Package: cta 1.3.0

Qiansheng Zhu

cta: Contingency Table Analysis Based on ML Fitting of MPH Models

Contingency table analysis is performed based on maximum likelihood (ML) fitting of multinomial-Poisson homogeneous (MPH) and homogeneous linear predictor (HLP) models. See Lang (2004) <doi:10.1214/aos/1079120140> and Lang (2005) <doi:10.1198/016214504000001042> for MPH and HLP models. Objects computed include model goodness-of-fit statistics; likelihood- based (cell- and link-specific) residuals; and cell probability and expected count estimates along with standard errors. This package can also compute test-inversion--e.g. Wald, profile likelihood, score, power-divergence--confidence intervals for contingency table estimands, when table probabilities are potentially subject to equality constraints. For test-inversion intervals, see Lang (2008) <doi:10.1002/sim.3391> and Zhu (2020) <doi:10.17077/etd.005331>.

Authors:Joseph B. Lang [aut], Qiansheng Zhu [aut, cre]

cta_1.3.0.tar.gz
cta_1.3.0.tar.gz(r-4.5-noble)cta_1.3.0.tar.gz(r-4.4-noble)
cta_1.3.0.tgz(r-4.4-emscripten)cta_1.3.0.tgz(r-4.3-emscripten)
cta.pdf |cta.html✨
cta/json (API)

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

Peer review:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

29 exports 0.00 score 6 dependencies 269 downloads

Last updated 3 years agofrom:7970b1ced0. Checks:OK: 2. Indexed: yes.

TargetResultDate
Doc / VignettesOKAug 25 2024
R-4.5-linuxOKAug 25 2024

Exports:block.fctcheck.HLPcheck.homogcheck.zero.order.homogci.tablecompute_cons_MLE_asecreate.Ucreate.Z.ZFdiff_Gsq_nrdiff_Gsq_robustdiff_PD_nrdiff_PD_robustdiff_Xsq_nrdiff_Xsq_robustf.psiM.fctmph.fitmph.summarynested_Gsq_nrnested_Gsq_robustnested_PD_nrnested_PD_robustnested_Xsq_nrnested_Xsq_robustnum.deriv.fctquadratic.fitsolve_quadraticWald_trans.Wald_nrWald_trans.Wald_robust

Dependencies:intervalslimSolvelpSolveMASSnumDerivquadprog

Readme and manuals

Help Manual

Help pageTopics
cta: Contingency Table Analysis Based on ML Fitting of MPH Modelscta-package
Matrix Direct Sumblock.fct
HLP Link Status Checkcheck.HLP
Z Homogeneity Checkcheck.homog
Zero-Order Z Homogeneity Checkcheck.zero.order.homog
Test-Inversion CIs for Estimands in Contingency Tablesci.table
Constrained MLE and ASEcompute_cons_MLE_ase
Orthogonal Complement of the Column Space of a Matrixcreate.U
Population Matrix and Sampling Constraint Matrixcreate.Z.ZF
Difference in G-Squared Statistic Based CIs (Non-Robust)diff_Gsq_nr
Difference in G-Squared Statistic Based CIs (Robust)diff_Gsq_robust
Difference in Power-Divergence Statistic Based CIs (Non-Robust)diff_PD_nr
Difference in Power-Divergence Statistic Based CIs (Robust)diff_PD_robust
Difference in X-Squared Statistic Based CIs (Non-Robust)diff_Xsq_nr
Difference in X-Squared Statistic Based CIs (Robust)diff_Xsq_robust
Model Comparison Statisticsf.psi
Marginalizing Matrix Based on Strata InformationM.fct
Fitting MPH and HLP Modelsmph.fit
Summary Statistics of the Fitted MPH Modelmph.summary
Nested G-Squared Statistic Based CIs (Non-Robust)nested_Gsq_nr
Nested G-Squared Statistic Based CIs (Robust)nested_Gsq_robust
Nested Power-Divergence Statistic Based CIs (Non-Robust)nested_PD_nr
Nested Power-Divergence Statistic Based CIs (Robust)nested_PD_robust
Nested X-Squared Statistic Based CIs (Non-Robust)nested_Xsq_nr
Nested X-Squared Statistic Based CIs (Robust)nested_Xsq_robust
Numerical Derivatives Based on Central Difference Formulanum.deriv.fct
Quadratic Fitquadratic.fit
Solve for Real Root(s) to the Quadratic Equationsolve_quadratic
Wald-Type CIs (Non-Robust)Wald_trans.Wald_nr
Wald-Type CIs (Robust)Wald_trans.Wald_robust