Infer progression of circadian rhythms in transcriptome data in
  which samples are not labeled with time of day and coverage of the circadian
  cycle may be incomplete. See Shilts et al. (2018) <doi:10.7717/peerj.4327>.

Analysis of agreement for nominal data between two raters using the Delta model. This model is proposed as an alternative to the widespread measure Cohen kappa coefficient, which performs poorly when the marginal distributions are very asymmetric (Martin-Andres and Femia-Marzo (2004), <doi:10.1348/000711004849268>; Martin-Andres and Femia-Marzo (2008) <doi:10.1080/03610920701669884>). The package also contains a function to perform a massive analysis of multiple raters against a gold standard. A shiny app is also provided to obtain the measures of nominal agreement between two raters.

The deltaPlotR package implements Angoff's Delta Plot method to detect dichotomous DIF. Several detection thresholds are included, either from multivariate normality assumption or by prior determination. Item purification is supported (Magis and Facon (2014) <doi:10.18637/jss.v059.c01>).

A collection of asymmetrical kernels belong to lifetime distributions for kernel density estimation is presented. 
    Mean Squared Errors (MSE) are calculated for estimated curves. For this purpose, R functions allow the distribution to be Gamma, Exponential or Weibull.
    For details see Chen (2000a,b), Jin and Kawczak (2003) and Salha et al. (2014) <doi:10.12988/pms.2014.4616>.

The distributed expectation maximization algorithms are used to solve parameters of multivariate Gaussian mixture models. The philosophy of the package is described in Guo, G. (2022) <doi:10.1080/02664763.2022.2053949>.

Developed to Solve the Multi-Criteria Decision Making Problems with Decision Making Trial and Evaluation Laboratory Technique in R.

Multi-sample algorithm based on contigs and coverage values, to 
    infer the relative distances of contigs from the replication origin and to 
    accurately compare bacterial growth rates between samples. Yuan Gao and 
    Hongzhe Li (2018) <doi:10.1038/s41592-018-0182-0>.