Multi-Fidelity emulator for data from computer simulations of the
    same underlying system but at different input locations and fidelity level,
    where both the input locations and fidelity level can be continuous. Active
    Learning can be performed with an implementation of the Integrated Mean Square 
    Prediction Error (IMSPE) criterion developed by Boutelet and Sung (2025, <doi:10.48550/arXiv.2503.23158>).

We develop Multi-source Graph Synthesis (MUGS), an algorithm designed to create 
    embeddings for pediatric Electronic Health Record (EHR) codes by leveraging graphical information from three distinct sources:
    (1) pediatric EHR data, (2) EHR data from the general patient population, and 
    (3) existing hierarchical medical ontology knowledge shared across different patient populations. 
    See Li et al. (2024) <doi:10.1038/s41746-024-01320-4> for details.

Produces a smooth estimate of the hazard
  function for censored data.

A set of user interface components for building 'shiny' applications and 
  'quarto' documents, including inputs, layouts, navigation, surfaces, and various
  utilities. All components 'Material UI' from the company 'MUI' <https://mui.com/>
  are available and all inputs have usage examples in R.

A simple tool allowing users to easily and dynamically explore or document a data set using a tree structure.

Give access to 'MUI X Tree View' components, which lets users navigate hierarchical 
  lists of data with nested levels that can be expanded and collapsed.

Background - Traditional gene set enrichment analyses are 
    typically limited to a few ontologies and do not account for the 
    interdependence of gene sets or terms, resulting in overcorrected p-values. 
    To address these challenges, we introduce mulea, an R package offering 
    comprehensive overrepresentation and functional enrichment analysis. 
    Results - mulea employs a progressive empirical false discovery rate 
    (eFDR) method, specifically designed for interconnected biological data, 
    to accurately identify significant terms within diverse ontologies. mulea 
    expands beyond traditional tools by incorporating a wide range of 
    ontologies, encompassing Gene Ontology, pathways, regulatory elements, 
    genomic locations, and protein domains. This flexibility enables 
    researchers to tailor enrichment analysis to their specific questions, 
    such as identifying enriched transcriptional regulators in gene expression 
    data or overrepresented protein domains in protein sets. To facilitate 
    seamless analysis, mulea provides gene sets (in standardised GMT format) 
    for 27 model organisms, covering 22 ontology types from 16 databases and 
    various identifiers resulting in almost 900 files. Additionally, the 
    muleaData ExperimentData Bioconductor package simplifies access to these 
    pre-defined ontologies. Finally, mulea's architecture allows for easy 
    integration of user-defined ontologies, or GMT files from external 
    sources (e.g., MSigDB or Enrichr), expanding its applicability across 
    diverse research areas. Conclusions - mulea is distributed as a CRAN R 
    package. It offers researchers a powerful and flexible toolkit for 
    functional enrichment analysis, addressing limitations of traditional 
    tools with its progressive eFDR and by supporting a variety of ontologies. 
    Overall, mulea fosters the exploration of diverse biological questions 
    across various model organisms.