Title: Implementation of the Fourier Amplitute Sensitivity Test (FAST)
Description: The Fourier Amplitute Sensitivity Test (FAST) is a method
to deterimine global sensitivities of a model on parameter
changes with relavtively few model runs. This package
implements this sensitivity analysis method.
Packaged: Thu Mar 26 18:57:23 2009; dreusser

Description: The Fourier Amplitute Sensitivity Test (FAST) is a method to deterimine global sensitivities of a model on parameter changes with relavtively few model runs. This package implements this sensitivity analysis method.

Title: Implementation of the Fourier Amplitude Sensitivity Test (FAST)
Description: The Fourier Amplitude Sensitivity Test (FAST) is a method
to determine global sensitivities of a model on parameter
changes with relatively few model runs. This package implements

Description: The Fourier Amplitude Sensitivity Test (FAST) is a method to determine global sensitivities of a model on parameter changes with relatively few model runs. This package implements this sensitivity analysis method.

Title: a Fast Implementation of Adaboost
Description: Implements Adaboost based on C++ backend code.
This is blazingly fast and especially useful for large, in memory data sets.
The package uses decision trees as weak classifiers. Once the classifiers
have been trained, they can be used to predict new data.
Currently, we support only binary classification tasks.
The package implements the Adaboost.M1 algorithm and the real

Title: Fast Differential Correlation Matrix Screening
Description: Fast spectral algorithms for differential analysis on large-scale correlation matrices of Li et. al. (2021) <arXiv:2111.03721> are implemented. The methods can identify a group of genes exhibiting differential correlation patterns between two groups. For high-dimensional matrices, compressed spectral screening methods are also provided based on random subsampling. The work to build this package is partially supported by the NSF grant DMS-2015298.

Title: Fast Creation of Dummy (Binary) Columns from Categorical
Description: Creates dummy columns from columns that have categorical variables (character or factor types). This package provides a significant speed increase from creating dummy variables through model.matrix().