Friendly implementation of the Mann-Whitney-Wilcoxon test for competitive gene set enrichment analysis.

Estimation equations are from a variety of sources and associated error estimation.

Methods for quality control and exploratory analysis of surface 
  water quality data collected in Massachusetts, USA.  Functions are developed 
  to facilitate data formatting for the Water Quality Exchange Network 
  <https://www.epa.gov/waterdata/water-quality-data-upload-wqx> and reporting 
  of data quality objectives to state agencies. Quality control methods are 
  from Massachusetts Department of Environmental Protection (2020) 
  <https://www.mass.gov/orgs/massachusetts-department-of-environmental-protection>.

Analyzes production and dispersal of seeds dispersed from trees and recovered in seed traps.  Motivated by long-term inventory plots where seed collections are used to infer seed production by each individual plant.

Simulates Multidimensional Adaptive Testing using the multidimensional three-parameter logistic model as described in Segall (1996) <doi:10.1007/BF02294343>, van der Linden (1999) <doi:10.3102/10769986024004398>, Reckase (2009) <doi:10.1007/978-0-387-89976-3>, and Mulder & van der Linden (2009) <doi:10.1007/s11336-008-9097-5>.

Calculates Model-Averaged Tail Area Wald
    (MATA-Wald) confidence intervals, and MATA-Wald confidence densities
    and distributions, which are constructed using single-model
    frequentist estimators and model weights. See Turek and Fletcher
    (2012) <doi:10.1016/j.csda.2012.03.002> and Fletcher et al (2019)
    <doi:10.1007/s10651-019-00432-5> for details.

Conveniently log everything you type into the R console. Logs are
    are stored as tidy data frames which can then be analyzed using 'tidyverse'
    style tools.

Statistical methods to match feature vectors between multiple datasets in a one-to-one fashion. Given a fixed number of classes/distributions, for each unit, exactly one vector of each class is observed without label. The goal is to label the feature vectors using each label exactly once so to produce the best match across datasets, e.g. by minimizing the variability within classes. Statistical solutions based on empirical loss functions and probabilistic modeling are provided. The 'Gurobi' software and its 'R' interface package are required for one of the package functions (match.2x()) and can be obtained at <https://www.gurobi.com/> (free academic license). For more details, refer to Degras (2022) <doi:10.1080/10618600.2022.2074429> "Scalable feature matching for large data collections" and Bandelt, Maas, and Spieksma (2004) <doi:10.1057/palgrave.jors.2601723> "Local search heuristics for multi-index assignment problems with decomposable costs".