resemble Similarity Retrieval and Local Learning for Spectral Chemometrics

Last update: 2026-04-20

Version: 3.0.0 – vortex

About

The resemble package provides computationally efficient methods for dissimilarity analysis and predictive modelling with complex spectral data. Its core functionality includes memory-based learning (MBL), evolutionary subset search and selection, and retrieval-based modelling using pre-computed model libraries. The package is designed to support local modelling, spectral library optimization, and model-based prediction in large and heterogeneous spectral data sets.

Documentation

The package includes comprehensive vignettes covering all major functionality:

1. Essential concepts and setup: Introduction, data preparation, and notation
2. Dimensionality reduction: PCA and PLS projections with ortho_projection()
3. Estimating dissimilarity between spectra: Dissimilarity methods and evaluation
4. Nearest neighbor search: Finding similar spectra with search_neighbors()
5. Simple global models: Global calibration with model()
6. Classical memory-based learning: Per-query local modelling with mbl()
7. Evolutionary subset search: Domain-adaptive calibration with gesearch()
8. Building a library of models: Pre-computed experts with liblex()

What’s new in version 3.0

Version 3.0 is a major release with a redesigned API, new modelling functions, and improved computational efficiency.

New modelling functions:

• liblex(): Builds a library of reusable localized models (experts) that can be stored and reused for prediction without refitting. Based on Ramirez-Lopez et al. (2026b).

• gesearch(): Evolutionary algorithm for selecting optimal subsets from large spectral libraries to build context-specific calibrations. Based on Ramirez-Lopez et al. (2026a).

• model(): Fits global PLS or GPR calibration models with cross-validation.

Redesigned dissimilarity interface:

The dissimilarity system now uses constructor functions:

• diss_pca(), diss_pls(): Mahalanobis distance in projection space
• diss_correlation(): Correlation-based dissimilarity (including moving window)
• diss_euclidean(), diss_mahalanobis(), diss_cosine(): Distance metrics

Component selection via ncomp_by_var(), ncomp_by_cumvar(), ncomp_by_opc(), or ncomp_fixed().

Redesigned neighbor and fitting interfaces:

• neighbors_k(), neighbors_diss(): Neighbor selection constructors
• fit_pls(), fit_wapls(), fit_gpr(): Local fitting constructors (replace local_fit_*() functions)

Breaking changes in mbl():

• k, k_diss, k_range replaced by neighbors argument
• method renamed to fit_method
• center and scale removed; now controlled within constructors

See NEWS.md for full details on deprecated and removed functions.

Core functionality

Dimensionality reduction:

• ortho_projection(): PCA or PLS projection with multiple algorithms (SVD, NIPALS, SIMPLS)

Computing dissimilarity matrices:

• dissimilarity(): Main interface for dissimilarity computation
• diss_pca(), diss_pls(), diss_correlation(), diss_euclidean(), diss_mahalanobis(), diss_cosine(): Method constructors
• diss_evaluate(): Evaluate dissimilarity matrices using side information

Neighbor search:

• search_neighbors(): Efficient k-nearest neighbor retrieval

Modelling spectral data:

• model(): Global PLS or GPR calibration
• mbl(): Memory-based learning for per-query local modelling
• gesearch(): Evolutionary subset selection for domain-adaptive calibration
• liblex(): Pre-computed library of local experts for fast prediction

Installation

Install from CRAN:

install.packages("resemble")

Or install the development version from GitHub:

# install.packages("devtools")
devtools::install_github("l-ramirez-lopez/resemble")

The package requires a C++ compiler. On Windows, install Rtools. On macOS, you may need to install gfortran and clang from CRAN tools.

Example: Memory-based learning with mbl()

library(resemble)
library(prospectr)
data(NIRsoil)

# Preprocess spectra
NIRsoil$spc_pr <- savitzkyGolay(
 detrend(NIRsoil$spc, wav = as.numeric(colnames(NIRsoil$spc))),
 m = 1, p = 1, w = 7
)

# Split into training and test sets
train_x <- NIRsoil$spc_pr[NIRsoil$train == 1 & !is.na(NIRsoil$CEC), ]
train_y <- NIRsoil$CEC[NIRsoil$train == 1 & !is.na(NIRsoil$CEC)]
test_x <- NIRsoil$spc_pr[NIRsoil$train == 0 & !is.na(NIRsoil$CEC), ]
test_y <- NIRsoil$CEC[NIRsoil$train == 0 & !is.na(NIRsoil$CEC)]

# Memory-based learning with Gaussian process regression
sbl <- mbl(
 Xr = train_x,
 Yr = train_y,
 Xu = test_x,
 neighbors = neighbors_k(seq(50, 130, by = 20)),
 diss_method = diss_pca(ncomp = ncomp_by_opc(40)),
 fit_method = fit_gpr(),
 control = mbl_control(validation_type = "NNv")
)
sbl
plot(sbl)
get_predictions(sbl)

Example: Pre-computed model library with liblex()

liblex() builds a library of local experts that can be reused for prediction without refitting:

# Build model library
model_lib <- liblex(
 Xr = train_x,
 Yr = train_y,
 neighbors = neighbors_k(c(40, 60, 80)),
 diss_method = diss_correlation(ws = 27, scale = TRUE),
 fit_method = fit_wapls(min_ncomp = 3, max_ncomp = 15, method = "mpls"),
 control = liblex_control(tune = TRUE)
)

