Finds optimal designs for nonlinear models using a metaheuristic algorithm called Imperialist Competitive Algorithm (ICA). See, for details, Masoudi et al. (2022) <doi:10.32614/RJ-2022-043>, Masoudi et al. (2017) <doi:10.1016/j.csda.2016.06.014> and Masoudi et al. (2019) <doi:10.1080/10618600.2019.1601097>.

Running Focused Identification of the Germplasm Strategy (FIGS) to make best subsets from Genebank Collection.

Implements the integrative conditional autoregressive horseshoe model 
    discussed in Jendoubi, T., Ebbels, T.M. Integrative analysis of time course metabolic data and biomarker discovery. 
    BMC Bioinformatics 21, 11 (2020) <doi:10.1186/s12859-019-3333-0>.
    The model consists in three levels: Metabolic pathways level modeling interdependencies between
    variables via a conditional auto-regressive (CAR) component, integrative analysis level to identify
    potential associations between heterogeneous omic variables via a Horseshoe prior and experimental
    design level to capture experimental design conditions through a mixed-effects model.
    The package also provides functions to simulate data from the model, construct pathway matrices,
    post process and plot model parameters.

Provides user-friendly tools for calibration in survey sampling.
    The package is production-oriented, and its interface is inspired by the famous
    popular macro 'Calmar' for SAS, so that 'Calmar' users can quickly get used to
    'icarus'. In addition to calibration (with linear, raking and logit methods),
    'icarus' features functions for calibration on tight bounds and penalized
    calibration.

Implementation of the methodology proposed in 'Data-driven design of targeted gene panels for estimating immunotherapy biomarkers', Bradley and Cannings (2021) <arXiv:2102.04296>. This package allows the user to fit generative models of mutation from an annotated mutation dataset, and then further to produce tunable linear estimators of exome-wide biomarkers. It also contains functions to simulate mutation annotated format (MAF) data, as well as to analyse the output and performance of models.