DEoptim - Global Optimization by Differential Evolution
Implements the Differential Evolution algorithm for global optimization of a real-valued function of a real-valued parameter vector as described in Mullen et al. (2011) <doi:10.18637/jss.v040.i06>.
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differential-evolutionevolutionary-algorithmglobal-optimizationoptimization
11.70 score 31 stars 132 dependents 840 scripts 19k downloadsRiskPortfolios - Computation of Risk-Based Portfolios
Collection of functions designed to compute risk-based portfolios as described in Ardia et al. (2017) <doi:10.1007/s10479-017-2474-7> and Ardia et al. (2017) <doi:10.21105/joss.00171>.
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covarianceoptimizationportfolioportfolio-optimizationrisk
5.26 score 54 stars 68 scripts 530 downloadsPeerPerformance - Luck-Corrected Peer Performance Analysis in R
Provides functions to perform the peer performance analysis of funds' returns as described in Ardia and Boudt (2018) <doi:10.1016/j.jbankfin.2017.10.014>.
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alphafinancefundsperformance-analysisperformance-metricsscreeningsharpe
4.93 score 13 stars 22 scripts 290 downloadsbayesGARCH - Bayesian Estimation of the GARCH(1,1) Model with Student-t Innovations
Provides the bayesGARCH() function which performs the Bayesian estimation of the GARCH(1,1) model with Student's t innovations as described in Ardia (2008) <doi:10.1007/978-3-540-78657-3>.
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bayesiangarchmcmcrisk-modelsstudent
4.13 score 16 stars 17 scripts 476 downloadsspantest - Mean-Variance Spanning Tests
Provides a comprehensive suite of portfolio spanning tests for asset pricing, such as Huberman and Kandel (1987) <doi:10.1111/j.1540-6261.1987.tb03917.x>, Gibbons et al. (1989) <doi:10.2307/1913625>, Kempf and Memmel (2006) <doi:10.1007/BF03396737>, Pesaran and Yamagata (2024) <doi:10.1093/jjfinec/nbad002>, and Gungor and Luger (2016) <doi:10.1080/07350015.2015.1019510>.
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3.60 score 1 stars 1 scripts 433 downloadsAdMit - Adaptive Mixture of Student-t Distributions
Provides functions to perform the fitting of an adaptive mixture of Student-t distributions to a target density through its kernel function as described in Ardia et al. (2009) <doi:10.18637/jss.v029.i03>. The mixture approximation can then be used as the importance density in importance sampling or as the candidate density in the Metropolis-Hastings algorithm to obtain quantities of interest for the target density itself.
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adaptivedistributionfittingmcmcmixturemixture-model
3.00 score 2 stars 9 scripts 423 downloads