FastRet 1.5.2

This is the first CRAN release since 1.3.0. It includes all changes listed below for versions 1.3.1 to 1.5.1, notably the 1.3.7 fix: cross-validation in train_frm() no longer requires the suggested package toscutil, so model training no longer fails in a plain CRAN install without Suggests packages.

Changes in this version:

• Replaced a structure(.Names = ) call in the GUI server code with stats::setNames(), fixing the R-devel CRAN check NOTE about deprecated special names.

• The xgboost grid search (tune_grid) now passes the training objective inside params, as xgboost 3.x requires; xgboost had warned that the old form will become an error in a future version.

• Added an inst/CITATION file and a reference to the FastRet paper (Fadil et al., 2026, doi:10.1021/acs.jcim.6c01344) in the package description and README.

• Fixed the privacy-contact link in the GUI, which pointed to a page of the University of Regensburg that no longer exists.

• Fixed a typo in the biocViews field of DESCRIPTION.

• README: installation now leads with the CRAN release, mentions the hosted GUI at https://fastret.spang-lab.de and lists Selective Measuring as a feature.

• Vignettes: corrected installation instructions, broken anchors, outdated function names and typos; documented the hyperparameter-tuning checkbox (Train) and the compound-matching option (Adjust) of the GUI; extended the contribution guidelines.

• CI: updated GitHub Actions to their current major versions.

FastRet 1.5.1

• The descriptor-cache builder (updateCachedCDs()) and the example-data generator (misc/scripts/make-example-data.R) now download Measurements_v10P.xlsx from the version-independent example-data release tag instead of the v1.3.0 package release, so all example/source artifacts live in one place and the build no longer pins an old package-version tag.

FastRet 1.5.0

Example datasets & models refresh:

1. The bundled RP dataset (and inst/extdata/RP.xlsx) was regenerated from the published Measurements_v10P.xlsx and now contains 458 metabolites (was 442), with an added INCHIKEY column.

2. inst/extdata/RP_adj.xlsx (returned by read_rpadj_xlsx()) is now real data: 25 RP metabolites re-measured under a steeper gradient (dataset RP_Steep in the FastRet paper), replacing the previous synthetic file. It also gains an INCHIKEY column.

3. inst/extdata/RP_lasso_model.rds was retrained on the new 458-metabolite RP.

4. The shipped chemical-descriptor cache (inst/cachedata/CDs.sqlite) was rebuilt from Measurements_v10P.xlsx; the outdated inst/extdata/Measurements_v8.xlsx is no longer shipped.

5. Documentation and the Shiny GUI now offer direct download links to the example files (hosted as GitHub release assets), so web-app users can follow the tutorials without installing the package.

FastRet 1.4.5

Model adjustment (compound matching):

1. The default compound matching used by adjust_frm() now matches on "INCHIKEY" alone (when complete InChIKeys are available in both new_data and frm$df), instead of the previous c("SMILES", "INCHIKEY"). The InChIKey is a structure-invariant identifier and alone uniquely identifies a molecule, whereas SMILES is matched as an exact raw string and often differs in spelling between datasets — so including it only made matching more fragile without adding identifying power. This also aligns the package default with the InChIKey-based matching used throughout the FastRet paper’s adjustment benchmarks. When InChIKeys are unavailable the fallback is unchanged (c("SMILES", "NAME")), and an explicit match_keys argument still overrides the default.

2. The GUI’s Adjust tab gained a “Compound matching” dropdown that exposes this choice: Automatic (recommended; InChIKey when available, else Name+SMILES), InChIKey, Name, or Name + SMILES. The selection is passed to adjust_frm() as match_keys.

FastRet 1.4.4

Hyperparameter tuning:

1. train_frm()’s tune_grid argument now also accepts the grid-size keywords "tiny", "small" and "large" (in addition to an explicit data.frame), e.g. train_frm(method = "brt", tune_grid = "small").

