Compare dissolution profiles with confidence interval of similarity
  factor f2 using bootstrap methodology as described in the literature, such as 
  Efron and Tibshirani (1993, ISBN:9780412042317), Davison and Hinkley (1997,
  ISBN:9780521573917), and Shah et al. (1998) <doi:10.1023/A:1011976615750>. 
  The package can also be used to simulate dissolution profiles based on 
  mathematical modelling and multivariate normal distribution.

Bootstrap based goodness-of-fit tests. It allows
    to perform rigorous statistical tests to check if a chosen
    model family is correct based on the marked empirical
    process. The implemented algorithms are described in
    (Dikta and Scheer (2021) <doi:10.1007/978-3-030-73480-0>)
    and can be applied
    to generalized linear models without any further implementation
    effort. As far as certain linearity conditions are fulfilled
    the resampling scheme are also applicable beyond generalized
    linear models. This is reflected in the software architecture
    which allows to reuse the resampling scheme by implementing
    only certain interfaces for models that are not supported
    natively by the package.

Bootstraps and imputes incomplete datasets. Then performs inference on estimates obtained from analysing the imputed datasets as proposed by von Hippel and Bartlett (2021) <doi:10.1214/20-STS793>.

Computes appropriate confidence intervals for the likelihood ratio tests commonly used in medicine/epidemiology, using the method of Marill et al. (2015) <doi:10.1177/0962280215592907>.  It is particularly useful when the sensitivity or specificity in the sample is 100%.  Note that this does not perform the test on nested models--for that, see 'epicalc::lrtest'.

Selection of informative features like genes, transcripts, RNA seq, etc. using Bootstrap Maximum Relevance and Minimum Redundancy technique from a given high dimensional genomic dataset. Informative gene selection involves identification of relevant genes and removal of redundant genes as much as possible from a large gene space. Main applications in high-dimensional expression data analysis (e.g. microarray data, NGS expression data and other genomics and proteomics applications).