Frequentist statistical inference for cluster randomised trials with
  multiple outcomes that controls the family-wise error rate and provides nominal
  coverage of confidence sets. A full description of the methods can be found in Watson et al. (2023) <doi:10.1002/sim.9831>.

Provides a new method for interpretable heterogeneous 
    treatment effects characterization in terms of decision rules 
    via an extensive exploration of heterogeneity patterns by an 
    ensemble-of-trees approach, enforcing high stability in the 
    discovery. It relies on a two-stage pseudo-outcome regression, and 
    it is supported by theoretical convergence guarantees. Bargagli-Stoffi, 
    F. J., Cadei, R., Lee, K., & Dominici, F. (2023) Causal rule ensemble: 
    Interpretable Discovery and Inference of Heterogeneous Treatment Effects.  
    arXiv preprint <doi:10.48550/arXiv.2009.09036>.

Provides 'R' utilities to build unlevered and levered discounted cash
    flow (DCF) tables for commercial real estate (CRE) assets. Functions generate
    bullet and amortising debt schedules, compute credit metrics such as debt
    coverage ratios (DCR), debt service coverage ratios (DSCR), interest coverage
    ratios, debt yield ratios, and forward loan-to-value ratios (LTV) based on net
    operating income (NOI). The toolkit evaluates refinancing feasibility under
    alternative market scenarios and supports end-to-end scenario execution from a
    YAML (YAML Ain't Markup Language) configuration file parsed with 'yaml'.
    Includes helpers for sensitivity analysis, covenant diagnostics, and
    reproducible vignettes.

Provides a new method for identification of clusters of genomic
 regions within chromosomes. Primarily, it is used for calling clusters of 
 cis-regulatory elements (COREs). 'CREAM' uses genome-wide maps of genomic regions
 in the tissue or cell type of interest, such as those generated from chromatin-based 
 assays including DNaseI, ATAC or ChIP-Seq. 'CREAM' considers proximity of the elements 
 within chromosomes of a given sample to identify COREs in the following steps:
 1) It identifies window size or the maximum allowed distance between the elements 
 within each CORE, 2) It identifies number of elements which should be clustered 
 as a CORE, 3) It calls COREs, 4) It filters the COREs with lowest order which 
 does not pass the threshold considered in the approach.