2. The built-in "small" grid was changed to a fast, effective 8-combination Retip-style grid (max_depth 2-5, eta 0.01/0.02, with gamma = 1, colsample_bytree/subsample 0.5, min_child_weight 10; the number of boosting rounds is chosen by early stopping). In the FastRet paper’s benchmark this internal grid search matches the accuracy of Retip’s own grid search. (The predefined grids were not previously exposed, so this is not a breaking change.)

3. The GUI’s Train tab gained a “Tune hyperparameters (grid search)” checkbox. When enabled for XGBoost it runs the small grid search (a few extra seconds); it has no effect for Lasso.

FastRet 1.4.3

Bug fixes (GUI):

1. Training from an uploaded workbook that contained an INCHIKEY column crashed the whole GUI session (the UI greyed out and had to be restarted). train_frm() keeps INCHIKEY in frm$df as metadata, but the Train tab’s result handler excluded only NAME/RT/SMILES before calling round(cds, 2), so the character INCHIKEY column made round() error. As this ran inside a reactive status observer without a tryCatch, the error tore down the session. The handler now excludes INCHIKEY too, and the extended-task status observers are wrapped so any future handler error shows a notification instead of killing the session. (This was not fixed by the 1.4.1 robust-upload change, which deliberately keeps INCHIKEY.)

2. Dropped upload columns are no longer silent. When an uploaded workbook contains columns FastRet does not use (anything other than NAME/RT/SMILES/INCHIKEY/RT_ADJ and chemical descriptors), the GUI now shows a notification naming the ignored columns, so it is clear they are not carried into the results/download.

FastRet 1.4.2

1. train_frm() gains a tune_grid argument: supplying a data.frame of xgboost hyperparameters runs a cross-validated grid search over those rows for gbtree/BRT models (instead of the fixed default parameters), making FastRet’s existing grid-search machinery user-accessible. NULL (default) is unchanged.

FastRet 1.4.1

GUI upload fixes:

1. Large uploads are now accepted. Shiny’s default request-size limit of 5 MB rejected normal inputs such as an 8 MB adjusted model. The GUI now raises the limit to 500 MB, so any model or data file a user is likely to upload is accepted (shiny.maxRequestSize, set in fastret_app()).

2. Excel uploads are read robustly. Instead of always reading the first worksheet, FastRet now searches every worksheet for the first one that contains the required columns (RT/SMILES/NAME for training, selective measuring and adjustment; NAME/SMILES for prediction) and silently drops any extra columns. Previously, uploading a workbook with additional columns besides the required ones - or with the data on a sheet other than the first - could cause model training to stall. New internal helpers read_input_xlsx() and drop_unsupported_columns() implement this and are covered by tests.

FastRet 1.4.0

Chemical-descriptor cache:

1. The chemical-descriptor cache is now a pure on-disk SQLite database (CDs.sqlite) instead of an in-memory R option backed by CDs.rds. On first use the shipped database is copied into the per-user cache directory (tools::R_user_dir("FastRet", "cache")) and all reads and writes go there. Because the cache lives on disk, descriptors computed in one process are immediately visible to every other process and worker, so parallel training and the GUI’s background workers now share a single cache. Concurrent access is made safe via SQLite WAL mode and INSERT OR IGNORE.

2. getCDs(), preprocess_data(), train_frm(), predict.frm() and adjust_frm() gained a cache argument. With cache = TRUE (default) descriptors are read from and written to the on-disk cache; with cache = FALSE the cache is bypassed and every descriptor is recomputed via rCDK (useful for benchmarking the uncached runtime).

3. Removed the internal FastRet.cachedCDs option and the N_SMI_CACHED constant. CDK / rcdklibs >= 2.9 is required (already enforced at GUI startup by check_cdk_version()), as it provides all descriptors in CDFeatures.

