Package: tppcm 0.1.0

Jie-Wen Tsai

tppcm: Step Discriminations for the Two-Parameter Partial Credit Model on Top of 'TAM'

In the two-parameter partial credit model (TPPCM) of Yu (1991) <https://hdl.handle.net/2142/21459>, every step of a polytomous item has its own discrimination. Estimation is left to existing software: 'TAM' (tam.mml.3pl() with a step design matrix, tam.mml.2pl(), tam.mml()) and, for the bilinear product form and non-negativity constraints, 'sirt' (xxirt()). The package adds what these packages do not provide: standard errors of step discriminations based on the full observed information, step-level Wald tests, and score tests with modification indices and expected parameter changes for moving down the model ladder saturated TPPCM -> product form -> generalized partial credit model -> partial credit model, limited-information fit statistics (the M2 statistic, root mean square error of approximation, standardized root mean squared residual, comparative fit index and Tucker-Lewis index; Maydeu-Olivares and Joe (2006) <doi:10.1007/s11336-005-1295-9>) for any step design, step contributions to the item information, and tests of differential step functioning across groups: per-step Lagrange multiplier tests from one multigroup fit (Glas (1998) <https://www3.stat.sinica.edu.tw/statistica/j8n3/j8n32/j8n32.htm>), with the generalized score test of Boos (1992) <doi:10.1080/00031305.1992.10475921>, likelihood ratio tests with configural, metric, scalar and partial multigroup designs, score-based tests (Merkle and Zeileis (2013) <doi:10.1007/s11336-012-9302-4>) and model-based trees (Zeileis, Hothorn and Hornik (2008) <doi:10.1198/106186008X319331>). The generalized partial credit model of Muraki (1992) <doi:10.1177/014662169201600206> is the special case with equal steps within items.

Authors:Jie-Wen Tsai [aut, cre]

tppcm_0.1.0.tar.gz
tppcm_0.1.0.zip(r-4.7-any)tppcm_0.1.0.zip(r-4.6-any)
tppcm_0.1.0.tar.gz(r-4.7-any)tppcm_0.1.0.tar.gz(r-4.6-any)
tppcm_0.1.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
tppcm/json (API)

# Install 'tppcm' in R:
install.packages('tppcm', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/jiewentsai/tppcm/issues

On CRAN:

Conda:

3.00 score 6 scripts 16 exports 13 dependencies

Last updated from:1168ea62ad. Checks:6 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-develOK281
source / vignettesOK265
linux-releaseOK332
windows-develOK116
windows-releaseOK128
wasm-releaseOK159

Exports:dif_testdsfdsf_designget_partsirt_parsitem_testm2miscore_testsim_tppcmstep_infotppcmtppcmtreewald_testxxirt_tppcmxxirt_vcov

Dependencies:admiscCDMlatticeMASSMatrixmvtnormpbapplypbvpolycorRcppRcppArmadillosirtTAM

tppcm recipes
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

Last update: 2026-10-28
Started: 2026-10-28

tppcm: step discriminations on top of TAM
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

Last update: 2026-10-28
Started: 2026-10-28