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BayCauRETM: R package for Bayesian Causal Inference for Recurrent Event Outcomes

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Published in:Journal of Open Source Software
Format: Online Article RSS Article
Published: 2026
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container_title Journal of Open Source Software
description
discipline_display Engineering & Technology
discipline_facet Engineering & Technology
format Online Article
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genre Journal Article
id rss_article:11892
institution FRELIP
journal_source_facet Journal of Open Source Software
publishDate 2026
publishDateSort 2026
record_format rss_article
spellingShingle BayCauRETM: R package for Bayesian Causal Inference for Recurrent Event Outcomes
Computer Science & Information Science
Computer Science & IT
Engineering & Technology
sub_discipline_display Computer Science & IT
sub_discipline_facet Computer Science & IT
subject_display Computer Science & Information Science
Computer Science & IT
Engineering & Technology
Computer Science & Information Science
Computer Science & IT
Engineering & Technology
subject_facet Computer Science & Information Science
Computer Science & IT
Engineering & Technology
title BayCauRETM: R package for Bayesian Causal Inference for Recurrent Event Outcomes
title_auth BayCauRETM: R package for Bayesian Causal Inference for Recurrent Event Outcomes
title_full BayCauRETM: R package for Bayesian Causal Inference for Recurrent Event Outcomes
title_fullStr BayCauRETM: R package for Bayesian Causal Inference for Recurrent Event Outcomes
title_full_unstemmed BayCauRETM: R package for Bayesian Causal Inference for Recurrent Event Outcomes
title_short BayCauRETM: R package for Bayesian Causal Inference for Recurrent Event Outcomes
title_sort baycauretm: r package for bayesian causal inference for recurrent event outcomes
topic Computer Science & Information Science
Computer Science & IT
Engineering & Technology
url https://joss.theoj.org/papers/10.21105/joss.09458