Wrapped: An R package for circular data

Nadarajah, Saralees; Zhang, Yuanyuan

doi:10.1371/journal.pone.0188512

Cited by 13 publications

(7 citation statements)

References 42 publications

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“…We obtain the maximum likelihood estimation of the parameters and Λ by using the "mle" subroutine in the package 'stats4' (version 3.4.3) of R. Note that when applying the mle subroutine, the parameter ranges should be selected as wide as possible to avoid local maxima. We also refer the advanced readers to an R package 'wrapped', introduced by Nadarajah and Zhang [15], for further computation in wrapped distributions.…”

Section: Application To Real Datamentioning

confidence: 99%

A new wrapped exponential distribution

Yilmaz

Biçer

2018

Math Sci

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We introduce a new wrapped exponential distribution named transmuted wrapped exponential (TWE) distribution, for the modeling of circular datasets by using the Transmutation Rank-Map method. This method is employed for the first time for a wrapped distribution with this study. The introduced distribution is more flexible than traditional wrapped exponential distribution. The paper provides the explicit form of important distributional properties of the introduced distribution such as expectation, median, moments, characteristic function, quantile function, hazard rate function and stress-strength reliability. Rényi and Shannon entropies are also obtained. The statistical inference problem for the TWE distribution is investigated using maximum likelihood, least squares and weighted least squares and comparative numerical study results are presented. Furthermore, we present a real dataset analysis.

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Section: Application To Real Datamentioning

confidence: 99%

A new wrapped exponential distribution

Yilmaz

Biçer

2018

Math Sci

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“…θ 2 = 0). We utilize the circular R package (Agostinelli and Lund, 2013) and the wrapped R package (Nadarajah and Zhang, 2017) to compute (4.2)-(4.4).…”

Section: Unknown Forward Optimization Methodsmentioning

confidence: 99%

Inverse Bayesian Optimization: Learning Human Acquisition Functions in an Exploration vs Exploitation Search Task

Sandholtz,

Miyamoto,

Bornn

et al. 2021

Preprint

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Bayesian optimization is a popular algorithm for sequential optimization of a latent objective function when sampling from the objective is costly. The search path of the algorithm is governed by the acquisition function, which defines the agent's search strategy. Conceptually, the acquisition function characterizes how the optimizer balances exploration and exploitation when searching for the optimum of the latent objective. In this paper, we explore the inverse problem of Bayesian optimization; we seek to estimate the agent's latent acquisition function based on observed search paths. We introduce a probabilistic solution framework for the inverse problem which provides a principled framework to quantify both the variability with which the agent performs the optimization task as well as the uncertainty around their estimated acquisition function.We illustrate our methods by analyzing human behavior from an experiment which was designed to force subjects to balance exploration and exploitation in search of an invisible target location. We find that while most subjects demonstrate clear trends in their search behavior, there is significant variation around these trends from round to round. A wide range of search strategies are exhibited across the subjects in our study, but upper confidence bound acquisition functions offer the best fit for the majority of subjects. Finally, some subjects do not map well to any of the acquisition functions we initially consider; these subjects tend to exhibit exploration preferences beyond that of standard acquisition functions to capture. Guided by the model discrepancies, we augment the candidate acquisition functions to yield a superior fit to the human behavior in this task.

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“…The quantiles and CDF of the wrapped Cauchy distribution and the quantiles of the mixed von Mises distribution cannot be found analytically, and they are also not part of the circular-package (Agostinelli and Lund, 2017). Therefore, we implemented them numerically based on the wrapped G distribution of the wrapped-package (Nadarajah and Zhang, 2017) and spline interpolation, respectively (see appendix, section A1). Finally, we also provide PDF, CDF, random number generation and quantiles for kernel density estimates with the functions rdens(n, density), ddens(x, density), pdens(x, density), qdens(p, density)…”

Section: Fitting Circular Distributionsmentioning

confidence: 99%

cylcop: An R Package for Circular-Linear Copulae with Angular Symmetry

Fieberg

2021

Preprint

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The cylcop package extends the copula package to allow modeling of correlated circular-linear random variables using copulae that are symmetric in the circular dimension. We present and derive several new circular-linear copulae with this property and demonstrate how they can be implemented in the cylcop package to model animal movements in discrete time. The package contains methods for estimating copulae parameters, plotting probability density and cumulative distribution functions, and simulating data.

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