Methods to reduce confounding bias from unmeasured confounders in observational studies of vaccine efficacy using negative control outcomes.

The Negative Binomial regression with mean and shape modeling and mean and variance modeling and Beta Binomial regression with mean and dispersion modeling.

Estimating the number of essential genes in a genome on
    the basis of data from a random transposon mutagenesis experiment,
    through the use of a Gibbs sampler.
    Lamichhane et al. (2003) <doi:10.1073/pnas.1231432100>.

Researchers often want to evaluate whether there is a negligible
    relationship among variables. The 'negligible' package provides functions that 
    are useful for conducting negligible effect testing (also called
    equivalence testing). For example, there are functions for evaluating the 
    equivalence of means or the presence of a negligible association 
    (correlation or  regression). Beribisky, N., Mara, C., & Cribbie, R. A. (2020) <doi:10.20982/tqmp.16.4.p424>.
    Beribisky, N., Davidson, H., Cribbie, R. A. (2019) <doi:10.7717/peerj.6853>.
    Shiskina, T., Farmus, L., & Cribbie, R. A. (2018) <doi:10.20982/tqmp.14.3.p167>.
    Mara, C. & Cribbie, R. A. (2017) <doi:10.1080/00220973.2017.1301356>.
    Counsell, A. & Cribbie, R. A. (2015) <doi:10.1111/bmsp.12045>.
    van Wieringen, K. & Cribbie, R. A. (2014) <doi:10.1111/bmsp.12015>.
    Goertzen, J. R. & Cribbie, R. A. (2010) <doi:10.1348/000711009x475853>.
    Cribbie, R. A., Gruman, J. & Arpin-Cribbie, C. (2004) <doi:10.1002/jclp.10217>.

A bootstrap method for Respondent-Driven Sampling (RDS) that relies on the underlying structure of the RDS network to estimate uncertainty.

Neighbourhood functions are key components of
  local-search algorithms such as Simulated Annealing or
  Threshold Accepting.  These functions take a solution and
  return a slightly-modified copy of it, i.e. a neighbour.
  The package provides a function neighbourfun() that
  constructs such neighbourhood functions, based on
  parameters such as admissible ranges for elements in a
  solution.  Supported are numeric and logical solutions.
  The algorithms were originally created for
  portfolio-optimisation applications, but can be used for
  other models as well.  Several recipes for neighbour
  computations are taken from "Numerical Methods and
  Optimization in Finance" by M. Gilli, D. Maringer and
  E. Schumann (2019, ISBN:978-0128150658).