Data sets and functions, for the display of gene expression array (microarray) data, and for demonstrations with such data.

Implements the DAAREM method for accelerating the convergence of slow, monotone sequences from smooth, fixed-point iterations such as the EM algorithm. For further details about the DAAREM method, see Henderson, N.C. and Varadhan, R. (2019) <doi:10.1080/10618600.2019.1594835>.

Data Analysis using Bootstrap-Coupled ESTimation.  Estimation
    statistics is a simple framework that avoids the pitfalls of
    significance testing. It uses familiar statistical concepts: means,
    mean differences, and error bars. More importantly, it focuses on the
    effect size of one's experiment/intervention, as opposed to a false
    dichotomy engendered by P values.  An estimation plot has two key
    features: 1. It presents all datapoints as a swarmplot, which orders
    each point to display the underlying distribution.  2. It presents the
    effect size as a bootstrap 95% confidence interval on a separate but
    aligned axes.  Estimation plots are introduced in Ho et al., Nature
    Methods 2019, 1548-7105.  <doi:10.1038/s41592-019-0470-3>.  The
    free-to-view PDF is located at
    <https://www.nature.com/articles/s41592-019-0470-3.epdf?author_access_token=Euy6APITxsYA3huBKOFBvNRgN0jAjWel9jnR3ZoTv0Pr6zJiJ3AA5aH4989gOJS_dajtNr1Wt17D0fh-t4GFcvqwMYN03qb8C33na_UrCUcGrt-Z0J9aPL6TPSbOxIC-pbHWKUDo2XsUOr3hQmlRew%3D%3D>.

Provides functions to manage databases: select, update, insert,
    and delete records, list tables, backup tables as CSV files, and import
    CSV files as tables.

Detection and attribution of climate change using methods including optimal fingerprinting via 
    generalized total least squares or an estimating equation approach (Li et al., 2025, 
    <doi:10.1175/JCLI-D-24-0193.1>; Ma et al., 2023, <doi:10.1175/JCLI-D-22-0681.1>).
    Provides shrinkage estimators for the covariance matrix following 
    Ledoit and Wolf (2004, <doi:10.1016/S0047-259X(03)00096-4>) and 
    Ledoit and Wolf (2017, <doi:10.2139/ssrn.2383361>).