Description: Provide consistent significance controlled variable selection procedure with different directions (forward, backward, stepwise) based on diverse criteria (AIC, BIC, adjusted r-square, and p-value). The algorithm selects a final model with only significant variables based on a correction choice of False Discovery Rate, Bonferroni, or no correction.
Packaged: 2019-06-06 05:54:03 UTC; superwooky

Packaged: 2020-04-02 23:18:38 UTC; jongwookkim

Packaged: 2019-06-25 23:11:10 UTC; superwooky

Description: Provides significance controlled variable selection algorithms with different directions (forward, backward, stepwise) based on diverse criteria (AIC, BIC, adjusted r-square, PRESS, or p-value). The algorithm selects a final model with only significant variables based on a correction choice of False Discovery Rate, Bonferroni, or no correction.
Packaged: 2020-04-17 04:01:29 UTC; jongwookkim

Title: Consistent Significance Controlled Variable Selection in
Description: Provides significance controlled variable selection algorithms with different directions (forward, backward, stepwise) based on diverse criteria (AIC, BIC, adjusted r-square, PRESS, or p-value). The algorithm selects a final model with only significant variables defined as those with significant p-values after multiple testing correction such as Bonferroni, False Discovery Rate, etc.
Packaged: 2021-06-16 16:37:18 UTC; jongwookkim

Packaged: 2021-06-30 18:22:58 UTC; jongwookkim

Packaged: 2021-09-22 21:42:35 UTC; adrianozambom

Description: Provides significance controlled variable selection algorithms with different directions (forward, backward, stepwise) based on diverse criteria (AIC, BIC, adjusted r-square, PRESS, or p-value). The algorithm selects a final model with only significant variables defined as those with significant p-values after multiple testing correction such as Bonferroni, False Discovery Rate, etc. See Zambom and Kim (2018) <doi:10.1002/sta4.210>.
Packaged: 2022-03-21 23:46:25 UTC; adrianozambom