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mvtnorm - Multivariate Normal and t Distributions

Computes multivariate normal and t probabilities, quantiles, random deviates, and densities. Log-likelihoods for multivariate Gaussian models and Gaussian copulae parameterised by Cholesky factors of covariance or precision matrices are implemented for interval-censored and exact data, or a mix thereof. Score functions for these log-likelihoods are available. A class representing multiple lower triangular matrices and corresponding methods are part of this package.

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openblasglibc

15.19 score 2.8k dependents 19k scripts 448k downloads

multcomp - Simultaneous Inference in General Parametric Models

Simultaneous tests and confidence intervals for general linear hypotheses in parametric models, including linear, generalized linear, linear mixed effects, and survival models. The package includes demos reproducing analyzes presented in the book "Multiple Comparisons Using R" (Bretz, Hothorn, Westfall, 2010, CRC Press).

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13.88 score 387 dependents 14k scripts 168k downloads

coin - Conditional Inference Procedures in a Permutation Test Framework

Conditional inference procedures for the general independence problem including two-sample, K-sample (non-parametric ANOVA), correlation, censored, ordered and multivariate problems described in <doi:10.18637/jss.v028.i08>.

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13.20 score 83 dependents 2.2k scripts 44k downloads

mboost - Model-Based Boosting

Functional gradient descent algorithm (boosting) for optimizing general risk functions utilizing component-wise (penalised) least squares estimates or regression trees as base-learners for fitting generalized linear, additive and interaction models to potentially high-dimensional data. Models and algorithms are described in <doi:10.1214/07-STS242>, a hands-on tutorial is available from <doi:10.1007/s00180-012-0382-5>. The package allows user-specified loss functions and base-learners.

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boosting-algorithmsgamglmmachine-learningmboostmodellingr-languagetutorialsvariable-selectionopenblas

12.57 score 77 stars 25 dependents 770 scripts 7.0k downloads

multcomp - Simultaneous Inference in General Parametric Models

Simultaneous tests and confidence intervals for general linear hypotheses in parametric models, including linear, generalized linear, linear mixed effects, and survival models. The package includes demos reproducing analyzes presented in the book "Multiple Comparisons Using R" (Bretz, Hothorn, Westfall, 2010, CRC Press).

Last updated

12.40 score 387 dependents 14k scripts 168k downloads

partykit - A Toolkit for Recursive Partytioning

A toolkit with infrastructure for representing, summarizing, and visualizing tree-structured regression and classification models. This unified infrastructure can be used for reading/coercing tree models from different sources ('rpart', 'RWeka', 'PMML') yielding objects that share functionality for print()/plot()/predict() methods. Furthermore, new and improved reimplementations of conditional inference trees (ctree()) and model-based recursive partitioning (mob()) from the 'party' package are provided based on the new infrastructure. A description of this package was published by Hothorn and Zeileis (2015) <https://jmlr.org/papers/v16/hothorn15a.html>.

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12.38 score 95 dependents 2.5k scripts 21k downloads

coin - Conditional Inference Procedures in a Permutation Test Framework

Conditional inference procedures for the general independence problem including two-sample, K-sample (non-parametric ANOVA), correlation, censored, ordered and multivariate problems described in <doi:10.18637/jss.v028.i08>.

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11.43 score 83 dependents 2.2k scripts 44k downloads

party - A Laboratory for Recursive Partytioning

A computational toolbox for recursive partitioning. The core of the package is ctree(), an implementation of conditional inference trees which embed tree-structured regression models into a well defined theory of conditional inference procedures. This non-parametric class of regression trees is applicable to all kinds of regression problems, including nominal, ordinal, numeric, censored as well as multivariate response variables and arbitrary measurement scales of the covariates. Based on conditional inference trees, cforest() provides an implementation of Breiman's random forests. The function mob() implements an algorithm for recursive partitioning based on parametric models (e.g. linear models, GLMs or survival regression) employing parameter instability tests for split selection. Extensible functionality for visualizing tree-structured regression models is available. The methods are described in Hothorn et al. (2006) <doi:10.1198/106186006X133933>, Zeileis et al. (2008) <doi:10.1198/106186008X319331> and Strobl et al. (2007) <doi:10.1186/1471-2105-8-25>.

