R Development Page
Contributed R Packages
Below is a list of all packages provided by project ctm.
Important note for package
binaries: R-Forge provides these binaries only for
the most recent version of R, but not for older
versions. In order to successfully install the
packages provided on R-Forge, you have to switch
to the most recent version of R or, alternatively,
install from the package sources (.tar.gz).
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basefun | Infrastructure for Computing with Basis Functions
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Some very simple infrastructure for basis functions. |
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Version: 1.1-4 |
Last change: 2023-05-16 16:39:41+02 |
Rev.: 2149 |
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Download:
 (.tar.gz) |
 (.zip) |
Build status: Current | Stable Release: Get basefun 1.1-4 from CRAN |
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R install command:
install.packages("basefun", repos="http://R-Forge.R-project.org") |
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cotram | Count Transformation Models
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Count transformation models featuring
parameters interpretable as discrete hazard ratios, odds ratios,
reverse-time discrete hazard ratios, or transformed expectations.
An appropriate data transformation for a count outcome and
regression coefficients are simultaneously estimated by maximising
the exact discrete log-likelihood using the computational framework
provided in package mlt, technical details are given in
Siegfried & Hothorn (2020) .
The package also contains an experimental implementation of
multivariate count transformation models with an application
to multi-species distribution models . |
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Version: 0.5-0 |
Last change: 2023-09-05 10:38:11+02 |
Rev.: 2259 |
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Download:
(.tar.gz) |
(.zip) |
Build status: Current | Stable Release: Get cotram 0.4-4 from CRAN |
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R install command:
install.packages("cotram", repos="http://R-Forge.R-project.org") |
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ctm | Conditional Transformation Models
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An experimental implementation of conditional transformation
models for the semiparametric estimation of conditional distribution
functions. Contains example analyses and a simulation study. |
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Version: 0.0-3 |
Last change: 2013-11-11 14:50:25+01 |
Rev.: 59 |
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Download:
(.tar.gz) |
(.zip) |
Build status: Current |
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R install command:
install.packages("ctm", repos="http://R-Forge.R-project.org") |
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mlt | Most Likely Transformations
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Likelihood-based estimation of conditional transformation
models via the most likely transformation approach described in
Hothorn et al. (2018) and Hothorn (2020)
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Version: 1.4-9 |
Last change: 2023-08-21 11:20:14+02 |
Rev.: 2247 |
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Download:
(.tar.gz) |
(.zip) |
Build status: Current | Stable Release: Get mlt 1.4-9 from CRAN |
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R install command:
install.packages("mlt", repos="http://R-Forge.R-project.org") |
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mlt.docreg | Most Likely Transformations: Documentation and Regression Tests
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Additional documentation, a package vignette and
regression tests for package mlt. |
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Version: 1.1-7 |
Last change: 2023-08-28 14:01:14+02 |
Rev.: 2255 |
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Download:
(.tar.gz) |
(.zip) |
Build status: Current | Stable Release: Get mlt.docreg 1.1-7 from CRAN |
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R install command:
install.packages("mlt.docreg", repos="http://R-Forge.R-project.org") |
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tbm | Transformation Boosting Machines
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Boosting the likelihood of conditional and shift transformation models as introduced in \doi{10.1007/s11222-019-09870-4}. |
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Version: 0.3-5 |
Last change: 2022-01-14 09:19:13+01 |
Rev.: 1766 |
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Download:
(.tar.gz) |
(.zip) |
Build status: Current | Stable Release: Get tbm 0.3-5 from CRAN |
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R install command:
install.packages("tbm", repos="http://R-Forge.R-project.org") |
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tram | Transformation Models
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Formula-based user-interfaces to specific transformation models
implemented in package mlt. 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, ). The underlying theory
is described in Hothorn et al. (2018) . An extension to
transformation models for clustered data is provided (Barbanti and Hothorn, 2022,
). Multivariate conditional transformation models
(Klein et al, 2022, ) and shift-scale transformation models (Siegfried et al, 2023,
) can be fitted as well. |
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Version: 1.0-0 |
Last change: 2023-08-25 12:40:22+02 |
Rev.: 2254 |
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Download:
(.tar.gz) |
(.zip) |
Build status: Current | Stable Release: Get tram 1.0-0 from CRAN |
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R install command:
install.packages("tram", repos="http://R-Forge.R-project.org") |
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tramME | Transformation Models with Mixed Effects
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Likelihood-based estimation of mixed-effects transformation models
using the Template Model Builder (TMB, Kristensen et al., 2016)
. The technical details of transformation models
are given in Hothorn et al. (2018) . Likelihood
contributions of exact, randomly censored (left, right, interval) and
truncated observations are supported. The random effects are assumed to be
normally distributed on the scale of the transformation function, the
marginal likelihood is evaluated using the Laplace approximation, and the
gradients are calculated with automatic differentiation (Tamasi & Hothorn,
2021) . Penalized smooth shift terms can be
defined using mgcv. |
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Version: 1.0.4 |
Last change: 2023-04-04 13:49:07+02 |
Rev.: 2108 |
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Download:
(.tar.gz) |
(.zip) |
Build status: Current | Stable Release: Get tramME 1.0.5 from CRAN |
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R install command:
install.packages("tramME", repos="http://R-Forge.R-project.org") |
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tramnet | Penalized Transformation Models
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Partially penalized versions of specific transformation models
implemented in package mlt. Available models include a fully parametric version
of the Cox model, other parametric survival models (Weibull, etc.), models for
binary and ordered categorical variables, normal and transformed-normal (Box-Cox type)
linear models, and continuous outcome logistic regression. Hyperparameter tuning
is facilitated through model-based optimization functionalities from package mlrMBO.
The accompanying vignette describes the methodology used in tramnet in detail.
Transformation models and model-based optimization are described in
Hothorn et al. (2019) and
Bischl et al. (2016) , respectively. |
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Version: 0.0-8 |
Last change: 2023-03-10 18:24:22+01 |
Rev.: 2093 |
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Download:
(.tar.gz) |
(.zip) |
Build status: Current | Stable Release: Get tramnet 0.0-8 from CRAN |
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R install command:
install.packages("tramnet", repos="http://R-Forge.R-project.org") |
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tramvs | Optimal Subset Selection for Transformation Models
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Greedy optimal subset selection for transformation models
(Hothorn et al., 2018, ) based on the abess
algorithm (Zhu et al., 2020, ). Applicable to
models from packages tram and cotram. |
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Version: 0.0-5 |
Last change: 2023-09-14 09:25:34+02 |
Rev.: 2260 |
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Download:
(.tar.gz) |
(.zip) |
Build status: Current | Stable Release: Get tramvs 0.0-4 from CRAN |
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R install command:
install.packages("tramvs", repos="http://R-Forge.R-project.org") |
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variables | Variable Descriptions
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Abstract descriptions of (yet) unobserved variables. |
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Version: 1.1-1 |
Last change: 2021-06-17 10:49:33+02 |
Rev.: 1683 |
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Download:
(.tar.gz) |
(.zip) |
Build status: Current | Stable Release: Get variables 1.1-1 from CRAN |
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R install command:
install.packages("variables", repos="http://R-Forge.R-project.org") |
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Build status codes
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