Package: resemble 3.0.1

Introduction | Methods available in the resemble package | Selecting the number of components | Variance-based selection | Individual variance threshold (ncomp_by_var()) | Cumulative variance threshold (ncomp_by_cumvar()) | Optimal component selection (ncomp_by_opc()) | Fixed number of components | Dimentionality reduction methods in ortho_projection() | Available methods | Example: PCA projection | Example: PLS projection | Example: Optimal component selection | Projecting new data | Multivariate side information | References

Introduction | Citing the package | Dataset used across the vignettes | Spectral preprocessing | References

Introduction | Main notation | Available dissimilarity construtors | Dissimilarity in orthogonal projection spaces | PCA-based dissimilarity | PLS-based dissimilarity | Correlation dissimilarity | Conventional correlation dissimilarity | Moving window correlation dissimilarity | Parallel execution for correlation-based dissimilarity (advanced users) | Euclidean dissimilarity | Cosine dissimilarity | Evaluating dissimilarity methods | References

Searching for neighbors | Neighbor selection methods | Fixed-$k$ selection | Dissimilarity-threshold selection | Using a specific number of neighbors | Using a dissimilarity threshold | Spiking the neighborhoods

Introduction | Fitting methods | Example | Data preparation | Fitting a PLS model | Fitting a GPR model | Prediction | Validation statistics | References

Memory-based learning | Fitting methods and constructors | The mbl_control() function and validation types | Model fitting and prediction | Additional examples | Using Yu argument | Supported parallel processing | References

Introduction | Glossary and conventions | The gesearch algorithm: How it works | Evolutionary search overview | Weakness score computation | Step 1: Evaluate each individual | Step 2: Aggregate to gene-level weakness | Interpretation | Weakness functions | Gene silencing | Final model | Key parameters | Practical considerations | Example workflow | The dataset, the target set, and its practical constraints | A simple linear model for predicting in the target set (baseline) | The model built with the samples found by gesearch | Further examples of gesearch | No response values available for the target set | No response values in the target set but with information about the response range | Supported parallel processing | References

Introduction | How liblex works | Build phase | Prediction phase | Data preparation | Core components | Neighbor selection | Dissimilarity methods | Fitting method | Control parameters | Building a library of models | Using fixed neighborhood size(s) | Using dissimilarity thresholds | Visualizing neighborhood centroids | Visualizing the regression models | Choosing anchor samples | Using k-means sampling | Building the library with selected anchors | Prediction | Prediction output | Weighting options | Enforcing specific experts | Validation and performance | Evaluation metrics | Uncertainty as a quality filter | Visualization | Comparison with classical MBL | Parallel processing | Summary | References