Introduction | The basics | Tuning the mutational modeling | Tuning the feature set choice and filtering | Improving estimates | Correcting for dropout | Inferring background mutation rates from -label data | Fancy functionalities | Hierarchical mutation rate model | Isoform deconvolution | Using the Apache Arrow backend | Step 1: Create an Arrow Dataset | Step 2: Create EZbakRArrowData object | Step 3: Run EstimateFractions like normal | Simulating the Arrow workflow

Introduction | Quickstart | Modeling cytoplasmic to nuclear flow | Modeling nuclear degradation | Multi-label time modeling | Modeling pre- and mature RNA dynamics | Cytoplasmic to nuclear flow of pre- and mature RNA | Performing comparative analyses | Taking a deeper dive | Running EZDynamics | The graph | Identifiability | The formulas | Example 1: nuclear and cytoplasmic fractionation | Example 2: pre-RNA dynamics | Example 3: preRNA/mature RNA nuclear/cytoplasmic dynamics | The ode_models object | Normalization | SparkNotes | The details | Uncertainty quantification

Introduction | EZQC | EZQC output | What to do if the QC looks iffy? | Characteristics of a good NR-seq experiment:

Introduction | The basics | Running the standard kdeg and ksyn estimation | What is the "standard" strategy? | Short feed analyses | How to run it | What is the "shortfeed" strategy? | Non-steady-state (NSS) analyses | Requirements of the "NSS" strategy | What is the "NSS" strategy? | Pulse-chase analyses

Necessary Setup | Step 0: Create an EZbakRData Object | Step 1: EstimateFractions() | Step 2: EstimateKinetics() | Step 3: AverageAndRegularize() | Step 4: CompareParameters()

Introduction | EZbakRData objects | Using EZget

Introduction | Example scenarios | The simplest case: a single "condition" factor | Dealing with multiple factors: simple interaction model | Dealing with multiple factors: estimating an actual interaction term | Regressing out potential batch effects