Constructs optimal policy trees which provide a rule-based treatment prescription policy.
 Input is covariate and reward data, where, typically, the rewards will be doubly robust reward estimates.
 This package aims to construct optimal policy trees more quickly than the existing 'policytree' package and
 is intended to be used alongside that package.
 For more details see Cussens, Hatamyar, Shah and Kreif (2025) <doi:10.48550/arXiv.2506.15435>.

Finds the critical sample size ("critical point of stability") for a 
    correlation to stabilize in Schoenbrodt and Perugini's definition of 
    sequential stability (see <doi:10.1016/j.jrp.2013.05.009>).

'FASTQC' is the most widely used tool for evaluating the quality of high throughput sequencing data.  
    It produces, for each sample, an html report and a compressed file containing the raw data. 
    If you have hundreds of samples, you are not going to open up each 'HTML' page. 
    You need some way of looking at these data in aggregate. 
    'fastqcr' Provides helper functions to easily parse, aggregate and analyze 
    'FastQC' reports for large numbers of samples. It provides a convenient solution for building 
    a 'Multi-QC' report, as well as, a 'one-sample' report with result interpretations.

New and faster implementations for quantile quantile plots. 
    The package also includes a function to prune data for quantile quantile 
    plots. This can drastically reduce the running time for large samples, 
    for 100 million samples, you can expect a factor 80X speedup.

Efficient algorithms for performing, updating, and removing rows or columns from the QR decomposition, R decomposition, or the inverse of the R decomposition of a matrix as rows or columns are added or removed. It also includes functions for solving linear systems of equations, normal equations for linear regression models, and normal equations for linear regression with a RIDGE penalty. For a detailed introduction to these methods, the monograph Matrix Computations (2013, <doi:10.1007/978-3-319-05089-8>) for complete introduction to the methods.