Multi-generational pedigree inference from incomplete data on
    hundreds of SNPs, including parentage assignment and sibship clustering.
    See Huisman (2017) (<DOI:10.1111/1755-0998.12665>) for more information.

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.

A regularization method for the cumulative link
    models.  The smooth-effect-on-response penalty (SERP) provides
    flexible modelling of the ordinal model by enabling the smooth
    transition from the general cumulative link model to a coarser form of
    the same model. In other words, as the tuning parameter goes from zero
    to infinity, the subject-specific effects associated with each
    variable in the model tend to a unique global effect. The parameter
    estimates of the general cumulative model are mostly unidentifiable or
    at least only identifiable within a range of the entire parameter
    space. Thus, by maximizing a penalized rather than the usual
    non-penalized log-likelihood, this and other numerical problems common
    with the general model are to a large extent eliminated. Fitting is
    via a modified Newton's method. Several standard model performance and
    descriptive methods are also available. For more details on the penalty
    implemented here, see, Ugba (2021) <doi:10.21105/joss.03705> and 
    Ugba et al. (2021) <doi:10.3390/stats4030037>.