Reference

Main Functions The most important functions to analyze mass spectrometry data with lots of missing values
median_normalization()	Column wise median normalization of the data matrix
proDA()	Main function to fit the probabilistic dropout model
dist_approx(<proDAFit>) dist_approx(<SummarizedExperiment>) dist_approx(<ANY>)	Distance method for 'proDAFit' object
test_diff() result_names(<proDAFit>)	Identify differentially abundant proteins
Accessor methods Methods to access values of the fit
abundances(<proDAFit>) design(<proDAFit>) hyper_parameters(<proDAFit>) feature_parameters(<proDAFit>) coefficients(<proDAFit>) coefficient_variance_matrices(<proDAFit>) reference_level(<proDAFit>) convergence(<proDAFit>)	Get different features and elements of the 'proDAFit' object
.DollarNames(<proDAFit>) `$`(<proDAFit>) `$<-`(<proDAFit>)	Fluent use of accessor methods
Quick start functions
pd_row_t_test() pd_row_f_test()	Row-wise tests of difference using the probabilistic dropout model
generate_synthetic_data()	Generate a dataset according to the probabilistic dropout model
Low-level statistics functions
pd_lm()	Fit a single linear probabilistic dropout model
invprobit()	Inverse probit function
predict(<proDAFit>)	Predict the parameters or values of additional proteins
Utility function
mply_dbl() msply_dbl()	apply function that always returns a numeric matrix
Class and Package Information
proDAFit-class	proDA Class Definition
proDA_package	proDA: Identify differentially abundant proteins in label-free mass spectrometry
Generic functions Function definitions for S4 Generics
abundances()	Get the abundance matrix
coefficients()	Get the coefficients
coefficient_variance_matrices()	Get the coefficients
convergence()	Get the convergence information
dist_approx()	Calculate an approximate distance for 'object'
feature_parameters()	Get the feature parameters
hyper_parameters()	Get the hyper parameters
reference_level()	Get the reference level
result_names()	Get the result_names
All functions
abundances()	Get the abundance matrix
abundances(<proDAFit>) design(<proDAFit>) hyper_parameters(<proDAFit>) feature_parameters(<proDAFit>) coefficients(<proDAFit>) coefficient_variance_matrices(<proDAFit>) reference_level(<proDAFit>) convergence(<proDAFit>)	Get different features and elements of the 'proDAFit' object
.DollarNames(<proDAFit>) `$`(<proDAFit>) `$<-`(<proDAFit>)	Fluent use of accessor methods
coefficients()	Get the coefficients
coefficient_variance_matrices()	Get the coefficients
convergence()	Get the convergence information
dist_approx(<proDAFit>) dist_approx(<SummarizedExperiment>) dist_approx(<ANY>)	Distance method for 'proDAFit' object
dist_approx()	Calculate an approximate distance for 'object'
feature_parameters()	Get the feature parameters
generate_synthetic_data()	Generate a dataset according to the probabilistic dropout model
hyper_parameters()	Get the hyper parameters
invprobit()	Inverse probit function
median_normalization()	Column wise median normalization of the data matrix
mply_dbl() msply_dbl()	apply function that always returns a numeric matrix
pd_lm()	Fit a single linear probabilistic dropout model
pd_row_t_test() pd_row_f_test()	Row-wise tests of difference using the probabilistic dropout model
predict(<proDAFit>)	Predict the parameters or values of additional proteins
proDA()	Main function to fit the probabilistic dropout model
proDAFit-class	proDA Class Definition
proDA_package	proDA: Identify differentially abundant proteins in label-free mass spectrometry
reference_level()	Get the reference level
test_diff() result_names(<proDAFit>)	Identify differentially abundant proteins