
Package index
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MVgen() - Amputation of a dataset
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check.conditions() - Check if the design is valid
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check.design() - Check if the design is valid
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datasim - A single simulated dataset
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eBayes.mod() - MI-aware Modifed eBayes Function
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formatLimmaResult() - Format a Result from Limma
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hid.ebayes() - MI-aware Modifed eBayes Function
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limmaCompleteTest.mod() - Computes a hierarchical differential analysis
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make.contrast() - Builds the contrast matrix
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make.design.1() - Builds the design matrix for designs of level 1
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make.design.2() - Builds the design matrix for designs of level 2
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make.design.3() - Builds the design matrix for designs of level 3
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make.design() - Builds the design matrix
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meanImp_emmeans() - Multiple Imputation Estimate
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mi4limma() - Differential analysis after multiple imputation
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mm_peptides - mm_peptides - peptide-level intensities for mouse
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multi.impute() - Multiple imputation of quantitative proteomics datasets
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norm.200.m100.sd1.vs.m200.sd1.list - A list of simulated datasets.
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proj_matrix() - Variance-Covariance Matrix Projection
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protdatasim() - Data simulation function
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qData - Extract of the abundances of Exp1_R25_pept dataset
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rubin1.all() - First Rubin rule (all peptides)
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rubin1.one() - First Rubin rule (a given peptide)
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rubin2.all() - Computes the 2nd Rubin's rule (all peptides)
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rubin2bt.all() - 2nd Rubin's rule Between-Imputation component (all peptides)
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rubin2bt.one() - 2nd Rubin's rule Between-Imputation Component (a given peptide)
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rubin2wt.all() - 2nd Rubin's rule Within-Variance Component (all peptides)
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rubin2wt.one() - 2nd Rubin's rule Within-Variance Component (a given peptide)
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sTab - Experimental design for the Exp1_R25_pept dataset
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test.design() - Check if xxxxxx
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within_variance_comp_emmeans() - Multiple Imputation Within Variance Component