Apache Drill is a low-latency distributed query engine designed to enable 
    data exploration and analysis on both relational and non-relational data stores, 
    scaling to petabytes of data. Methods are provided that enable working with Apache 
    Drill instances via the REST API, DBI methods
    and using 'dplyr'/'dbplyr' idioms. Helper functions are included to facilitate
    using official Drill Docker images/containers.

Enables reading and writing binary and ASCII data to 
             RS232/RS422/RS485 or any other virtual serial interface of the
             computer.

Infrastructure for ordering objects with an implementation of several
    seriation/sequencing/ordination techniques to reorder matrices, dissimilarity
    matrices, and dendrograms. Also provides (optimally) reordered heatmaps,
    color images and clustering visualizations like dissimilarity plots, and
    visual assessment of cluster tendency plots (VAT and iVAT). Hahsler et al (2008) <doi:10.18637/jss.v025.i03>.

Calculate and compare lower confidence bounds for binomial series system reliability. The R 'shiny' application, launched by the function launch_app(), weaves together a workflow of customized simulations and delta coverage calculations to output recommended lower confidence bound methods.

Estimating the force of infection from time varying, age varying, or constant
    serocatalytic models from population based seroprevalence studies using a Bayesian framework,
    including data simulation functions enabling the generation of serological surveys based on this
    models. This tool also provides a flexible prior specification syntax for the force of infection
    and the seroreversion rate, as well as methods to assess model convergence and comparison
    criteria along with useful visualisation functions.

Speeds up the process of loading raw data from MBA (Multiplex Bead Assay) examinations, performs quality control checks, and automatically normalises the data, preparing it for more advanced, downstream tasks. The main objective of the package is to create a simple environment for a user, who does not necessarily have experience with R language. The package is developed within the project 'PvSTATEM', which is an international project aiming for malaria elimination.