.DollarNames.proDAFit   Fluent use of accessor methods
abundances              Get the abundance matrix
accessor_methods        Get different features and elements of the
                        'proDAFit' object
coefficient_variance_matrices
                        Get the coefficients
coefficients            Get the coefficients
convergence             Get the convergence information
dist_approx             Calculate an approximate distance for 'object'
dist_approx_impl        Distance method for 'proDAFit' 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
pd_lm                   Fit a single linear probabilistic dropout model
pd_row_t_test           Row-wise tests of difference using the
                        probabilistic dropout model
predict,proDAFit-method
                        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
result_names            Get the result_names
test_diff               Identify differentially abundant proteins
