• Current version
  • Actively used in methylome analysis
  • In development
    • 06-23-24
      • New functions added to identify DMRs and others accumulative improvements.
  • Current version
  • Actively used in methylome analysis
  • In development
    • 01-15-23:
      • Update function ‘uniqueGRfilterByCov’ adding parameter ‘and.min.cov’. To get the results with previous default parameter set: and.min.cov = FALSE
    • 02-07-23:
      • Add Random Forest algorithm to functions ‘evaluateDIMPclass’ and ‘estimateCutPoint’.
  • Current version
  • Actively used in methylome analysis
  • In development
    • 05-12-22:
      • Add a new functions getDMGs wrapping several steps of DMG estimations.
      • Replace function ‘data.table’ with functions from dplyr package.
  • Current version
  • Actively used in methylome analysis
  • In development
    • 04-06-22:
      • Fixed issues with parallel computation.
    • 11-04-21:
      1) Upgrading corresponding to R version 4.1.1.
      - Improvement of parallel computation - Improvement on some non-linear algorithms.
  • Actively used in methylome analysis
  • In development
    • 07-14-21:
      1) Upgrading corresponding to R version 4.1.0.
      - Prevent potential crash originated by changes in some R packages internally used by Methyl-IT.
      - J-Divergence is now available in estimateCutPoint function
  • Actively used in methylome analysis
  • In development
    • 06-09-20:
      1) Upgrading corresponding to R version 4.0.0.
      - Prevent potential crash originated by changes in some R packages internally used by Methyl-IT.
  • 04-17-20:
    1) A new vignette added:
    - DMR detection with Methyl-IT
  • 03-08-20:
    1) Two new vignette added:
    - Principal Components and Linear Discriminant Analyses with Methyl-IT
    - Association Between Gene Expression and Cytosine DNA Methylation at gene-body
  • 03-23-20:
    1) Update documentation and code style reformatted

New features

  • Improvement of the ‘countTest’ function applied for DMG identification. The new version was named ‘countTest2

  • Added function ‘gofReport’ search for the best fitted model between the set of models requested by the user.

  • The functions using machine-learning algorithms were improved

  • The documentation was notably improved