Package: tram 1.2-2
tram: Transformation Models
Formula-based user-interfaces to specific transformation models implemented in package 'mlt' (<doi:10.32614/CRAN.package.mlt>, <doi:10.32614/CRAN.package.mlt.docreg>). Available models include Cox models, some parametric survival models (Weibull, etc.), models for ordered categorical variables, normal and non-normal (Box-Cox type) linear models, and continuous outcome logistic regression (Lohse et al., 2017, <doi:10.12688/f1000research.12934.1>). The underlying theory is described in Hothorn et al. (2018) <doi:10.1111/sjos.12291>. An extension to transformation models for clustered data is provided (Barbanti and Hothorn, 2022, <doi:10.1093/biostatistics/kxac048>). Multivariate conditional transformation models (Klein et al, 2022, <doi:10.1111/sjos.12501>) and shift-scale transformation models (Siegfried et al, 2023, <doi:10.1080/00031305.2023.2203177>) can be fitted as well. The package contains an implementation of the tram-GCM test, a doubly robust score test, described in Kook et al. (2024, <doi:10.1080/01621459.2024.2395588>).
Authors:
tram_1.2-2.tar.gz
tram_1.2-2.zip(r-4.5)tram_1.2-2.zip(r-4.4)
tram_1.2-2.tgz(r-4.4-any)
tram_1.2-2.tar.gz(r-4.5-noble)tram_1.2-2.tar.gz(r-4.4-noble)
tram_1.2-2.tgz(r-4.4-emscripten)
tram.pdf |tram.html✨
tram/json (API)
NEWS
# Install 'tram' in R: |
install.packages('tram', repos = c('https://r-forge.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://r-forge.r-project.org/projects/ctm
Last updated 8 hours agofrom:cd75b22816. Checks:5 OK. Indexed: yes.
Target | Result | Latest binary |
---|---|---|
Doc / Vignettes | OK | Jan 20 2025 |
R-4.5-win | OK | Jan 20 2025 |
R-4.5-linux | OK | Jan 20 2025 |
R-4.4-win | OK | Jan 20 2025 |
R-4.4-mac | OK | Jan 20 2025 |
Exports:AaregBoxCoxColrComprisCoxphL1LehmannLmMmltmtramOVLperm_testPIPolrrobust_score_testROCscore_testSurvregtramtram_dataTV
Dependencies:alabamabasefunBBcodetoolsconeprojFormulalatticeMASSMatrixmltmultcompmvtnormnloptrnumDerivorthopolynompolynomquadprogRcppRcppArmadillosandwichsurvivalTH.datavariableszoo