Releases: lehmasve/hdflex
Releases · lehmasve/hdflex
hdflex 0.3.2
hdflex 0.3.0
- Enhanced parallelization using RcppThreads for the
stsc()function. - Improved (computational) performance
- Added S3 class method for
stscanddscobjects:summary.stsc_objandsummary.dsc_objfor generating plots showing the evolution of the tuning parameter, as well as standard accuracy metrics such as Mean-Squared-Error, Continuous-Ranked-Probability-Score, and Predictive-Log-Likelihood-Score. - Introduction of the new argument
biasforstsc()andtvc, allowing users to decide whether bias correction should be applied to the F-Signals in the TVC-models. - Addition of the new argument
inclforstsc()anddsc, enabling users to specify whether certain signals are required to be included in the subsets. - Improved internal structure and performance for
dsc(). - Renamed the argument
burn_in_tvctoburn_inandsample_lengthtoinit. - Consolidated the arguments
risk_aversion,min_weight, andmax_weightintoportfolio_arguments.
hdflex 0.2.1
- Fixed a bug in the computation of the time-varying coefficients in the first step of the stsc() method.
- The forgetting factor delta in the second step of the stsc() method now already applies to the most recent predictive likelihood score in t-1, as stated in Equation (13) in Adaemmer et al. (2023). Previously, the score in t-1 was given a weight of 1.0
- Added a new argument to stsc() to decide whether the subset combinations in the second step of the method should be combined with equal weights (as proposed in Adaemmer et al. (2023)) or with weights derived from the predictive log-likelihood scores.
hdflex 0.2.0
- Added the function stsc() to directly apply the STSC-algorithm from Adaemmer, Lehmann and Schuessler (2023). This function is faster and more memory efficient than subsequently applying tvc() and dsc() as it is now completely written in Rcpp.
- Fixed the package overview help file.
- Updated documentation.
- Updated example.
hdflex 0.1.0
CRAN release.