VineCopula - Statistical Inference of Vine Copulas
Provides tools for the statistical analysis of regular vine copula models, see Aas et al. (2009) <doi:10.1016/j.insmatheco.2007.02.001> and Dissman et al. (2013) <doi:10.1016/j.csda.2012.08.010>. The package includes tools for parameter estimation, model selection, simulation, goodness-of-fit tests, and visualization. Tools for estimation, selection and exploratory data analysis of bivariate copula models are also provided.
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copulaestimationstatisticsvine
10.64 score 97 stars 23 dependents 490 scripts 6.3k downloadsRcppThread - R-Friendly Threading in C++
Provides a C++11-style thread class and thread pool that can safely be interrupted from R. See Nagler (2021) <doi:10.18637/jss.v097.c01>.
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cpp
9.76 score 62 stars 107 dependents 10 scripts 12k downloadskde1d - Univariate Kernel Density Estimation
Provides an efficient implementation of univariate local polynomial kernel density estimators that can handle bounded and discrete data. See Geenens (2014) <doi:10.48550/arXiv.1303.4121>, Geenens and Wang (2018) <doi:10.48550/arXiv.1602.04862>, Nagler (2018a) <doi:10.48550/arXiv.1704.07457>, Nagler (2018b) <doi:10.48550/arXiv.1705.05431>.
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cpp
6.46 score 14 stars 17 dependents 74 scripts 1.1k downloadskdecopula - Kernel Smoothing for Bivariate Copula Densities
Provides fast implementations of kernel smoothing techniques for bivariate copula densities, in particular density estimation and resampling, see Nagler (2018) <doi:10.18637/jss.v084.i07>.
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openblascpp
6.30 score 9 stars 1 dependents 36 scripts 4.1k downloadswdm - Weighted Dependence Measures
Provides efficient implementations of weighted dependence measures and related asymptotic tests for independence. Implemented measures are the Pearson correlation, Spearman's rho, Kendall's tau, Blomqvist's beta, and Hoeffding's D; see, e.g., Nelsen (2006) <doi:10.1007/0-387-28678-0> and Hollander et al. (2015, ISBN:9780470387375).
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dependencestatisticscpp
5.79 score 3 stars 20 dependents 13 scripts 5.3k downloadsvinereg - D-Vine Quantile Regression
Implements D-vine quantile regression models with parametric or nonparametric pair-copulas. See Kraus and Czado (2017) <doi:10.1016/j.csda.2016.12.009> and Schallhorn et al. (2017) <doi:10.48550/arXiv.1705.08310>.
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copulaestimationstatisticsvinecpp
5.44 score 11 stars 25 scripts 256 downloadskdevine - Multivariate Kernel Density Estimation with Vine Copulas
Implements the vine copula based kernel density estimator of Nagler and Czado (2016) <doi:10.1016/j.jmva.2016.07.003>. The estimator does not suffer from the curse of dimensionality and is therefore well suited for high-dimensional applications.
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cpp
4.45 score 16 stars 35 scripts 321 downloadsVC2copula - Extend the 'copula' Package with Families and Models from 'VineCopula'
Provides new classes for (rotated) BB1, BB6, BB7, BB8, and Tawn copulas, extends the existing Gumbel and Clayton families with rotations, and allows to set up a vine copula model using the 'copula' API. Corresponding objects from the 'VineCopula' API can easily be converted.
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3.53 score 4 stars 17 scripts 204 downloadssvines - Stationary Vine Copula Models
Provides functionality to fit and simulate from stationary vine copula models for time series, see Nagler et al. (2022) <doi:10.1016/j.jeconom.2021.11.015>.
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cpp
3.30 score 4 stars 6 scripts 270 downloadscctools - Tools for the Continuous Convolution Trick in Nonparametric Estimation
Implements the uniform scaled beta distribution and the continuous convolution kernel density estimator.
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cpp
3.18 score 1 dependents 6 scripts 281 downloads