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>.

Implements the methodology introduced in Capezza, Lepore, and Paynabar (2025) 
    <doi:10.1080/00401706.2025.2561744> for process monitoring with limited labeling resources. 
    The package provides functions to (i) simulate data streams with true latent states and 
    multivariate Gaussian observations as done in the paper, (ii) fit partially hidden Markov models (pHMMs) 
    using a constrained Baum-Welch algorithm with partial labels, and (iii) perform stream-based 
    active learning that balances exploration and exploitation to decide whether to request labels 
    in real time. The methodology is particularly suited for statistical process monitoring 
    in industrial applications where labeling is costly.

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>.