Dr. Tharindu De Alwis

The sdrt() function is designed for estimating subspaces for Sufficient Dimension Reduction (SDR) in time series, with a specific focus on the Time Series Central Mean subspace (TS-CMS). The package employs the Fourier transformation method proposed by Samadi and De Alwis (2023) and the Nadaraya-Watson kernel smoother method proposed by Park et al. (2009) for estimating the TS-CMS. The package provides tools for estimating distances between subspaces and includes functions for selecting model parameters using the Fourier transformation method.

The itdr() routine allows for the estimation of sufficient dimension reduction subspaces in univariate regression such as the central mean subspace or central subspace in regression. This is achieved using Fourier transformation methods proposed by Zhu and Zeng (2006) , convolution transformation methods proposed by Zeng and Zhu (2010) , and iterative Hessian transformation methods proposed by Cook and Li (2002) . Additionally, mitdr() function provides optimal estimators for sufficient dimension reduction subspaces in multivariate regression by optimizing a discrepancy function using a Fourier transform approach proposed by Weng and Yin (2022) , and selects the sufficient variables using Fourier transform sparse inverse regression estimators proposed by Weng (2022) .