resemble-package: Overview of the functions in the resemble package

In resemble: Memory-Based Learning in Spectral Chemometrics

Overview of the functions in the resemble package

Description

Functions for memory-based learning

Details

This is the version 2.2.3 – embryo of the package. It implements a number of functions useful for modeling complex spectral spectra (e.g. NIR, IR). The package includes functions for dimensionality reduction, computing spectral dissimilarity matrices, nearest neighbor search, and modeling spectral data using memory-based learning. This package builds upon the methods presented in Ramirez-Lopez et al. (2013) \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1016/j.geoderma.2012.12.014")}.

Development versions can be found in the github repository of the package at https://github.com/l-ramirez-lopez/resemble.

The functions available for dimensionality reduction are:

• ortho_projection

• pc_projection

• pls_projection

• predict.ortho_projection

The functions available for computing dissimilarity matrices are:

• dissimilarity

• f_diss

• cor_diss

• sid

• ortho_diss

The functions available for evaluating dissimilarity matrices are:

• sim_eval

The functions available for nearest neighbor search:

• search_neighbors

The functions available for modeling spectral data:

• mbl

• mbl_control

Other supplementary functions:

• plot.mbl

• plot.ortho_projection

Author(s)

Maintainer / Creator: Leonardo Ramirez-Lopez [e-mail]

Authors:

• Leonardo Ramirez-Lopez (ORCID)

• Antoine Stevens (ORCID)

• Claudio Orellano

• Raphael Viscarra Rossel (ORCID)

• Zefang Shen

• Craig Lobsey (ORCID)

• Alex Wadoux (ORCID)

References

Ramirez-Lopez, L., Behrens, T., Schmidt, K., Stevens, A., Dematte, J.A.M., Scholten, T. 2013a. The spectrum-based learner: A new local approach for modeling soil vis-NIR spectra of complex data sets. Geoderma 195-196, 268-279.

See Also

Useful links:

• https://github.com/l-ramirez-lopez/resemble

• Report bugs at https://github.com/l-ramirez-lopez/resemble/issues

resemble documentation built on May 29, 2024, 8:49 a.m.