GUI:

1. Reworked the model-adjustment controls in the Adjust tab. The old “Components of linear model” checkbox group (which let users toggle the RT, RT^2, RT^3, log(RT), exp(RT), and sqrt(RT) predictors of a linear adjustment model) has been replaced by an “Adjustment method” radio button that lets users pick the model family used for the correction: Lasso (recommended), Linear model, or XGBoost. Lasso and XGBoost adjust using the base retention time together with the molecular descriptors and can therefore capture compound-specific shifts, whereas the linear model fits a simple straight-line correction from the base retention time only. The GUI now passes adj_type to adjust_frm() accordingly; the deprecated RT-transformation predictors are no longer exposed.

2. Promoted the four workflows to dedicated navbar tabs (Train, Select, Adjust, Predict) instead of selecting them from a single drop-down. Each tab shows a short label with the full mode description on hover, and carries only its own controls and results. The Privacy Policy, Contact, and About pages remain as separate tabs.

3. Simplified the Train sidebar to the essentials: data upload, method (XGBoost/Lasso), seed, and the console log. The “Show advanced settings” toggle and the “Preprocessing Options” checkbox group were removed – GUI training now always uses the package defaults of train_frm().

4. GUI-trained models are now reproducible by default. The training seed previously defaulted to the current system time and was never actually passed to train_frm(); it now defaults to a fixed value (42) and is applied, so training the same data twice yields identical models. The seed remains user-editable.

5. The console log is now always visible in every mode (the per-mode “Show console logs” checkboxes were removed), including a new console-log box for Selective Measuring.

6. The Selective Measuring tab now exposes the selection variant (named as in the paper: SMmax, SM1, SM0, SMinf, with an info button explaining each) and the random seed, so users can pick the variant and reproduce a selection from the GUI.

7. Reviewed the GUI help texts for clarity, tone and formatting, and reworded the prediction console output so it no longer implies that descriptors are recomputed when they are served from the cache.

8. Each tab’s sidebar now starts with a heading (the mode name) and a help button that explains, in a few sentences, what the mode is for and when to use it.

Testing:

1. Added shinytest2 end-to-end GUI tests (tests/testthat/test-gui-e2e.R) that drive the real app via headless Chrome and exercise every mode and model type (Train: Lasso + XGBoost; Selective Measuring; Predict; Adjust: Lasso + Linear model + XGBoost). They are skipped on CRAN and when Chrome/Java are unavailable.

Documentation:

1. Rewrote the GUI Usage vignette to match the new navbar-tab layout and the simplified sidebars, with up-to-date field/button names and a fresh screenshot of every mode (Train/Select/Adjust/Predict). The screenshots are regenerated reproducibly via misc/scripts/make-gui-screenshots.R.

FastRet 1.3.7

Bugfix:

1. Removed a leftover toscutil::stub() development call from the cross-validation path of train_frm(). The call had no effect on results (it only assigned predict.frm argument defaults into a function environment that was never read again), but it made toscutil — a Suggests package — a hard runtime dependency. As a result, model training failed with “there is no package called ‘toscutil’” whenever Suggests packages were not installed (e.g. a plain CRAN install).

Dependencies:

1. Now requires future (>= 1.40.0). start_gui() uses with(future::plan(...), local = TRUE), which relies on the with.FutureStrategyList method introduced in future 1.40.0.

FastRet 1.3.6

Documentation:

1. Improved the GUI documentation: corrected the description of the Selective Measuring algorithm (PAM / k-medoids clustering, not k-means), added step-by-step instructions and a Java/CDK troubleshooting note to the GUI-Usage vignette, and fixed minor wording in the GUI help texts.

FastRet 1.3.5

Bugfix:

1. predict.frm() now imputes missing/non-finite values not only for base model predictors, but also for adjustment model predictors. This prevents NA predictions if the adjustment model depends on predictors that are missing/non-finite in the new data.

