A. Data and estimation | A1. Getting raw questionnaire data into shape | A2. Fitting the saturated TPPCM | A3. GPCM, step model, PCM and custom restrictions | A4. Items with different numbers of categories | A5. Rank-1 (product form) and the lower bound of the discriminations | B. Reports and components | B1. One parameter table | B2. Standard errors and confidence intervals | B3. Getting at the components | B4. Information functions | C. Tests | C1. Which items have unequal step discriminations | C2. Is the GPCM enough | C3. Likelihood-ratio tests of nested models | C4. Overall fit | D. Differential item and step functioning | D1. Known groups: which step has DIF | D2. A covariate related to ability: impact is not DIF | D3. Numeric and ordered covariates | D4. Unknown grouping: a DIF tree | D5. Which step functions differently between known groups (DSF) | E. Simulation, unsupported models, weights and groups | E1. Simulating data and estimating power | E2. Models the package does not handle | E3. Weights, multiple groups and latent regression

1. The model and the package | The two-parameter partial credit model | What the package does and does not do | 2. Estimating the TPPCM with TAM | Data | The saturated TPPCM | When tam.mml.3pl() does not converge | The common designs | Restrictions at the step level | 3. Reports, standard errors, fit and information | irt_pars(): a compact parameter table | Standard errors | Overall fit: m2() | Information: step_info() | 4. Tests | Per-item Wald tests: are the step discriminations of this item equal? | Score tests from the GPCM | 5. Rank-1: the product form ~ item + step | 6. Differential step functioning | Impact is not DIF: model the group distributions first | A DSF workflow for known groups | Why the joint test is the screen | Why fit the TPPCM rather than the GPCM? | tppcmtree(): DIF trees | Example: verbal aggression | 7. get_parts(): the mathematical components | 8. Suggested workflow and caveats | Workflow | Caveats | References