abundances | Get the abundance matrix |
accessor_methods | Get different features and elements of the 'proDAFit' object |
as_replicate | Get numeric vector with the count of the replicate for each... |
cash-proDAFit-method | Fluent use of accessor methods |
coefficients | Get the coefficients |
coefficient_variance_matrices | Get the coefficients |
convergence | Get the convergence information |
distance_sq | Square distance between two Gaussian distributions |
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... |
grapes-zero_dom_mat_mult-grapes | Helper function that makes sure that NA * 0 = 0 in matrix... |
hyper_parameters | Get the hyper parameters |
invprobit | Inverse probit function |
invprobit_fast | Same thing as invprobit, but without the parameter validation |
median_normalization | Column wise median normalization of the data matrix |
mply_dbl | apply function that always returns a numeric matrix |
pd_lm | Fit a single linear probabilistic dropout model |
pd_lm.fit | The work horse for fitting the probabilistic dropout model |
pd_row_t_test | Row-wise tests of difference using the probabilistic dropout... |
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... |
reference_level | Get the reference level |
result_names | Get the result_names |
test_diff | Identify differentially abundant proteins |
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