Mark Culp

Mark Culp

4 packages on CRAN

ada

cran
99.99th

Percentile

Performs discrete, real, and gentle boost under both exponential and logistic loss on a given data set. The package ada provides a straightforward, well-documented, and broad boosting routine for classification, ideally suited for small to moderate-sized data sets.

99.99th

Percentile

Implements several safe graph-based semi-supervised learning algorithms. The first algorithm is the Semi-Supervised Semi-Parametric Model (S4PM) and the fast Anchor Graph version of this approach. For additional technical details, refer to Culp and Ryan (2013) <http://jmlr.org/papers/v14/culp13a.html>, Ryan and Culp (2015) <http://www.jmlr.org/papers/v16/ryan15a.html> and the package vignette. The underlying fitting routines are executed in C++. All tuning parameter estimation is optimized using K-fold Cross-Validation.

spa

cran
99.99th

Percentile

Implements the Sequential Predictions Algorithm

C50

cran
99.99th

Percentile

C5.0 decision trees and rule-based models for pattern recognition that extend the work of Quinlan (1993, ISBN:1-55860-238-0).