Internal Improvements:

1. Consolidated the imputation logic in predict.frm() into a shared internal helper.

FastRet 1.3.4

Internal Improvements:

1. start_gui() and start_gui_in_devmode() now rely on with(future::plan(...), local = TRUE) so that temporary future plans are restored automatically without manual bookkeeping.

FastRet 1.3.3

API Improvements:

1. selective_measuring() accepts "max" and "inf" as additional values for its rt_coef parameter:
   • "max" is an alias for the existing "max_ridge_coef"
   • "inf" sets all chemical descriptor features to zero before clustering so that RT alone drives the distance metric (i.e., it is “infinitely” more important than the chemical descriptors).

FastRet 1.3.2

Bugfix:

1. adjust_frm() automatically switched to “lm” adjustment if predictors contained only one predictor, regardless of whether add_cds was TRUE or FALSE. This has now been fixed, so that “lasso”, “ridge” and “gbtree” adjustment is now possible if either add_cds is TRUE, add_cds is NULL or predictors contains more than one predictor.

FastRet 1.3.1

API Improvements:

1. Added arguments match_rts and match_keys to adjust_frm():
   • If match_rts=TRUE (default), RTs are obtained by matching rows in new_data to rows in frm$df based on match_keys.
   • If match_rts=FALSE, RTs are obtained by applying the base model to new_data.
   • match_keys can be any combination of “INCHIKEY”, “SMILES” and “NAME”. If left at default NULL, SMILES+INCHIKEY is used if both columns are present in the adjusted and the original training data. Otherwise, SMILES+NAME is used.

FastRet 1.3.0

API Improvements:

