Description: Collection of functions for manipulating sets of HPO terms and

Package: hpower
Version: 0.1-0
Date: 25 November 1991
Author: Written by Daniel F. Heitjan (dheitjan@peter.cpmc.columbia.edu).
R port by Stefan Funke (funke@attglobal.net)
Title: Computation of power and sample size for tests of the general
linear hypothesis
Description: A suite of Splus/R functions to compute power and sample
size for tests of the general linear hypothesis.
License: These functions are not copyrighted.  The author requests,
however, that you acknowledge the source and communicate any
improvements to him.  No guarantees WHATSOEVER from the author or the
porter.

Title: Miscellaneous convenience functions and dataset
Authors@R: c(person("Hugo", "Quené", role = c("aut", "cre"),
Description: 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

Title: Miscellaneous Convenience Functions and Dataset
Authors@R: c(person("Hugo", "Quene", role = c("aut", "cre"),
Description: Miscellaneous convenience functions and wrapper functions
to convert frequencies between Hz, semitones, mel and Bark,
and to add a bracketed line to an existing plot.
Author: Hugo Quene [aut, cre] (<https://orcid.org/0000-0001-7988-1346>)

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

Title: Regularization Paths for Lasso or Elastic-Net Penalized Huber
Description: Efficient algorithms for fitting regularization paths for lasso or elastic-net penalized regression models with Huber loss, quantile loss or squared loss.

Title: Hazard Ratio Estimation using Cox Model Weighted by the
Authors@R: person("David", "Hajage", email = "david.hajage@aphp.fr", role = c("aut", "cre"))
Description: 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.