Implements Design-Robust Meta-Analysis (DR-Meta), a
    variance-function random-effects framework in which between-study
    heterogeneity is modelled as a function of a study-level design
    robustness index, allowing heterogeneity to depend systematically on
    study quality or design strength rather than being treated as a single
    nuisance parameter. The package provides profiled restricted maximum
    likelihood (REML) estimation of the overall effect and
    variance-function parameters, study-specific weights, heterogeneity
    diagnostics (tau-squared, I-squared), influence and leave-one-out
    analysis, and graphical tools including forest plots and influence
    plots. The DR-Meta framework nests classical fixed-effects and
    standard random-effects meta-analysis as special cases, making it a
    strict generalisation of existing approaches.

Provides an 'R' interface to the 'Drogon' high-performance
    'C++' 'HTTP' server framework
    (<https://github.com/drogonframework/drogon>). Offers a 'plumber'-style
    application programming interface for building 'REST' services from
    'R' with substantially higher throughput, including streaming
    responses and full-duplex 'WebSocket' endpoints.

A toolkit for parsing dice notation, analyzing rolls,
    calculating success probabilities, and plotting outcome distributions.

Several functions are provided for dose-response (or concentration-response) characterization from omics data. 'DRomics' is especially dedicated to omics data obtained using a typical dose-response design, favoring a great number of tested doses (or concentrations) rather than a great number of replicates (no need of replicates). 'DRomics' provides functions 1) to check, normalize and or transform data, 2) to select monotonic or biphasic significantly responding items (e.g. probes, metabolites), 3) to choose the best-fit model among a predefined family of monotonic and biphasic models to describe each selected item, 4) to derive a benchmark dose or concentration and a typology of response from each fitted curve. In the available version data are supposed to be single-channel microarray data in log2, RNAseq data in raw counts, or already pretreated continuous omics data (such as metabolomic data) in log scale. In order to link responses across biological levels based on a common method, 'DRomics' also handles apical data as long as they are continuous and follow a normal distribution for each dose or concentration, with a common standard error. For further details see Delignette-Muller et al (2023) <DOI:10.24072/pcjournal.325> and Larras et al (2018) <DOI:10.1021/acs.est.8b04752>.