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openblas

11.35 score 33 dependents 3.5k scripts 27k downloads

ipred - Improved Predictors

Improved predictive models by indirect classification and bagging for classification, regression and survival problems as well as resampling based estimators of prediction error.

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10.86 score 451 dependents 3.8k scripts 143k downloads

party - A Laboratory for Recursive Partytioning

A computational toolbox for recursive partitioning. The core of the package is ctree(), an implementation of conditional inference trees which embed tree-structured regression models into a well defined theory of conditional inference procedures. This non-parametric class of regression trees is applicable to all kinds of regression problems, including nominal, ordinal, numeric, censored as well as multivariate response variables and arbitrary measurement scales of the covariates. Based on conditional inference trees, cforest() provides an implementation of Breiman's random forests. The function mob() implements an algorithm for recursive partitioning based on parametric models (e.g. linear models, GLMs or survival regression) employing parameter instability tests for split selection. Extensible functionality for visualizing tree-structured regression models is available. The methods are described in Hothorn et al. (2006) <doi:10.1198/106186006X133933>, Zeileis et al. (2008) <doi:10.1198/106186008X319331> and Strobl et al. (2007) <doi:10.1186/1471-2105-8-25>.

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openblas

9.87 score 33 dependents 3.5k scripts 27k downloads

TH.data - TH's Data Archive

Contains data sets used in other packages Torsten Hothorn maintains.

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8.98 score 391 dependents 189 scripts 144k downloads

libcoin - Linear Test Statistics for Permutation Inference

Basic infrastructure for linear test statistics and permutation inference in the framework of Strasser and Weber (1999) <https://epub.wu.ac.at/102/>. This package must not be used by end-users. CRAN package 'coin' implements all user interfaces and is ready to be used by anyone.

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openblas

8.46 score 175 dependents 31 scripts 63k downloads

exactRankTests - Exact Distributions for Rank and Permutation Tests

Computes exact conditional p-values and quantiles using an implementation of the Shift-Algorithm by Streitberg & Roehmel.

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8.10 score 73 dependents 411 scripts 47k downloads

mlt - Most Likely Transformations

Likelihood-based estimation of conditional transformation models via the most likely transformation approach described in Hothorn et al. (2018) <DOI:10.1111/sjos.12291> and Hothorn (2020) <DOI:10.18637/jss.v092.i01>. Shift-scale (Siegfried et al, 2023, <DOI:10.1080/00031305.2023.2203177>) and multivariate (Klein et al, 2022, <DOI:10.1111/sjos.12501>) transformation models are part of this package. A package vignette is available from <DOI:10.32614/CRAN.package.mlt.docreg> and more convenient user interfaces to many models from <DOI:10.32614/CRAN.package.tram>.

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7.66 score 8 dependents 92 scripts 5.6k downloads

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>) and a tutorial explains applications in survival analysis (Siegfried et al., 2025, <DOI:10.48550/arXiv.2402.06428>). 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 a doubly robust score test, described in Kook et al. (2024, <DOI:10.1080/01621459.2024.2395588>).

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7.12 score 6 dependents 212 scripts 3.6k downloads

modeltools - Tools and Classes for Statistical Models

A collection of tools to deal with statistical models. The functionality is experimental and the user interface is likely to change in the future. The documentation is rather terse, but packages `coin' and `party' have some working examples. However, if you find the implemented ideas interesting we would be very interested in a discussion of this proposal. Contributions are more than welcome!

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6.75 score 242 dependents 102 scripts 76k downloads

basefun - Infrastructure for Computing with Basis Functions

Some very simple infrastructure for basis functions.

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6.42 score 9 dependents 21 scripts 5.1k downloads

variables - Variable Descriptions

Abstract descriptions of (yet) unobserved variables.

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6.36 score 10 dependents 16 scripts 5.0k downloads

mlt.docreg - Most Likely Transformations: Documentation and Regression Tests

Additional documentation, a package vignette and regression tests for package mlt.

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5.63 score 1.2k downloads

trtf - Transformation Trees and Forests

Recursive partytioning of transformation models with corresponding random forest for conditional transformation models as described in 'Transformation Forests' (Hothorn and Zeileis, 2021, <doi:10.1080/10618600.2021.1872581>) and 'Top-Down Transformation Choice' (Hothorn, 2018, <DOI:10.1177/1471082X17748081>).

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4.73 score 12 scripts 4.6k downloads