scholarly journals evmix: An R package for Extreme Value Mixture Modeling, Threshold Estimation and Boundary Corrected Kernel Density Estimation

2018 ◽  
Vol 84 (5) ◽  
Author(s):  
Yang Hu ◽  
Carl Scarrott
2020 ◽  
Author(s):  
Jose Alquicira-Hernandez ◽  
Joseph E. Powell

AbstractSummaryData sparsity in single-cell experiments prevents an accurate assessment of gene expression when visualised in a low-dimensional space. Here, we introduce Nebulosa, an R package that uses weighted kernel density estimation to recover signals lost through drop-out or low expression.Availability and implementationNebulosa can be easily installed from www.github.com/powellgenomicslab/Nebulosa


2021 ◽  
pp. 1-11
Author(s):  
Lisa Steinmann ◽  
Barbora Weissova

This article introduces datplot, an R package designed to prepare chronological data for visualization, focusing on the treatment of objects dated to overlapping periods of time. Datplot is suitable for all disciplines in which scientists long for a synoptic method that enables the visualization of the chronology of a collection of heterogeneously dated objects. It is especially helpful for visualizing trends in object assemblages over long periods of time—for example, the development of pottery styles—and it can also assist in the dating of stratigraphy. As both authors come from the field of classical archaeology, the examples and case study demonstrating the functionality of the package analyze classical materials. In particular, we focus on presenting an assemblage of epigraphic evidence from Bithynia (northwestern Turkey), with a microregional focal point in the territory of Nicaea (modern Iznik). In the article, we present the internal methodology of datplot and the process of preparing a dataset of categorically and numerically dated objects. We demonstrate visualizing the data prepared by datplot using kernel density estimation and compare the outcome with more established methods such as histograms and line graphs.


2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Wenzhong Shi ◽  
Chengzhuo Tong ◽  
Anshu Zhang ◽  
Bin Wang ◽  
Zhicheng Shi ◽  
...  

A Correction to this paper has been published: https://doi.org/10.1038/s42003-021-01924-6


2021 ◽  
Vol 13 (1) ◽  
pp. 796-806
Author(s):  
Zhen Shuo ◽  
Zhang Jingyu ◽  
Zhang Zhengxiang ◽  
Zhao Jianjun

Abstract Understanding the risk of grassland fire occurrence associated with historical fire point events is critical for implementing effective management of grasslands. This may require a model to convert the fire point records into continuous spatial distribution data. Kernel density estimation (KDE) can be used to represent the spatial distribution of grassland fire occurrences and decrease the influences historical records in point format with inaccurate positions. The bandwidth is the most important parameter because it dominates the amount of variation in the estimation of KDE. In this study, the spatial distribution characteristic of the points was considered to determine the bandwidth of KDE with the Ripley’s K function method. With high, medium, and low concentration scenes of grassland fire points, kernel density surfaces were produced by using the kernel function with four bandwidth parameter selection methods. For acquiring the best maps, the estimated density surfaces were compared by mean integrated squared error methods. The results show that Ripley’s K function method is the best bandwidth selection method for mapping and analyzing the risk of grassland fire occurrence with the dependent or inaccurate point variable, considering the spatial distribution characteristics.


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