1. getCDs():
   • Calculation of CDs is skipped, if all CDs are already present in the input dataframe.
   • In addition to a dataframe with column “SMILES”, a plain character vector of SMILES strings can now be provided as input.
   • Improved progress output.
   • More Smiles are now pre-cached internally to speed up retrieval of CDs for large datasets.
2. plot_frm():
   • Now available as exported function (the function existed before, but was only exposed via the Graphical User Interface). Now users can call it directly from R scripts.
   • Added semi-transparent background to legends for better readability
   • Changed the point character from unfilled circles with colored borders to filled circles with black borders for better visibility.
   • Constant predictions (causing correlation to be NA) do no longer cause a warning.
3. preprocess_data():
   • Added argument add_cds to control whether chemical descriptors should be added to the input data using getCDs().
   • Added argument rm_ucs to control whether unsupported columns (i.e. columns that are neither mandatory nor optional) should be removed from the input data.
   • Added argument rt_terms to control whether transformations of the RT column (square, cube, log, exp, sqrt) should be added to the input data.
   • In case of missing mandatory columns (SMILES, RT, NAME) an error is now raised.
   • INCHIKEY and Chemical Descriptors listed in CDFeatures are allowed as optional columns.
   • Columns that are neither mandatory nor optional are automatically removed.
   • Improved runtime for generation of polynomial features and/or interaction terms.
   • Removal of NA and/or near-zero-variance predictors is now done after adding polynomial features and/or interaction terms.
   • Improved progress output.
4. train_frm():
   • Added argument do_cv to control whether cross-validation should be performed for performance estimation. Default is TRUE.
   • Removal of near-zero-variance predictors and/or removal of NA values is now done as part of the internal model training, i.e. it happens separately for each fold during cross-validation. This prevents data leakage from the training set to the validation set. The corresponding hint about “overoptimistic cross-validation results” has consequently been removed from the documentation.
   • Argument method now accepts two values for training models with xgbtree base: “gbtreeDefault” (train xgboost with default params) and “gbtreeRP” (train xgboost with parameters optimized for the RP dataset). The old value “gbtree” still works and is now an alias for “gbtreeDefault”.
   • Improved documentation of the return value (i.e. frm objects are fully specified now).
   • Added type checking for each user input.
   • Performance estimation via cross-validation now uses the new clipping mechanism provided by clip_predictions(). Of course, the clipping is always based on the RT range of training folds, not the whole original training data.
   • Calculating proportion of variance explained (R²) no longer throws a warning for constant predictions (causing correlation to be NA). Instead, R² is set to 0 in such cases.
5. predict.frm():
   • Data transformations applied to the training data (adding polynomial features and/or adding interaction terms) are now automatically applied to new data as well. This was not the case before, leading to errors when trying to predict RTs using models trained with degree_polynomial>1 and/or interaction_terms=TRUE, unless the transformations were manually applied to the new data beforehand.
   • Added argument clip to allow clipping of predictions to be within the RT range of the training data. Works for both adjusted and unadjusted models.
   • Predictions are now clipped to be within a sensible range by default. To produce unclipped predictions, set clip=FALSE. See clip_predictions() for details.
   • If a chemical descriptor is NA in the new data, but required by the model, the NA value is now replaced by the mean of that descriptor in the training data. Previously, the prediction for such entries was NA. The old behavior can be restored by setting impute=FALSE in predict.frm().
6. selective_measuring():
   • Added argument rt_coef, allowing user to control the influence of RT on the clustering. A value of 0 means that RT is ignored, a value of “max_ridge_coefficient” means that RT has the same weight as the most important chemical descriptor and a value of 1 means no scaling at all (except standardization to z-scores, which is applied before to the whole dataset before the ridge regression is trained).
7. adjust_frm():
   • Added argument seed to allow reproducible results.
   • Added argument do_cv to control whether cross-validation should be performed for performance estimation. Default is TRUE.
   • Added argument adj_type to control which model should be trained for adjustment: supported options are “lm”, “lasso”, “ridge”, or “gbtree”. Previously, only “lm” was supported. To stay backwards compatible, the default is “lm”.
   • Added argument add_cds to control whether chemical descriptors should be added to the input data using getCDs(). Only recommended for adj_type other than “lm”.
   • Added support for mapping by SMILES+INCHIKEY in addition to SMILES+NAME. SMILES+INCHIKEY is used by default if both columns are present in the adjusted and the original training data. Otherwise SMILES+NAME is used as before.
   • Improved error handling. Previously, unmappable entries in the new data had been ignored silently. Now, an error is raised in such cases.
   • Function arguments are now stored in the returned frm object for better reproducibility.
   • Mapping is now performed by matching each new entry to the average RT of all original training entries with the same key (SMILES+INCHIKEY or SMILES+NAME). Example: if the new dataset contains a key twice, and the original training data contains the key three times, both new entries are mapped to the average RT of the three original entries.
   • Performance estimation via cross-validation now uses the new clipping mechanism provided by clip_predictions(). Of course, the clipping is always based on the RT range of training folds, not the whole original training data.
   • Calculating proportion of variance explained (R²) no longer throws a warning for constant predictions (causing correlation to be NA). Instead, R² is set to 0 in such cases.
8. print.frm():
   • frm objects can now be printed directly to the console in a user-friendly format.
9. clip_predictions():
   • New utility function for clipping predicted RTs to be within a sensible range. Used internally by train_frm(), predict.frm() and adjust_frm().
10. get_predictors():
    • Added arguments base and adjust to control whether predictors for the base model, the adjustment model or both should be returned.

Bugfixes:

1. Interaction terms generated by preprocess_data() are now generated correctly as product of the involved features instead of a division. This follows common practice in regression modeling and avoids division by zero issues. Passing older models, trained with division-based interaction terms, to downstream functions like predict.frm() or adjust_frm() will now lead to an error. (This is not a breaking change, as predict.frm() and friends have in fact never been able to handle such models).
2. plot_frm() with type “scatter.cv.adj” or “scatter.train.adj” now correctly shows retention times from the new data (used for model adjustment) as x-axis values instead of the original training retention times.
3. catf() now only emits escape codes (i.e. colored output), it the output is directed to a terminal. If the output is redirected to a file or a pipe, no escape codes are emitted anymore. Since catf() is used throughout the package for logging, this fixes the output for the whole package.

