Package: esreg 0.6.2

esreg: Joint Quantile and Expected Shortfall Regression

Simultaneous modeling of the quantile and the expected shortfall of a response variable given a set of covariates, see Dimitriadis and Bayer (2019) <doi:10.1214/19-EJS1560>.

Authors:Sebastian Bayer [aut, cre], Timo Dimitriadis [aut]

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NEWS

# Install 'esreg' in R:
install.packages('esreg', repos = c('https://bayerse.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/bayerse/esreg/issues

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3

On CRAN:

expected-shortfallquantile-regressionvalue-at-risk

3.48 score 2 stars 1 packages 10 scripts 276 downloads 20 exports 10 dependencies

Last updated 2 years agofrom:086e4fdfac. Checks:OK: 9. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 03 2024
R-4.5-win-x86_64OKNov 03 2024
R-4.5-linux-x86_64OKNov 03 2024
R-4.4-win-x86_64OKNov 03 2024
R-4.4-mac-x86_64OKNov 03 2024
R-4.4-mac-aarch64OKNov 03 2024
R-4.3-win-x86_64OKNov 03 2024
R-4.3-mac-x86_64OKNov 03 2024
R-4.3-mac-aarch64OKNov 03 2024

Exports:cdf_at_quantileconditional_mean_sigmaconditional_truncated_variancedensity_quantile_functionesr_lossesr_rho_lpesregestfun.esregG_vecG1_funG1_prime_funG1_prime_prime_funG2_curly_funG2_funG2_prime_funG2_prime_primelambda_matrixsigma_matrixvcovAvcovB

Dependencies:FormulalatticeMASSMatrixMatrixModelsquantregRcppRcppArmadilloSparseMsurvival