A simple interface to build designs using the package 'DeclareDesign'. 
    In one line of code, users can specify the parameters of individual 
    designs and diagnose their properties. The designers can also be used 
    to compare performance of a given design across a range of combinations 
    of parameters, such as effect size, sample size, and assignment probabilities.

Generate balanced factorial designs with crossed and nested random and fixed effects <https://github.com/mmrabe/designr>.

Different sample size calculations with different study designs.
             These techniques are explained by Chow (2007)
             <doi:10.1201/9781584889830>.

Functions for (1) ranking, selecting, and prioritising  genes,
    proteins, and metabolites from high dimensional biology experiments, (2)
    multivariate hit calling in high content screens, and (3) combining data
    from diverse sources.

S3 classes for multivariate optimization using the desirability function by Derringer and Suich (1980).

In-line functions for multivariate optimization via
    desirability functions (Derringer and Suich, 1980,
    <doi:10.1080/00224065.1980.11980968>) with easy use within 'dplyr'
    pipelines.

Written to help undergraduate as well as graduate students to get started
    with R for basic econometrics without the need to import specific functions
    and datasets from many different sources. Primarily, the package is meant to 
    accompany the German textbook Auer, L.v., Hoffmann, S., Kranz, T. (2024,
    ISBN: 978-3-662-68263-0) from which the exercises cover all the topics from the textbook
    Auer, L.v. (2023, ISBN: 978-3-658-42699-6).

Calculates the desparsified lasso as originally introduced in van de Geer et al. (2014) <doi:10.1214/14-AOS1221>, and provides inference suitable for high-dimensional time series, based on the long run covariance estimator in Adamek et al. (2020) <doi:10.48550/arXiv.2007.10952>. Also estimates high-dimensional local projections by the desparsified lasso, as described in Adamek et al. (2022) <doi:10.48550/arXiv.2209.03218>.