rOpenSci | Statistics

Statistics

Statistical algorithms and statistics-specific workflows
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Bayesian Modeling and Causal Inference for Multivariate Longitudinal Data

Santtu Tikka
Description

Easy-to-use and efficient interface for Bayesian inference of complex panel (time series) data using dynamic multivariate panel models by Helske and Tikka (2024) doi:10.1016/j.alcr.2024.100617. The package supports joint modeling of multiple measurements per individual, time-varying and time-invariant effects, and a wide range of discrete and continuous distributions. Estimation of these dynamic multivariate panel models is carried out via Stan. For an in-depth tutorial of the package, see (Tikka and Helske, 2023) arxiv:2302.01607.

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Multiple Empirical Likelihood Tests

Eunseop Kim
Description

Performs multiple empirical likelihood tests. It 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 provided in Kim, MacEachern, and Peruggia (2023) doi:10.1080/10485252.2023.2206919. A companion paper by Kim, MacEachern, and Peruggia (2024) doi:10.18637/jss.v108.i05 is available for further information. This work was supported by the U.S. National Science Foundation under Grants No. SES-1921523 and DMS-2015552.

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Mechanistic Simulation of Species Range Dynamics

Katarzyna Markowska
Description

Species range dynamics simulation toolset.

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Accelerated Oblique Random Forests

Byron Jaeger
Description

Fit, interpret, and compute predictions with oblique random forests. Includes support for partial dependence, variable importance, passing customized functions for variable importance and identification of linear combinations of features. Methods for the oblique random survival forest are described in Jaeger et al., (2023) DOI:10.1080/10618600.2023.2231048.

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GLMMcosinor
CRAN Peer-reviewed

Fit a Cosinor Model Using a Generalised Mixed Modelling Framework

Rex Parsons
Description

Allows users to fit a cosinor model using the glmmTMB framework. This extends on existing cosinor modelling packages, including cosinor and circacompare, by including a wide range of available link functions and the capability to fit mixed models. The cosinor model is described by Cornelissen (2014) doi:10.1186/1742-4682-11-16.

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Market Structure, Concentration and Inequality Measures

Andreas Schneider
Description

Based on individual market shares of all participants in a market or space, the package offers a set of different structural and concentration measures frequently - and not so frequently - used in research and in practice. Measures can be calculated in groups or individually. The calculated measure or the resulting vector in table format should help practitioners make more informed decisions. Methods used in this package are from: 1. Chang, E. J., Guerra, S. M., de Souza Penaloza, R. A. & Tabak, B. M. (2005) “Banking concentration: the Brazilian case”. 2. Cobham, A. and A. Summer (2013). “Is It All About the Tails? The Palma Measure of Income Inequality”. 3. Garcia Alba Idunate, P. (1994). “Un Indice de dominancia para el analisis de la estructura de los mercados”. 4. Ginevicius, R. and S. Cirba (2009). “Additive measurement of market concentration” doi:10.3846/1611-1699.2009.10.191-198. 5. Herfindahl, O. C. (1950), “Concentration in the steel industry” (PhD thesis). 6. Hirschmann, A. O. (1945), “National power and structure of foreign trade”. 7. Melnik, A., O. Shy, and R. Stenbacka (2008), “Assessing market dominance” doi:10.1016/j.jebo.2008.03.010. 8. Palma, J. G. (2006). “Globalizing Inequality: Centrifugal and Centripetal Forces at Work”. 9. Shannon, C. E. (1948). “A Mathematical Theory of Communication”. 10. Simpson, E. H. (1949). “Measurement of Diversity” doi:10.1038/163688a0.

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Class-Agnostic Time Series

Christoph Sax
Description

Time series toolkit with identical behavior for all time series classes: ts,xts, data.frame, data.table, tibble, zoo, timeSeries, tsibble, tis or irts. Also converts reliably between these classes.

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Evaluate Clinical Prediction Models by Net Monetary Benefit

Rex Parsons
Description

Estimates when and where a model-guided treatment strategy may outperform a treat-all or treat-none approach by Monte Carlo simulation and evaluation of the Net Monetary Benefit. Details can be viewed in Parsons et al. (2023) doi:10.21105/joss.05328.

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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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