https://doi.org/10.48550%2FarXiv.2302.02468). g) Tsagris M. and Papastamoulis P. (2024). \"Directional data analysis using the spherical Cauchy and the Poisson kernel-based distribution\". [doi:10.48550/arXiv.2409.03292](https://doi.org/10.48550%2FarXiv.2409.03292).",[40],"aut",{"_20":42,"_30":47},{"_22":43,"_24":44,"_26":45,"_28":46},8676,"corbouli","Corbae-Ouliaris Frequency Domain Filtering","Corbae-Ouliaris frequency domain filtering. According to Corbae and Ouliaris (2006) [doi:10.1017/CBO9781139164863.008](https://doi.org/10.1017%2FCBO9781139164863.008), this is a solution for extracting cycles from time series, like business cycles etc. when filtering. This method is valid for both stationary and non-stationary time series.",[40,48,49],"cre","mnt",{"_20":51,"_30":56},{"_22":52,"_24":53,"_26":54,"_28":55},14952,"mvcauchy","Multivariate Cauchy Distribution","The Cauchy distribution is a special case of the t distribution when the degrees of freedom are equal to 1. The functions are related to the multivariate Cauchy distribution and include simulation, computation of the density, maximum likelihood estimation, contour plot of the bivariate Cauchy distribution, and discriminant analysis. References include: Nadarajah S. and Kotz S. (2008). \"Estimation methods for the multivariate t distribution\". Acta Applicandae Mathematicae, 102(1): 99–118. [doi:10.1007/s10440-008-9212-8](https://doi.org/10.1007%2Fs10440-008-9212-8), and Kanti V. Mardia, John T. Kent and John M. Bibby (1979). \"Multivariate analysis\", ISBN:978-0124712522. Academic Press, London.",[32],{"_20":58,"_30":63},{"_22":59,"_24":60,"_26":61,"_28":62},16342,"plattice","Lattice Plot for Panel Data","It creates a lattice plot to visualize panel or longitudinal data. The observed values are plotted as dots and the fitted values as lines, both against time. The plot is customizable and easy to edit, even if you do not know how to construct a lattice plot from scratch.",[40],{"_20":65,"_30":70},{"_22":66,"_24":67,"_26":68,"_28":69},16696,"prior3D","3D Prioritization Algorithm","Three-dimensional systematic conservation planning, conducting nested prioritization analyses across multiple depth levels and ensuring efficient resource allocation throughout the water column. It provides a structured workflow designed to address biodiversity conservation and management challenges in the 3 dimensions, while facilitating users’ choices and parameterization ([doi:10.1016/j.ecolmodel.2024.110919](https://doi.org/10.1016%2Fj.ecolmodel.2024.110919)).",[40,48,49],{"_20":72,"_30":77},{"_22":73,"_24":74,"_26":75,"_28":76},16697,"priorCON","Graph Community Detection Methods into Systematic Conservation Planning","An innovative tool-set that incorporates graph community detection methods into systematic conservation planning. It is designed to enhance spatial prioritization by focusing on the protection of areas with high ecological connectivity. Unlike traditional approaches that prioritize individual planning units, 'priorCON' focuses on clusters of features that exhibit strong ecological linkages. The 'priorCON' package is built upon the 'prioritizr' package [doi:10.32614/CRAN.package.prioritizr](https://doi.org/10.32614%2FCRAN.package.prioritizr), using commercial and open-source exact algorithm solvers that ensure optimal solutions to prioritization problems.",[40,48,49],{"_20":79,"_30":84},{"_22":80,"_24":81,"_26":82,"_28":83},18582,"scoredec","S-Core Graph Decomposition","S-Core Graph Decomposition algorithm for graphs. This is a method for decomposition of a weighted graph, as proposed by Eidsaa and Almaas (2013) [doi:10.1103/PhysRevE.88.062819](https://doi.org/10.1103%2FPhysRevE.88.062819). The high speed and the low memory usage make it suitable for large graphs.",[40,48,49],{"_20":86,"_30":91},{"_22":87,"_24":88,"_26":89,"_28":90},22512,"lightAUC","Fast AUC Computation","Fast calculation of Area Under Curve (AUC) metric of a Receiver Operating Characteristic (ROC) curve, using the algorithm of Fawcett (2006) [doi:10.1016/j.patrec.2005.10.010](https://doi.org/10.1016%2Fj.patrec.2005.10.010). Therefore it is appropriate for large-scale AUC metric calculations.",[49,40,48],"otherAuthors",[94,109,113,117,121,125,128,131,134,140,145,150,155],{"_22":95,"_24":96,"_97":-5,"_98":-5,"_99":-5,"_100":-5,"_101":-5,"_102":103,"_104":-5,"_105":-5,"_106":-5,"_107":-5,"_108":103},12904,"Giorgos Athineou","email","extra","orc_id","orc_link","github_id","created_at","2024-11-24T20:49:28.774818+00:00","github_link","unknown_link","website_link","linkedin_link","_last_scraped_at",{"_22":110,"_24":111,"_97":-5,"_98":-5,"_99":-5,"_100":-5,"_101":-5,"_102":112,"_104":-5,"_105":-5,"_106":-5,"_107":-5,"_108":112},2770,"Abdulaziz Alenazi","2024-11-24T20:49:21.105835+00:00",{"_22":114,"_24":115,"_97":-5,"_98":-5,"_99":-5,"_100":-5,"_101":-5,"_102":116,"_104":-5,"_105":-5,"_106":-5,"_107":-5,"_108":116},309,"Michail Tsagris","2024-11-24T20:49:18.832187+00:00",{"_22":118,"_24":119,"_97":120,"_98":-5,"_99":-5,"_100":-5,"_101":-5,"_102":116,"_104":-5,"_105":-5,"_106":-5,"_107":-5,"_108":116},413,"Panagiotis Papastamoulis","papapast@yahoo.gr",{"_22":122,"_24":123,"_97":-5,"_98":-5,"_99":-5,"_100":-5,"_101":-5,"_102":124,"_104":-5,"_105":-5,"_106":-5,"_107":-5,"_108":124},13470,"Zehao Yu","2024-11-24T20:49:29.104201+00:00",{"_22":126,"_24":127,"_97":-5,"_98":-5,"_99":-5,"_100":-5,"_101":-5,"_102":124,"_104":-5,"_105":-5,"_106":-5,"_107":-5,"_108":124},13471,"Anamul Sajib",{"_22":129,"_24":130,"_97":-5,"_98":-5,"_99":-5,"_100":-5,"_101":-5,"_102":124,"_104":-5,"_105":-5,"_106":-5,"_107":-5,"_108":124},13472,"Eli Amson",{"_22":132,"_24":133,"_97":-5,"_98":-5,"_99":-5,"_100":-5,"_101":-5,"_102":124,"_104":-5,"_105":-5,"_106":-5,"_107":-5,"_108":124},13473,"Micah J. Waldstein",{"_22":135,"_24":136,"_97":-5,"_98":-5,"_99":137,"_100":138,"_101":-5,"_102":139,"_104":-5,"_105":-5,"_106":-5,"_107":-5,"_108":139},28981,"Nikolaos Nagkoulis","0000-0002-1900-2634","https://orcid.org/0000-0002-1900-2634","2024-11-24T20:49:39.98182+00:00",{"_22":141,"_24":142,"_97":-5,"_98":-5,"_99":143,"_100":144,"_101":-5,"_102":139,"_104":-5,"_105":-5,"_106":-5,"_107":-5,"_108":139},28982,"Antonios D. Mazaris","0000-0002-4961-5490","https://orcid.org/0000-0002-4961-5490",{"_22":146,"_24":147,"_97":-5,"_98":-5,"_99":148,"_100":149,"_101":-5,"_102":139,"_104":-5,"_105":-5,"_106":-5,"_107":-5,"_108":139},28980,"Aggeliki Doxa","0000-0003-4279-1499","https://orcid.org/0000-0003-4279-1499",{"_22":151,"_24":152,"_97":-5,"_98":-5,"_99":153,"_100":154,"_101":-5,"_102":139,"_104":-5,"_105":-5,"_106":-5,"_107":-5,"_108":139},28983,"Stelios Katsanevakis","0000-0002-5137-7540","https://orcid.org/0000-0002-5137-7540",{"_22":156,"_24":157,"_97":-5,"_98":-5,"_99":158,"_100":159,"_101":-5,"_102":160,"_104":-5,"_105":-5,"_106":-5,"_107":-5,"_108":160},37732,"Maria Papazekou","0000-0001-7063-9553","https://orcid.org/0000-0001-7063-9553","2025-02-11T03:14:01.800781+00:00","Christos Adam has been quite busy, working on 9 packages. You could say Christos Adam is on a coding spree! Keep up the great work! Christos Adam collaborated with 13 other developers. That's like an entire class of coding geniuses coming together!","actionData","errors"]

Christos Adam

9 Packages

Team