Process and manage the data from the Empatica E4. All functions operate on the EDA data stream, but other streams will be added soon. The Empatica E4 is a wearable physiological monitor made by Empatica (Empatica is not associated with any of this code). You can find more information about the E4 at Empatica's website <https://www.empatica.com/research/e4/>.

Fits dose-response models using an evolutionary
    algorithm to estimate the model parameters. The procedure currently
    can fit 3-parameter, 4-parameter, and 5-parameter log-logistic models
    as well as exponential models. Functions are also provided to plot,
    make predictions, and calculate confidence intervals for the resulting
    models. For details see "Nonlinear Dose-response Modeling of
    High-Throughput Screening Data Using an Evolutionary Algorithm",
    Ma, J., Bair, E., Motsinger-Reif, A.; Dose-Response
    18(2):1559325820926734 (2020) <doi:10.1177/1559325820926734>.

Computation and visualization of the empirical attainment function (EAF) for the analysis of random sets in multi-criterion optimization. M. López-Ibáñez, L. Paquete, and T. Stützle (2010) <doi:10.1007/978-3-642-02538-9_9>.

An implementation of multiple-locus association mapping on a genome-wide scale. 'Eagle' can handle inbred and  outbred study populations, populations of arbitrary unknown complexity, and data larger than the memory capacity of the computer. Since 'Eagle' is based on linear mixed models, it is best suited to the analysis of data on continuous traits. However, it can tolerate non-normal data. 'Eagle' reports, as its findings, the best set of snp in strongest association with a trait. For users unfamiliar with R, to perform an analysis, run 'OpenGUI()'. This  opens a web browser to the menu-driven user interface for the input of data, and for performing genome-wide analysis.

Estimator augmentation methods for statistical inference on high-dimensional data, 
    as described in Zhou, Q. (2014) <arXiv:1401.4425v2>
    and Zhou, Q. and Min, S. (2017) <doi:10.1214/17-EJS1309>.
    It provides several simulation-based inference methods: (a) Gaussian and 
    wild multiplier bootstrap for lasso, group lasso, scaled lasso, scaled group
    lasso and their de-biased estimators, (b) importance sampler for approximating
    p-values in these methods, (c) Markov chain Monte Carlo lasso sampler with 
    applications in post-selection inference.