Offers tools to estimate and visualize levels of major pollutants
             (CO, NO2, SO2, Ozone, PM2.5 and PM10) across the conterminous
             United States for user-defined time ranges. Provides functions to
             retrieve pollutant data from the U.S. Environmental Protection
             Agency’s 'Air Quality System' (AQS) API service
             <https://aqs.epa.gov/aqsweb/documents/data_api.html> for
             interactive visualization through a 'shiny' application, allowing
             users to explore pollutant levels for a given location over time
             relative to the National Ambient Air Quality Standards (NAAQS).

Provides a parallel estimation method for generalized 
  linear models without compiling with a multithreaded LAPACK or BLAS.

Computes the All-Resolution Inference method in the permutation framework, i.e., simultaneous lower confidence bounds for the number of true discoveries. <doi:10.1002/sim.9725>.

Palettes inspired by Paris 2024 Olympic and Paralympic Games for 
    data visualizations. Length of color palettes is configurable.

Provision of a set of models and methods for use in the allocation and management of capital in financial portfolios.

Parallel estimation of the mutual information based on entropy
    estimates from k-nearest neighbors distances and algorithms for the
    reconstruction of gene regulatory networks
    (Sales et al, 2011 <doi:10.1093/bioinformatics/btr274>).

Executes simple parametric models for right-censored 
    survival data.  Functionality emulates capabilities in 'Minitab', including
    fitting right-censored data, assessing fit, plotting survival functions,
    and summary statistics and probabilities.

Enable optimization of parameters of ordinary differential equations.
             Therefore, using 'SUNDIALS' to solve the ODE-System (see Hindmarsh, Alan C., Peter N. Brown, Keith E. Grant, Steven L. Lee, Radu Serban, Dan E. Shumaker, and Carol S. Woodward. (2005) <doi:10.1145/1089014.1089020>).
             Furthermore, for optimization the particle swarm algorithm is used (see: Akman, Devin, Olcay Akman, and Elsa Schaefer. (2018) <doi:10.1155/2018/9160793> and Sengupta, Saptarshi, Sanchita Basak, and Richard Peters. (2018) <doi:10.3390/make1010010>).