Package: HiCPotts
Title: Hierarchical Modeling to Identify and Correct Genomic Biases in
        Hi-C
Version: 1.3.1
Date: 2026-09-06
Authors@R: c(
    person(given = "Itunu. Godwin", family = "Osuntoki",
           email = "hitunes4@gmail.com", role = c("aut", "cre"),
           comment = c(ORCID = "0009-0005-1037-9346")),
    person(given = "Nicolae. Radu", family = "Zabet",
           email = "r.zabet@qmul.ac.uk", role = "aut"))
Description: Bayesian analysis of Hi-C interaction counts using a
    three-state hierarchical mixture model with Potts spatial dependence
    and genomic distance, GC-content, transposable-element and
    accessibility covariates. The three biological components are
    low-mean noise, true signal with a distinct covariate-response
    pattern, and elevated false signal whose covariate-response slopes
    resemble the noise component. Robust regression fitting uses a
    multi-chain soft empirical-Bayes pilot to construct one shared prior
    that is frozen for all production chains, dispersion uses
    component-group-specific Gamma priors,
    zero inflation uses a conjugate augmented Gibbs step, and spatial
    coupling uses retained-state approximate Bayesian computation. The
    official classification pools post-burn-in latent-state frequencies
    from the fitted model; parameter-plus-Potts allocation is retained
    as a separate sensitivity analysis. Parameter summaries include
    posterior intervals, effective sample sizes and split-chain R-hat,
    with configurable diagnostic criteria for reporting. Cached
    native likelihood calculations, direct checkerboard allocation,
    reproducible cross-platform parallel chains, fit provenance and
    stage timings improve computational efficiency and auditability
    without changing the model target or official classification rule.
License: GPL-3 | file LICENSE
Encoding: UTF-8
URL: https://github.com/igosungithub/HiCPotts
BugReports: https://github.com/igosungithub/HiCPotts/issues
biocViews: StatisticalMethod, FunctionalGenomics, GenomeAnnotation,
        GenomeWideAssociation, PeakDetection, DataImport, Spatial,
        Bayesian, Classification, HiddenMarkovModel, Regression
Depends: R (>= 4.5)
Imports: Rcpp (>= 0.11.0), Biostrings, GenomicRanges, IRanges,
        S4Vectors, ggnewscale, parallel, rhdf5, rlang, rtracklayer,
        stats, withr
Suggests: BSgenome, BSgenome.Dmelanogaster.UCSC.dm6, BiocManager,
        BiocStyle, ggplot2 (>= 3.5.0), knitr (>= 1.30), reshape2 (>=
        1.4.4), rmarkdown (>= 2.10), testthat (>= 3.0.0)
LinkingTo: Rcpp, RcppArmadillo
VignetteBuilder: knitr
Config/testthat/edition: 3
NeedsCompilation: yes
Roxygen: list(markdown = TRUE)
RoxygenNote: 7.3.3
git_url: https://git.bioconductor.org/packages/HiCPotts
git_branch: devel
git_last_commit: 5d3f5d0
git_last_commit_date: 2026-09-06
Repository: Bioconductor 3.24
Date/Publication: 2026-09-06
Packaged: 2026-09-06 21:44:04 UTC; biocbuild
Author: Itunu. Godwin Osuntoki [aut, cre] (ORCID:
    <https://orcid.org/0009-0005-1037-9346>),
  Nicolae. Radu Zabet [aut]
Maintainer: Itunu. Godwin Osuntoki <hitunes4@gmail.com>
Built: R 4.6.1; x86_64-apple-darwin20; 2026-09-07 07:02:31 UTC; unix
