Read tables chunk by chunk using a C++ backend and a simple R interface.

An implementation of the probability mass function, cumulative density function, quantile function, random number generator, maximum likelihood estimator, and p-value generator from a conditional hypergeometric distribution: the distribution of how many items are in the overlap of all samples when samples of arbitrary size are each taken without replacement from populations of arbitrary size.

Causal Inference Assistance (CIA) for performing causal inference within the structural causal modelling framework. Structure learning is performed using partition Markov chain Monte Carlo (Kuipers & Moffa, 2017) and several additional functions have been added to help with causal inference. Kuipers and Moffa (2017) <doi:10.1080/01621459.2015.1133426>.

Calculation of consensus values for atomic weights, isotope amount ratios, and isotopic abundances with the associated uncertainties using multivariate meta-regression approach for consensus building.

Identification of markers of rare cell types by looking at genes whose expression is confined in small regions of the expression space <https://github.com/ScialdoneLab>.

Clustering multi-subject resting state functional Magnetic Resonance Imaging data. This methods enables the clustering of subjects based on multi-subject resting state functional Magnetic Resonance Imaging data. Objects are clustered based on similarities and differences in cluster-specific estimated components obtained by Independent Component Analysis.

This calculates a variety of different CIs for proportions
    and difference of proportions that are commonly used in the
    pharmaceutical industry including Wald, Wilson, Clopper-Pearson,
    Agresti-Coull and Jeffreys for proprotions. And Miettinen-Nurminen
    (1985) <doi:10.1002/sim.4780040211>, Wald, Haldane, and Mee
    <https://www.lexjansen.com/wuss/2016/127_Final_Paper_PDF.pdf> for
    difference in proportions.

Provides functionality for computing support intervals for univariate parameters based on confidence intervals or parameter estimates with standard errors (Pawel et al., 2022) <doi:10.48550/arXiv.2206.12290>.