The active subspace method is a sensitivity analysis technique that finds important linear combinations of input variables for a simulator. This package provides functions allowing estimation of the active subspace without gradient information using Gaussian processes as well as sequential experimental design tools to minimize the amount of data required to do so. Implements Wycoff et al. (JCGS, 2021) <doi:10.48550/arXiv.1907.11572>.

Framework for analysing multiple omics datasets in the context of molecular pathways, biological processes and other types of gene sets. The package uses p-value merging to combine gene- or protein-level signals, followed by ranked hypergeometric tests to determine enriched pathways and processes. Genes can be integrated using directional constraints that reflect how the input datasets are expected interact with one another. This approach allows researchers to interpret a series of omics datasets in the context of known biology and gene function, and discover associations that are only apparent when several datasets are combined. The recent version of the package is part of the following publication: Directional integration and pathway enrichment analysis for multi-omics data. Slobodyanyuk M^, Bahcheli AT^, Klein ZP, Bayati M, Strug LJ, Reimand J. Nature Communications (2024) <doi:10.1038/s41467-024-49986-4>.

Provides functions to express clock time data relative to 
    anchor points (typically solar); fit kernel density functions to animal 
    activity time data; plot activity distributions; quantify overall 
    levels of activity; statistically compare activity metrics through 
    bootstrapping; evaluate variation in linear variables with time (or 
    other circular variables).

Reads raw 'accelerometry' from 'GT3X+' data and 
    plain table data to calculate Activity Index from 'Bai et al.' (2016)
    <doi:10.1371/journal.pone.0160644>.  The Activity Index refers to the 
    square root of the second-level average variance of the three
    'accelerometry' axes.