The author gives a short introduction to the SODAS software. SODAS (Symbolic Official Data Analysis System) is a modular software in which each statistical method (symbolic objects data base, distance matrix for symbolic objects, divisible classification of symbolic data, symbolic kernel discriminant analysis, symbolic description of groups, factorial discriminant analysis, principal component analysis, histograms and elementary statistics, segmentation tree for stratified data, decision tree, etc.) is manipulated as an icon and icons are linked in a chaining. A symbolic data analysis with SODAS software looks graphically like a chain with links the statistical methods. The top icon represents the symbolic data file. A chaining gathers a set of symbolic statistical methods applied to a specified SODAS file. The chaining editor is used to create, modify, launch, suppress or rename any chaining. In all cases consistency control are made if a method needs results from a preceding method

References in zbMATH (referenced in 74 articles , 1 standard article )

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  1. Liu, Fei; Billard, L.: Partition of interval-valued observations using regression (2022)
  2. Sun, Yuying; Zhang, Xinyu; Wan, Alan T. K.; Wang, Shouyang: Model averaging for interval-valued data (2022)
  3. Beyaztaş, Beste Hamiye: Bootstrap based multi-step ahead joint forecast densities for financial interval-valued time series (2021)
  4. de A. T. de Carvalho, Francisco; de A. Lima Neto, Eufrásio; da Silva, Kassio C. F.: A clusterwise nonlinear regression algorithm for interval-valued data (2021)
  5. Dias, Sónia; Brito, Paula; Amaral, Paula: Discriminant analysis of distributional data via fractional programming (2021)
  6. Kejžar, Nataša; Korenjak-Černe, Simona; Batagelj, Vladimir: Clustering of modal-valued symbolic data (2021)
  7. Samadi, S. Yaser; Billard, Lynne: Analysis of dependent data aggregated into intervals (2021)
  8. Umbleja, Kadri; Ichino, Manabu; Yaguchi, Hiroyuki: Hierarchical conceptual clustering based on quantile method for identifying microscopic details in distributional data (2021)
  9. Calcagnì, Antonio; Lombardi, Luigi; Avanzi, Lorenzo; Pascali, Eduardo: Multiple mediation analysis for interval-valued data (2020)
  10. Guan, Rong; Wang, Huiwen; Zheng, Haitao: Improving accuracy of financial distress prediction by considering volatility: an interval-data-based discriminant model (2020)
  11. Nascimento, Diego C.; Pimentel, Bruno; Souza, Renata; Leite, João P.; Edwards, Dylan J.; Santos, Taiza E. G.; Louzada, Francisco: Dynamic time series smoothing for symbolic interval data applied to neuroscience (2020)
  12. Maharaj, Elizabeth Ann; Teles, Paulo; Brito, Paula: Clustering of interval time series (2019)
  13. Duarte Silva, A. Pedro; Filzmoser, Peter; Brito, Paula: Outlier detection in interval data (2018)
  14. Sun, Yuying; Han, Ai; Hong, Yongmiao; Wang, Shouyang: Threshold autoregressive models for interval-valued time series data (2018)
  15. Hao, Peng; Guo, Junpeng: Constrained center and range joint model for interval-valued symbolic data regression (2017)
  16. Hron, Karel; Brito, Paula; Filzmoser, Peter: Exploratory data analysis for interval compositional data (2017)
  17. Le-Rademacher, J.; Billard, L.: Principal component analysis for histogram-valued data (2017)
  18. Wei, Yuan; Wang, Shanshan; Wang, Huiwen: Interval-valued data regression using partial linear model (2017)
  19. Blanco-Fernández, A.; González-Rodríguez, G.: Inferential studies for a flexible linear regression model for interval-valued variables (2016)
  20. Li, Wenhua; Guo, Junpeng; Chen, Ying; Wang, Minglu: A new representation of interval symbolic data and its application in dynamic clustering (2016)

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