Internal Improvements:

1. Added or improved unit tests for:
   • adjust_frm()
   • fit_gbtree()
   • fit_glmnet()
   • get_param_grid()
   • get_predictors()
   • getCDs()
   • plot_frm()
   • predict_frm()
   • preprocess_data()
   • selective_measuring()
   • train_frm()
   • validate_inputdata()
2. Removed caret dependency by adding custom implementations for:
   • createFolds()
   • nearZeroVar()
3. Extract mapping and merging part of adjust_frm() into a private function merge_dfs().
4. Replaced fit_glmnet(), fit_lasso() and fit_ridge() with a single function fit_glmnet(), that takes the method (“lasso” or “ridge”) as parameter. Instead of a dataframe df that has to contain only predictors plus the RT column (as response), the function now takes a matrix of predictors X and a vector of responses y. This makes the function more flexible and easier to test.
5. Replaced fit_gbtree_grid() with a much simpler function find_params_best(). Instead of allowing the specification of every grid parameter, the new function instead accepts a keyword searchspace for specifying predefined grids to choose from.
6. Improved fit_gbtree by exposing lots of hardcoded internal xgboost parameters as function parameters with sensible defaults. In particular, the user can now set xpar to “default”, “rpopt” or a predefined grid-size to train the model with different hyperparameter settings. Furthermore, the function is now written in a way that works with both, version 1.7.9.1 and the new 3.1.2.1 version published on 2025/12/03 (yes, version 2.x was skipped completely).
7. Added helper function get_param_grid() for returning predefined hyperparameter grids for xgboost model training based on keywords like “tiny”, “small” or “large”.
8. Added function benchmark_find_params() to benchmark runtime of find_params_best() for different numbers of cores and/or threads. As it turns out, choosing a higher number of cores is usually more efficient (at the cost of worse progress output).
9. Added utility functions named(), as_str(), is_valid_smiles() and as_canonical()

FastRet 1.2.2

• Improved selective_measuring() by aligning glmnet coefficients to columns by name (more stable) and by including RT, scaled by max(abs(coefs)), in PAM clustering.
• Added libwebp-dev as dependency to Dockerfile.

FastRet 1.2.1

• Add updated Measurements Measurements_v8.xlsx to inst/extdata/. The new list contains corrections to the old RP dataset plus 1660 new measurements measured on a total of 18 different chromatographic environments.
• Reintroduced RAM caching (although hugely simplified).

FastRet 1.2.0

• Added seed parameter to selective_measuring() function for reproducible clustering results
• Enhanced documentation for train_frm() function
• Removed digest and shinybusy dependencies
• Major refactoring of caching system and related functions
• Removed mock files from inst/mockdata/
• Removed objects: getCDsFor1Molecule(), get_cache_dir(), ram_cache (these were exported, but declared as internal)
• Added private function parLapply2
• Added comprehensive GitHub Copilot instructions file
• Improved code organization and documentation across multiple R files

FastRet 1.1.5

• Improved read_retip_hilic_data(): the dataset is now only downloaded from GitHub if the package is not installed. If it is installed, the dataset is loaded directly.

• Internal Changes:

  • Removed TODOS.md
  • Bumped version to 1.1.5
  • Moved all data related functions from util.R to data.R
  • Added a README to misc/datasets
  • Added functions load_all() and document() to util.R
  • Replaced xlsx and readxl packages with openxlsx

FastRet 1.1.4

• Added a cache cleanup handler that gets registered via reg.finalizer() upon package loading to ensure that the cache directory is removed if it doesn’t contain any files that should persist between R sessions.

