A toolkit with infrastructure for representing, summarizing, and
  visualizing tree-structured regression and classification models. This
  unified infrastructure can be used for reading/coercing tree models from
  different sources ('rpart', 'RWeka', 'PMML') yielding objects that share
  functionality for print()/plot()/predict() methods. Furthermore, new and improved
  reimplementations of conditional inference trees (ctree()) and model-based
  recursive partitioning (mob()) from the 'party' package are provided based
  on the new infrastructure. A description of this package was published
  by Hothorn and Zeileis (2015) <https://jmlr.org/papers/v16/hothorn15a.html>.

Parse messy geographic coordinates from various character formats
    to decimal degree numeric values. Parse coordinates into
    their parts (degree, minutes, seconds); calculate hemisphere
    from coordinates; pull out individually degrees,
    minutes, or seconds; add and subtract degrees, minutes,
    and seconds. C++ code herein originally inspired from code
    written by Jeffrey D. Bogan, but then completely re-written.

An R package for polygenic trait analysis.

Anomaly detection method based on the paper "Truth will out: Departure-based process-level detection of stealthy attacks on control systems" from Wissam Aoudi, Mikel Iturbe, and Magnus Almgren (2018) <DOI:10.1145/3243734.3243781>. Also referred to the following implementation: <https://github.com/rahulrajpl/PyPASAD>.

Provide summary table of daily physical activity and per-person/grouped heat map for accelerometer data from SenseWear Armband. See <https://templehealthcare.wordpress.com/the-sensewear-armband/> for more information about SenseWear Armband.

Power and sample size calculation for testing fixed effect coefficients in multilevel linear mixed effect models with one or more than one independent populations. Laird, Nan M. and Ware, James H. (1982) <doi:10.2307/2529876>.

Power calculations are a critical component of any research study to determine the 
	minimum sample size necessary to detect differences between multiple groups. Here we 
	present an 'R' package, 'PASSED', that performs power and sample size calculations for 
	the test of two-sample means or ratios with data following beta, 
	gamma (Chang et al. (2011), <doi:10.1007/s00180-010-0209-1>), normal, 
	Poisson (Gu et al. (2008), <doi:10.1002/bimj.200710403>), binomial, geometric, and 
	negative binomial (Zhu and Lakkis (2014), <doi:10.1002/sim.5947>) distributions.