Miscellaneous convenience functions and dataset
c(person("Hugo", "Quené", role = c("aut", "cre"),
email = "h.quene@uu.nl") )
This package contains some miscellaneous convenience functions,
to create a matrix of dummy columns from a factor,
to determine whether x lies in range [a,b],
to add a rectangular bracket to an existing plot,
and to convert frequencies between Hz, semitones, mel and Bark.
This package also contains an example data set of a stratified sample
of 80 talkers of Dutch.

Miscellaneous Convenience Functions and Dataset
c(person("Hugo", "Quene", role = c("aut", "cre"),
email = "h.quene@uu.nl",
comment = c(ORCID="0000-0001-7988-1346")) )
Miscellaneous convenience functions and wrapper functions
to convert frequencies between Hz, semitones, mel and Bark,
to create a matrix of dummy columns from a factor,
to determine whether x lies in range [a,b],
and to add a bracketed line to an existing plot.
This package also contains an example data set of a stratified sample
of 80 talkers of Dutch.

Regularization Paths for Huber Loss Regression and Quantile
Regression Penalized by Lasso or Elastic-Net
Efficient algorithms for fitting entire regularization paths for Huber loss regression and quantile regression penalized by lasso or elastic-net.

Regularization Paths for Lasso or Elastic-Net Penalized Huber
Loss Regression and Quantile Regression
Efficient algorithms for fitting regularization paths for lasso or elastic-net penalized regression models with Huber loss, quantile loss or squared loss.

Hazard Ratio Estimation using Cox Model Weighted by the
Estimated Propensity Score
person("David", "Hajage", email = "david.hajage@aphp.fr", role = c("aut", "cre"))
Estimates the log hazard ratio associated with a binary exposure using a Cox PH model weighted by the propensity score.
Propensity model is estimated using a simple logistic regression.
Variance estimation takes into account the propensity score estimation step with the method proposed by Hajage et al. (2018) <doi:10.1002/bimj.201700330>.
Both the average treatment effect on the overall (ATE) or the treated (ATT) population can be estimated.
For the ATE estimation, both unstabilized and stabilized weights can be used.