• Added an article about installation details incl. a troubleshooting section

• Improved function docs

• Improved examples by removing donttest blocks

• Improved examples & tests by using smaller example datasets to reduce runtime

FastRet 1.1.3

• Moved patch.R from the R folder to misc/scripts, which is excluded from the package build using .Rbuildignore. The file is conditionally sourced by the private function start_gui_in_devmode() if available, allowing its use during development without including it in the package.

• Added \value tags to the mentioned .Rd files describing the functions’ return values.

• Added Bonini et al. (2020) doi:10.1021/acs.analchem.9b05765 as reference to the description part of the DESCRIPTION file, listing it as Related work. This reference is used in the documentation for read_retip_hilic_data() and ram_cache. No additional references are used in the package documentation.

• Added Fadi Fadil as a contributor. Fadi measured the example datasets shipped with FastRet.

• Added ORCID IDs for contributors as described in CRAN’s checklist for submissions.

FastRet 1.1.2

• Wrapped examples of read_rp_xlsx() and read_rpadj_xlsx() into donttest to prevent note “Examples with CPU time > 2.5 times elapsed time: …”. By now I’m pretty sure the culprit is the xlsx package, which uses a java process for reading the file. Maybe we should switch to openxlsx or readxl in the future.

FastRet 1.1.1

• Improved examples of preprocess_data() to prevent note “Examples with CPU time > 2.5 times elapsed time: preprocess_data (CPU=2.772, elapsed=0.788)”.

FastRet 1.1.0

• Added RAM caching to getCDs()

FastRet 1.0.3

• Added examples to start_gui(), fastret_app(), getCDsFor1Molecule(), analyzeCDNames(), check_lm_suitabilitym(), plot_lm_suitability(), extendedTask(), selective_measuring(), train_frm(), adjust_frm(), get_predictors()

• Improved lots of existing examples

• Added additional logging messages at various places

• Submitted to CRAN, but rejected because the following examples caused at least one of the following notes on the CRAN testing machines: (1) “CPU time > 5s”, (2) “CPU time > 2.5 times elapsed time”. In this context, “CPU time” is calculated as the sum of the measured “user” and “system” times.

  function	user	system	elapsed	ratio
  check_lm_suitability	5.667	0.248	2.211	2.675
  predict.frm	2.477	0.112	0.763	3.393
  getCDs	2.745	0.089	0.961	2.949

FastRet 1.0.2

• Improved github actions

FastRet 1.0.1

• Improved github actions

FastRet 1.0.0

Completely refactored source code, e.g.:

• Added a test suite covering all important functions

• The UI now uses Extended Tasks for background processing, allowing GUI usage by multiple users at the same time

• The clustering now uses Partitioning Around Medoids (PAM) instead of k-means, which is faster and much better suited for our use case

• The training of the Lasso and/or XGBoost models is no longer done using caret but using glmnet and xgboost directly. The new implementation is much faster and allows for full control over the number of workers started.

• Function getCDs now caches the results on Disk, making the retrieval of chemical descriptors much faster

• The GUI now has a console element, showing the progress of the background tasks like clustering and model training

• The GUI has a cleaner interface, because lots of the options are now hidden in the “Advanced” tab by default and are only displayed upon user request

FastRet 0.99.7

• Fixed R CMD check findings
• Fixed GitHub Actions
• Fixed Dockerfile

FastRet 0.99.6

• Added contact, privacy policy
• Fixed Dockerfile

FastRet 0.99.4

• Improved GitHub Actions and Formatting of source code

FastRet 0.99.3

• Reduce required R version in DESCRIPTION from 4.2 to 4.1
• Added Dockerfile
• Fixed R CMD check warnings
• Fixed R CMD check action

FastRet 0.99.2

• Added documentation website at: https://spang-lab.github.io/FastRet/

FastRet 0.99.1

• Initial version.

  Copy of commit cd243aa82a56df405df8060b84535633cf06b692 of Christian Amesöders Repository. (Christian wrote this initial version of FastRet as part of his master thesis at the Institute of functional Genomics, University of Regensburg).