# Predict new observations
predictions <- predict(model_lib, test_x)

Example: Evolutionary subset selection with gesearch()

gesearch() selects optimal subsets from large spectral libraries:

# Search for optimal calibration subset
gs <- gesearch(
 Xr = train_x, 
 Yr = train_y,
 Xu = test_x,
 k = 50, 
 b = 100, 
 retain = 0.97,
 target_size = 200,
 fit_method = fit_pls(ncomp = 15, method = "mpls"),
 optimization = c("reconstruction", "similarity"),
 seed = 42
)

# Predict using selected subset
preds <- predict(gs, test_x)
plot(gs)

Memory-based learning overview

Memory-based learning (MBL, a.k.a. instance-based learning or local modelling) is a non-linear lazy learning approach. For each prediction, the algorithm:

1. Finds the k-nearest neighbors in the reference set
2. Fits a local model using those neighbors
3. Predicts the response for the target observation

The mbl() function offers three regression methods for local models:

• fit_gpr(): Gaussian process regression with linear kernel
• fit_pls(): Partial least squares
• fit_wapls(): Weighted average PLS (Shenk et al., 1997)

Citing the package

citation(package = "resemble")

News: Memory-based learning and resemble

• 2026.05: van Leeuwen et al., 2026
  used resemble for principal component Mahalanobis nearest-neighbour search to extract spectrally similar samples from the KSSL library for MIR model calibration in Dutch soils.

• 2026.04: Irving et al., 2026
  used resemble in modelling workflows for infrared spectroscopy prediction of soil microbial properties across Australian soils.

• 2026.03: Shrestha et al., 2026
  used resemble in a hybrid localisation workflow to predict farm-scale soil cadmium from a regional spectral library; LOCAL models with MIR data performed best.

• 2025.10: Summerauer et al., 2025 used resemble for MBL modelling of soil properties from infrared spectra across tropical hillslopes in Eastern Africa.

• 2025.05: Sun and Shi, 2025 combined spectral and geographical similarity for SOC prediction; local PLSR outperformed global models.

• 2025.03: Breure et al., 2025
  used resemble for local VNIR modelling of soil carbon fractions (POC and MAOC) across European agricultural soils, published in Nature Communications.

• 2025.03: Purushothaman et al., 2025 applied MBL to AVIRIS-NG hyperspectral data for soil property prediction in India.

• 2025.01: Dai et al., 2025 used MBL for POC and MAOC prediction from VNIR in Guangdong.

• 2024.12: Asrat et al., 2024 MBL for local calibration sample selection in the Moroccan Soil Spectral Library.

• 2024.09: Barbetti et al., 2024 MBL to detect SOC changes in long-term experiments using vis–NIR.

• 2023.11: Wang et al., 2023 N-MBL (MBL + RF within local fitting) for regional vis–NIR models.

• 2022: Sanderman et al., 2022 evaluated transferability of large MIR spectral databases across instruments.

• 2022.01: Ng et al., 2022 showed that MBL yields better local SOC predictions than spiking approaches.

• 2021.10: Ramirez-Lopez et al., 2021 MBL to predict soil properties in Africa.

• 2020.08: Charlotte Rivard’s MIR MBL tutorial: https://whrc.github.io/Soil-Predictions-MIR/

• 2020.01: Sanderman et al., 2020 MIR spectroscopy for prediction of soil health indicators; MBL and Cubist excelled.

• 2019.03: Ramirez-Lopez et al., 2019 MBL in digital soil mapping at farm scale.

• 2019.03: Jaconi et al., 2019 MBL for national-scale NIR texture predictions in Germany.

• 2018.01: Dotto et al., 2018 MBL for SOC prediction in Brazil.

• 2016.04: Viscarra Rossel et al., 2016 memory-based learning for soil property prediction.

• 2014.03: First CRAN release of resemble.

Related packages

• prospectr: Signal processing and chemometrics for spectroscopy

Contributing

Contributions are welcome! Please read our Contributing Guidelines (available in the GitHub repo) before submitting pull requests.

This project follows a Code of Conduct available in the GitHub repo.

Bug reports

Report issues at GitHub or contact the maintainer (ramirez.lopez.leo@gmail.com).

References

Lobsey, C. R., Viscarra Rossel, R. A., Roudier, P., & Hedley, C. B. 2017. rs-local data-mines information from spectral libraries to improve local calibrations. European Journal of Soil Science, 68(6), 840-852.

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

Ramirez-Lopez, L., Viscarra Rossel, R., Behrens, T., Orellano, C., Perez-Fernandez, E., Kooijman, L., Wadoux, A. M. J.-C., Breure, T., Summerauer, L., Safanelli, J. L., & Plans, M. (2026a). When spectral libraries are too complex to search: Evolutionary subset selection for domain-adaptive calibration. Analytica Chimica Acta, under review.

Ramirez-Lopez, L., Metz, M., Lesnoff, M., Orellano, C., Perez-Fernandez, E., Plans, M., Breure, T., Behrens, T., Viscarra Rossel, R., & Peng, Y. (2026b). Rethinking local spectral modelling: From per-query refitting to model libraries. Analytica Chimica Acta, under review.

Saul, L. K., & Roweis, S. T. 2003. Think globally, fit locally: unsupervised learning of low dimensional manifolds. Journal of machine learning research, 4(Jun), 119-155.

Shenk, J., Westerhaus, M., and Berzaghi, P. 1997. Investigation of a LOCAL calibration procedure for near infrared instruments. Journal of Near Infrared Spectroscopy, 5, 223-232.