Estimate and confidence/credible intervals for an unknown
    regressor x0 given an observed y0.

Perform two linear combination methods for biomarkers: (1) Empirical performance optimization for specificity (or sensitivity) at a controlled sensitivity (or specificity) level of Huang and Sanda (2022) <doi:10.1214/22-aos2210>, and (2) weighted maximum score estimator with empirical minimization of averaged false positive rate and false negative rate. Both adopt the algorithms of Huang and Sanda (2022) <doi:10.1214/22-aos2210>. 'MOSEK' solver is used and needs to be installed; an academic license for 'MOSEK' is free.

Conducting Line transect-based one-dimensional nearest neighbor distance and conducting hypothesis
 testing related to local distributional aggregation pattern of species. The reason that such a package is needed is
 because traditional two-dimensional nearest neighbor distance is not applicable when biodiversity data are sampled via
 optimal ecological survey methods, like line transects. In comparison to the entire studied region, line transect-collected local biodiversity data are spatially constrained
 and sampling-limited. To this end, two-dimensional nearest neighbor distance would tend to over-estimate  distributional aggregation pattern of species when using this limited biodiversity information.
 Accordingly, one-dimensional nearest neighbor distance is needed and the associated statistical testing should be established for analyzing
 line transect-derived biodiversity data.

L-systems or Lindenmayer systems are parallel rewriting systems which can
    be used to simulate biological forms and certain kinds of fractals.
    Briefly, in an L-system a series of symbols in a string are replaced
    iteratively according to rules to give a more complex string. Eventually,
    the symbols are translated into turtle graphics for plotting. Wikipedia has
    a very good introduction: en.wikipedia.org/wiki/L-system This package
    provides basic functions for exploring L-systems.