Monte Carlo simulation framework for different randomized clinical
    trial designs with a special emphasis on estimators based on covariate
    adjustment. The package implements regression-based covariate adjustment
    (Rosenblum & van der Laan (2010) <doi:10.2202/1557-4679.1138>) and a
    one-step estimator (Van Lancker et al (2024)
    <doi:10.48550/arXiv.2404.11150>) for trials with continuous, binary and
    count outcomes. The estimation of the minimum sample-size required to reach
    a specified statistical power for a given estimator uses bisection to find
    an initial rough estimate, followed by stochastic approximation
    (Robbins-Monro (1951) <doi:10.1214/aoms/1177729586>) to improve the
    estimate, and finally, a grid search to refine the estimate in the
    neighborhood of the current best solution.

A modeling tool allowing gene selection, reverse engineering, and prediction in cascade networks. Jung, N., Bertrand, F., Bahram, S., Vallat, L., and Maumy-Bertrand, M. (2014) <doi:10.1093/bioinformatics/btt705>.

These experimental expression data (5 leukemic 'CLL' B-lymphocyte of aggressive form from 'GSE39411', <doi:10.1073/pnas.1211130110>), after B-cell receptor stimulation, are used as examples by packages such as the 'Cascade' one, a modeling tool allowing gene selection, reverse engineering, and prediction in cascade networks. Jung, N., Bertrand, F., Bahram, S., Vallat, L., and Maumy-Bertrand, M. (2014) <doi:10.1093/bioinformatics/btt705>.

Provides a cascade select widget for usage in 'Shiny'
    applications. This is useful for selection of hierarchical choices
    (e.g. continent, country, city). It is taken from the 'JavaScript'
    library 'PrimeReact'.

Apply styles to tag elements directly and with the .style
  pronoun. Using the pronoun, styles are created within the context of a tag
  element. Change borders, backgrounds, text, margins, layouts, and more.

Functions for implementing the novel algorithm CASCORE, which is designed to detect latent community structure in graphs with node covariates. This algorithm can handle models such as the covariate-assisted degree corrected stochastic block model (CADCSBM). CASCORE specifically addresses the disagreement between the community structure inferred from the adjacency information and the community structure inferred from the covariate information. For more detailed information, please refer to the reference paper: Yaofang Hu and Wanjie Wang (2022) <arXiv:2306.15616>. 
    In addition to CASCORE, this package includes several classical community detection algorithms that are compared to CASCORE in our paper. These algorithms are: Spectral Clustering On Ratios-of Eigenvectors (SCORE), normalized PCA, ordinary PCA, network-based clustering, covariates-based clustering and covariate-assisted spectral clustering (CASC). By providing these additional algorithms, the package enables users to compare their performance with CASCORE in community detection tasks.