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dc.contributor.authorOrtiz, Helena
dc.contributor.authorAcal González, Christian José 
dc.contributor.authorVidal, Marc
dc.contributor.authorRoldan, Juan B
dc.contributor.authorAguilera Del Pino, Ana María 
dc.date.accessioned2025-12-18T08:13:07Z
dc.date.available2025-12-18T08:13:07Z
dc.date.issued2026
dc.identifier.urihttps://hdl.handle.net/10481/108928
dc.description.abstractIn the interest of transparency and knowledge transfer, the attached file contains the data used and the code scripts implemented in the R statistical software for the paper entitled “New approaches for scalar-on-function regression via independent component analysis”.es_ES
dc.description.abstractIn the “Models” folder, the CaseI and CaseII files can be found; these contain the functions used to simulate the data for the first and second simulations, respectively. The “Methods” folder includes the functions corresponding to the employed methods (1–12), as well as the fica and fica_hybrid functions, which are used to run the different types of FICA. In addition, this folder contains the Simulation file, from which all simulation data are generated and the corresponding results are obtained. Finally, the “RRAMs” folder includes the basic coefficients for the set and reset curves, along with the response variable data in CSV format, where column 4 corresponds to set and column 5 to reset. Important: To run the simulation, the directories containing the models and the methods must be defined before loading each of them.es_ES
dc.language.isoenges_ES
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.titleR code and data used in paper "New approaches for scalar-on-function regression via independent component analysis"es_ES
dc.typejournal articlees_ES
dc.rights.accessRightsopen accesses_ES
dc.type.hasVersionAOes_ES


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Attribution-NonCommercial-NoDerivatives 4.0 Internacional
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivatives 4.0 Internacional