Collapse, partition, combine, fill gaps in and expand date/time ranges.

User-friendly, clear and simple statistics, primarily for
  publication in psychological science. The main functions are wrappers for
  other packages, but there are various additions as well. Every relevant step
  from data aggregation to reportable printed statistics is covered for basic
  experimental designs.

A fast negative binomial mixed model for conducting association analysis of multi-subject single-cell data. It can be used for identifying marker genes, differential expression and co-expression analyses. The model includes subject-level random effects to account for the hierarchical structure in multi-subject single-cell data. See He et al. (2021) <doi:10.1038/s42003-021-02146-6>.

Tools to generate Necklaces, Bracelets, Lyndon words and de Bruijn sequences. The generation relies on integer partitions and uses the 'KStatistics' package. Methods used in the package refers to E. Di Nardo and G. Guarino (2022) <arXiv:2208.06855>.

Showcasing needmining (the semi-automatic extraction of customer needs from
	social media data) with Twitter data. It uses the handling of the Twitter API provided by
	the package 'rtweet' and the textmining algorithms provided by the package 'tm'.
	Niklas Kuehl (2016) <doi:10.1007/978-3-319-32689-4_14> wrote an introduction to the topic of needmining.

A simple function for easier package loading and auto-installation.

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>.