Nonlinear Dimensionality Reduction for Clustering
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Updated
Jul 1, 2020 - R
Nonlinear Dimensionality Reduction for Clustering
Dimensionality Reduction via Regression using Kernel Ridge Regression in R
This package implements the efficient dynamic programming approach to conduct estimation and testing for linear models in presence of structural breaks as described in Bai & Perron (1998) and Perron, Yamamoto, & Zhou (2020).
A recopilation of nonlinear growth models used in many branches of science
This package implements hypothesis testing procedures that can be used to identify the number of regimes in a Markov-Switching model.
Shiny App to check the need of nonlinear effects in standard regression models
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