Evaluates acute lymphoblastic leukemia maintenance therapy practice at patient and cohort level.

Implements several exact methods for allocating optimal sample sizes when
    designing stratified samples. These methods are discussed in Wright (2012)
    <doi:10.1080/00031305.2012.733679> and Wright (2017)
    <doi:10.1016/j.spl.2017.04.026>.

Streamline use of the 'All of Us' Researcher Workbench (<https://www.researchallofus.org/data-tools/workbench/>)with tools to extract and manipulate data from the 'All of Us' database. Increase interoperability with the Observational Health Data Science and Informatics ('OHDSI') tool stack by decreasing reliance of 'All of Us' tools and allowing for cohort creation via 'Atlas'. Improve reproducible and transparent research using 'All of Us'.

Examples of datasets on allometry, the study of the relationship of biological traits to body size. This package contains the dataset of morphological measurement taken from 113 maritime earwigs (Anisolabis maritima) by Matsuzawa and Konuma (2025) <doi:10.1093/biolinnean/blaf031>.

Implementation of the technique of Lleonart et al. 
    (2000) <doi:10.1006/jtbi.2000.2043> to scale body measurements 
    that exhibit an allometric growth. This procedure is a theoretical 
    generalization of the technique used by Thorpe 
    (1975) <doi:10.1111/j.1095-8312.1975.tb00732.x> and
    Thorpe (1976) <doi:10.1111/j.1469-185X.1976.tb01063.x>.

We developed a lightweight machine learning tool for RNA profiling of acute lymphoblastic leukemia (ALL), however, it can be used for any problem where multiple classes need to be identified from multi-dimensional data. The methodology is described in Makinen V-P, Rehn J, Breen J, Yeung D, White DL (2022) Multi-cohort transcriptomic subtyping of B-cell acute lymphoblastic leukemia, International Journal of Molecular Sciences 23:4574, <doi:10.3390/ijms23094574>. The classifier contains optimized mean profiles of the classes (centroids) as observed in the training data, and new samples are matched to these centroids using the shortest Euclidean distance. Centroids derived from a dataset of 1,598 ALL patients are included, but users can train the models with their own data as well. The output includes both numerical and visual presentations of the classification results. Samples with mixed features from multiple classes or atypical values are also identified.