Overview | Convert to titer workflow | Config | 4PL function: OD = D + (A - D) / (1 + 10^((log10(conc) - c) * b)) | Assign each sample a "true" concentration (UI/ml) | ~60% positive (conc > 0.1), ~40% negative | Negative control concentrations per plate (one fixed OD per plate per dilution) | Antitoxin | Generate one row per sample × dilution, compute OD via 4PL + noise | Custom models

Imperfect test | Fitting corrected data

Age-time varying model

Generate models comparison data.frame | Generate custom metrics

Visualize model | Customize the plot | Built-in function | ggplot2 functions

Mixture model

Local estimation by polynomial

Frequentist methods | Polynomial models | Fractional polynomial model | Nonlinear models | Farrington model | Weibull model | Bayesian methods | Farrington | Log-logistic

Penalized splines | Penalized likelihood framework | Generalized Linear Mixed Model framework

Aggregate data

Input data format | Data transformation

1. Simple SIR model with gamma distributed lengths of stay | 2. How the algorithm work? | 3. Waiting time distribution | 4. Multiple transitions from a compartment | 5. Additional examples | Example 1 | Example 2

Migrate deSolve code to denim | Original code in deSolve | Model definition | Model configurations | Simulation | Compare output

Model definition in denim | Denim DSL | Define a classic SIR model | Time varying transition | R list

1. Transition to multiple states in denim | 2. Multinomial in denim | 2.1. Model definition | Proportion normalization | 2.2. Example model | Model definition in denim | Equivalent model definition in deSolve | Run simulation and compare | 3. Competing risks in denim | 3.1. Model definition | 3.2. Example model

Non-parametric vs parametric | Example 1 | Example 2