rOpenSci | Statistics

Statistics

Statistical algorithms and statistics-specific workflows
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Accelerated Oblique Random Survival Forests

Byron Jaeger
Description

Fit, interpret, and make predictions with oblique random survival forests. Oblique decision trees are notoriously slow compared to their axis based counterparts, but aorsf runs as fast or faster than axis-based decision tree algorithms for right-censored time-to-event outcomes. Methods to accelerate and interpret the oblique random survival forest are described in Jaeger et al., (2022) arXiv::2208.01129.

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Multiple Empirical Likelihood Tests

Eunseop Kim
Description

Performs multiple empirical likelihood tests for linear and generalized linear models. The package offers an easy-to-use interface and flexibility in specifying hypotheses and calibration methods, extending the framework to simultaneous inferences. The core computational routines are implemented using the Eigen C++ library and RcppEigen interface, with OpenMP for parallel computation. Details of the testing procedures are given in Kim, MacEachern, and Peruggia (2021) arxiv:2112.09206. This work was supported by the U.S. National Science Foundation under Grants No. SES-1921523 and DMS-2015552.

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Categorical Analysis of Neo- And Paleo-Endemism

Joel H. Nitta
Description

Provides functions to analyze the spatial distribution of biodiversity, in particular categorical analysis of neo- and paleo-endemism (CANAPE) as described in Mishler et al (2014) doi:10.1038/ncomms5473. canaper conducts statistical tests to determine the types of endemism that occur in a study area while accounting for the evolutionary relationships of species.

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