R Package for fitting penalized ordinal models using GMIFS

  • Link to ordinalgmifs package available from the Comprehensive R Archive Network for fitting cumulative, adjacent category, forward and backward continuation ratio, and stereotype ordinal response models for the high-dimensional data setting.

    • R Package for fitting mixed effects ordinal response models

  • We have developed an R package to invoke the MIXOR Fortran stand-alone program.
    Link to mixor R package that includes a vignette illustrating usage.

    • R Packages for fitting penalized constrained continuation ratio models

  • Link to glmnetcr package available from the Comprehensive R Archive Network
  • Link to glmpathcr package available from the Comprehensive R Archive Network

    • R Package for detecting batch effects using guided PCA

  • Download gPCA_1.0.tar.gz

    • R package for fitting classification trees to ordinal response data

    Link to rpartOrdinal package available from the Comprehensive R Archive Network

    Stand alone application for 3':5' Quality Assessment

    Installation instructions
    Application: PixelAnalyzer.zip
    GDAC Files Runtime (Affymetrix, Inc.)
    PixelAnalyzer Source code

    Detection Call Algorithms for High-throughput Gene Expression Microarray Data

    Affy zip file 1
    Affy zip file 2
    Affy zip file 3
    Affy zip file 4

    Non-parametric meta-analysis approach for combining independent microarray datasets

    Test CEL 1
    Test CEL 2
    Test CEL 3
    Test CEL 4
    Test CEL 5
    Test CEL 6

    Measurement error models for estimating cross-platform correlations and gene-specific reliabilities

    Supplementary Material
    Stratagene Technical Replicates:
    QAQC8.CEL
    QAQC10.CEL
    QAQC13.CEL
    QAQC16.CEL
    GMU1420.txt
    GMU1421.txt
    GMU1422.txt
    C3B_7-7.txt
    C3B_8-22.txt
    C3B_8-31.txt

    Breast/Ovarian Samples:
    VBR29.CEL
    VBR36.CEL
    VBR42.CEL
    VBR43.CEL
    VBR44.CEL
    VBR45.CEL
    VBR46.CEL
    VBR47.CEL
    VOV1.CEL
    VOV2.CEL
    VOV3.CEL
    VOV4.CEL
    VOV5.CEL
    VOV7.CEL
    VOV8.CEL
    VOVTB.CEL
    VBR29_C3B.txt
    VBR36_C3B.txt
    VBR42_C3B.txt
    VBR43_C3B.txt
    VBR44_C3B.txt
    VBR45_C3B.txt
    VBR46_C3B.txt
    VBR47_C3B.txt
    OVO1_C3B.txt
    OVO2_C3B.txt
    OVO3_C3B.txt
    OVO4_C3B.txt
    OVO5_C3B.txt
    OVO7_C3B.txt
    OVO8_C3B.txt
    OVOTB_C3B.txt

    Goodness-of-fit test for logistic regression models estimated using complex sample survey data

    STATA ado program svylogitgof
    STATA help svylogitgof
    README for svylogitgof

    Quantifying the effects of highly correlated covariates and varying levels of strength of association on variable importance estimates from random forests

    Supplementary Material
    B- versus T-lineage ALL:
    Probe sets with significant RF variable importance estimates

    Mixed Effects Models for Assessing RNA Quality

    Supplementary Material
    Pixel level data
    Renal Cell Samples:
    NA-F
    NA-U
    NB-F
    ND-U
    TA-U
    TA-F1
    TA-F2
    TB-F
    TD-U

    Ovarian Samples:
    Ovarian1_G
    Ovarian2_G
    Ovarian3_G
    Ovarian4_G
    Ovarian5_G
    Ovarian1_I
    Ovarian2_I
    Ovarian3_I
    Ovarian4_I
    Ovarian5_I