Common utilities used in other MOSAIC-family packages are 
    collected here.

Data sets from Project MOSAIC (<http://www.mosaic-web.org>) used
    to teach mathematics, statistics, computation and modeling.  Funded by the
    NSF, Project MOSAIC is a community of educators working to tie together
    aspects of quantitative work that students in science, technology,
    engineering and mathematics will need in their professional lives, but
    which are usually taught in isolation, if at all.

Stability based methods for model order selection in clustering problems
 (Valentini, G (2007), <doi:10.1093/bioinformatics/btl600>).
 Using multiple perturbations of the data the stability of clustering solutions is assessed. Different
 perturbations may be used: resampling techniques, random projections and noise injection. Stability measures
 for the estimate of clustering solutions and statistical tests to assess their significance are provided.

Provides tools for analyzing Marshall-Olkin shock models semi-independent time.
    It includes interactive 'shiny' applications for exploring copula-based dependence 
    structures, along with functions for modeling and visualization. The methods are 
    based on Mijanovic and Popovic (2024, submitted) "An R package for Marshall-Olkin 
    shock models with semi-independent times."

This project aims to make an accessible model for mosquito control resource optimization. The model uses data provided by users to estimate the mosquito populations in the sampling area for the sampling time period, and the optimal time to apply a treatment or multiple treatments.

Provides functions similar to the 'SAS' macros previously provided
              to accompany Collins, Dziak, and Li (2009) <DOI:10.1037/a0015826>
              and Dziak, Nahum-Shani, and Collins (2012) <DOI:10.1037/a0026972>, papers
              which outline practical benefits and challenges of factorial
              and fractional factorial experiments for scientists interested
              in developing biological and/or behavioral interventions, especially
              in the context of the multiphase optimization strategy
              (see Collins, Kugler & Gwadz 2016) <DOI:10.1007/s10461-015-1145-4>.  The package
              currently contains three functions. First, RelativeCosts1() draws a graph
              of the relative cost of complete and reduced factorial designs versus
              other alternatives. Second, RandomAssignmentGenerator() returns a dataframe
              which contains a list of random numbers that can be used to conveniently
              assign participants to conditions in an experiment with
              many conditions. Third, FactorialPowerPlan() estimates the power, detectable effect
              size, or required sample size of a factorial or fractional factorial
              experiment, for main effects or interactions, given several possible choices
              of effect size metric, and allowing pretests and clustering.