Provides a comprehensive collection of practical R utilities 
    designed to streamline the full life cycle of package development, 
    interactive data analysis, and session management. It offers tools for 
    package management, development workflows, debugging, data processing, 
    and system optimization. These utilities help R developers and data 
    scientists automate release tasks, manage and scan dependencies, 
    audit session states, optimize memory usage, and scramble sensitive 
    Personally Identifiable Information ('PII').

Provides a non-drawing graphic device for benchmarking purpose.
    In order to properly benchmark graphic drawing code it is necessary
    to factor out the device implementation itself so that results are not 
    related to the specific graphics device used during benchmarking. The 
    'devoid' package implements a graphic device that accepts all the required
    calls from R's graphic engine but performs no action. Apart from 
    benchmarking it is unlikely that this device has any practical use.

Data sets and sample analyses from Jay L. Devore (2008),
        "Probability and Statistics for Engineering and the Sciences
        (7th ed)", Thomson.

A set of functions to quantify the relationship between development
    rate and temperature and to build phenological models. The package comprises 
    a set of models and estimated parameters borrowed from a literature review 
    in ectotherms. The methods and literature review are described in Rebaudo 
    et al. (2018) <doi:10.1111/2041-210X.12935>, Rebaudo and Rabhi (2018) 
    <doi:10.1111/eea.12693>, and Regnier et al. (2021) <doi:10.1093/ee/nvab115>. 
    An example can be found in Rebaudo et al. (2017) 
    <doi:10.1007/s13355-017-0480-5>.

Collection of package development tools.

Develop and evaluate treatment rules based on: (1) the standard indirect approach of split-regression, which fits regressions separately in both treatment groups and assigns an individual to the treatment option under which predicted outcome is more desirable; (2) the direct approach of outcome-weighted-learning proposed by Yingqi Zhao, Donglin Zeng, A. John Rush, and Michael Kosorok (2012) <doi:10.1080/01621459.2012.695674>; (3) the direct approach, which we refer to as direct-interactions, proposed by Shuai Chen, Lu Tian, Tianxi Cai, and Menggang Yu (2017) <doi:10.1111/biom.12676>. Please see the vignette for a walk-through of how to start with an observational dataset whose design is understood scientifically and end up with a treatment rule that is trustworthy statistically, along with an estimation of rule benefit in an independent sample.