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
Config/pak/sysreqs: make libbz2-dev liblzma-dev libxml2-dev libssl-dev
        xz-utils zlib1g-dev
Repository: https://bioc.r-universe.dev
Date/Publication: 2026-09-06 17:44:43 UTC
RemoteUrl: https://github.com/bioc/HiCPotts
RemoteRef: HEAD
RemoteSha: 5d3f5d02a29a7a0c9a6b51d55dd2adbb8acf54d0
Packaged: 2026-10-06 13:11:09 UTC; root
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>