| hdi-package {hdi} | R Documentation |
hdi
Description
Implementation of multiple approaches to perform inference in high-dimensional models.
Details
The DESCRIPTION file:
| Package: | hdi |
| Type: | Package |
| Title: | High-Dimensional Inference |
| Version: | 0.1-9 |
| Date: | 2021-05-27 |
| Author: | Lukas Meier [aut, cre], Ruben Dezeure [aut], Nicolai Meinshausen [aut], Martin Maechler [aut], Peter Buehlmann [aut] |
| Maintainer: | Lukas Meier <meier@stat.math.ethz.ch> |
| Description: | Implementation of multiple approaches to perform inference in high-dimensional models. |
| Depends: | scalreg |
| DependsNote: | scalreg does not correctly import lars etc, so we need to depend on it |
| Imports: | grDevices, graphics, stats, parallel, MASS, glmnet, linprog |
| Suggests: | Matrix |
| SuggestsNote: | for tests only |
| Encoding: | UTF-8 |
| License: | GPL |
Index of help topics:
boot.lasso.proj P-values based on the bootstrapped lasso
projection method
clusterGroupBound Hierarchical structure group tests in linear
model
fdr.adjust Function to calculate FDR adjusted p-values
glm.pval Function to calculate p-values for a
generalized linear model.
groupBound Lower bound on the l1-norm of groups of
regression variables
hdi Function to perform inference in
high-dimensional (generalized) linear models
hdi-package hdi
lasso.cv Select Predictors via (10-fold)
Cross-Validation of the Lasso
lasso.firstq Determine the first q Predictors in the Lasso
Path
lasso.proj P-values based on lasso projection method
lm.ci Function to calculate confidence intervals for
ordinary multiple linear regression.
lm.pval Function to calculate p-values for ordinary
multiple linear regression.
multi.split Calculate P-values Based on Multi-Splitting
Approach
plot.clusterGroupBound
Plot output of hierarchical testing of groups
of variables
rXb Generate Data Design Matrix X and Coefficient
Vector beta
riboflavin Riboflavin data set
ridge.proj P-values based on ridge projection method
stability Function to perform stability selection
Author(s)
Lukas Meier, Ruben Dezeure, Nicolai Meinshausen, Martin Mächler, Peter Bühlmann, Maintainer: Lukas Meier <meier@stat.math.ethz.ch>
References
Dezeure, R., Bühlmann, P., Meier, L. and Meinshausen, N. (2015) High-dimensional inference: confidence intervals, p-values and R-software hdi. Statistical Science 30, 533–558.
Meinshausen, N., Meier, L. and Bühlmann, P. (2009) P-values for high-dimensional regression. Journal of the American Statistical Association 104, 1671–1681.
Meinshausen, N. (2015) Group-bound: confidence intervals for groups of variables in sparse high-dimensional regression without assumptions on the design. Journal of the Royal Statistical Society: Series B, 77(5), 923–945.
Meinshausen, N. and Bühlmann, P. (2010) Stability selection (with discussion). Journal of the Royal Statistical Society: Series B 72, 417–473.