An educational package providing intuitive functions for calculating 
    confidence intervals (CI) for various statistical parameters. Designed 
    primarily for teaching and learning about statistical inference
    (particularly confidence intervals). 
    Offers user-friendly wrappers around established methods for proportions, 
    means, and bootstrap-based intervals. Integrates seamlessly with Tidyverse 
    workflows, making it ideal for classroom demonstrations and student exercises.

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 proportions. 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.