Reads in continuous glucose monitor data of many different formats, calculates a host of glycemic variability metrics, and plots glucose over time.

Implementation of the affine-invariant method of Goodman & Weare (2010) <DOI:10.2140/camcos.2010.5.65>, a method of producing Monte-Carlo samples from a target distribution.

DBI/RJDBC interface to h2 database. h2 version 2.3.232 is included.

An R interface to the 'Handsontable' JavaScript library, which is a
    minimalist Excel-like data grid editor.  See <https://handsontable.com/> for details.

The renewal Hawkes (RHawkes) process (Wheatley,
    Filimonov, and Sornette, 2016 <doi:10.1016/j.csda.2015.08.007>) is
    an extension to the classical Hawkes self-exciting point process
    widely used in the modelling of clustered event sequence data.
    This package provides functions to simulate the RHawkes process
    with a given immigrant hazard rate function and offspring birth
    time density function, to compute the exact likelihood of a
    RHawkes process using the recursive algorithm proposed by Chen and
    Stindl (2018) <doi:10.1080/10618600.2017.1341324>, to compute the
    Rosenblatt residuals for goodness-of-fit assessment, and to
    predict future event times based on observed event times up to a
    given time. A function implementing the linear time RHawkes
    process likelihood approximation algorithm proposed in Stindl and
    Chen (2021) <doi:10.1007/s11222-021-10002-0> is also included.

The evaluation criteria of rangeland health, condition and landscape function analysis
             based on species diversity and functional diversity of rangeland plant communities.

Non-parametric clustering of joint pattern multi-genetic/epigenetic factors. This package contains functions designed to cluster subjects based on gene features including single nucleotide polymorphisms (SNPs), DNA methylation (CPG), gene expression (GE), and covariate data. The novel concept follows the general K-means (Hartigan and Wong (1979) <doi:10.2307/2346830> framework but uses weighted Euclidean distances across the gene features to cluster subjects. This approach is unique in that it attempts to capture all pairwise interactions in an effort to cluster based on their complex biological interactions.