1.3.0-dev

.gitignore

@@ -5,6 +5,7 @@
 .Rproj.user
 .vscode/
 *.bkp
+*.log
 check/
 docs/
 FastRet.Rproj

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: FastRet
 Title: Retention Time Prediction in Liquid Chromatography
-Version: 1.3.0
+Version: 1.3.5
 Authors@R: c(
     person(given = "Tobias", family = "Schmidt", role = c("aut", "cre", "cph"), email = "tobias.schmidt331@gmail.com", comment = c(ORCID = "0000-0001-9681-9253")),
     person(given = "Christian", family = "Amesoeder", role = c("aut", "cph"), email = "christian-amesoeder@web.de", comment = c(ORCID = "0000-0002-1668-8351")),

NEWS.md

@@ -1,4 +1,61 @@
 
+# FastRet 1.3.5 <!-- Commit Date: 2026-01-13 -->
+
+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 <!-- Commit Date: 2026-01-12 -->
+
+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 <!-- Commit Date: 2026-01-11 -->
+
+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 <!-- Commit Date: 2026-01-11 -->
+
+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 <!-- Commit Date: 2026-01-11 -->
+
+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 <!-- Commit Date: 2025-11-12 -->
 
 API Improvements:

R/app.R

@@ -49,8 +49,7 @@ start_gui <- function(port = 8080,
     # Use [start_gui_in_devmode()] for development
     catf("Checking CDK version")
     check_cdk_version()
-    oldplan <- future::plan("multisession", workers = nw)
-    on.exit(future::plan(oldplan), add = TRUE)
+    with(future::plan("multisession", workers = nw), local = TRUE)
     catf("Starting FastRet GUI")
     app <- fastret_app(port, host, reload, nsw)
     shiny::runApp(app)
@@ -140,8 +139,7 @@ start_gui_in_devmode <- function(strategy = "sequential",
     ))
 
     catf("Initializing cluster")
-    oldplan <- future::plan(strategy)
-    on.exit(future::plan(oldplan), add = TRUE, after = FALSE)
+    with(future::plan(strategy), local = TRUE)
 
     catf("Starting FastRet GUI in development mode")
     pkg_root <- dirname(system.file("DESCRIPTION", package = "FastRet"))

R/sm.R

@@ -13,7 +13,7 @@
 #' re-measurement. The selection process includes:
 #'
 #' 1. Generating chemical descriptors from the SMILES strings of the molecules.
-#'    These are the features used by [train_frm()] and [adjust_frm()].
+#'    These are the features used by [train_frm()] and [adjust_frm()].˝
 #' 2. Standardizing chemical descriptors to have zero mean and unit variance.
 #' 3. Training a Ridge Regression model with the standardized chemical
 #'    descriptors as features and the retention times as the target variable.
@@ -40,15 +40,17 @@
 #'
 #' @param rt_coef
 #' Which coefficient to use for scaling RT before clustering. Options are:
-#' - max_ridge_coef: scale with the maximum absolute coefficient obtained in
-#'   ridge regression. I.e., RT will have approximately the same weight as the
-#'   most important chemical descriptor.
-#' - 1: do not scale RT any further, i.e., use standardized RT. The effect of
+#' - `0`: exclude RT from the clustering.
+#' - 'max' / 'max_ridge_coef': scale with the maximum absolute coefficient
+#'   obtained in ridge regression. I.e., RT will have approximately the same
+#'   weight as the most important chemical descriptor.
+#' - `1`: do not scale RT any further, i.e., use standardized RT. The effect of
 #'   leaving RT unscaled is kind of unpredictable, as the ridge coefficients
 #'   depend on the dataset. If the maximum absolute coefficient is much smaller
 #'   than 1, RT will dominate the clustering. If it is much larger than 1, RT
 #'   will have little influence on the clustering.
-#' - 0: exclude RT from the clustering.
+#' - 'inf': set all chemical descriptor values to zero, i.e., RT is "infinitely"
+#'   more important than any chemical descriptor.
 #'
 #' @return
 #' A list containing the following elements:
@@ -77,6 +79,15 @@ selective_measuring <- function(raw_data,
                                 rt_coef = "max_ridge_coef"
                                 ) {
 
+    stopifnot(
+        is.data.frame(raw_data),
+        all(c("NAME", "SMILES", "RT") %in% colnames(raw_data)),
+        is.numeric(k_cluster), length(k_cluster) == 1, !is.na(k_cluster), k_cluster >= 2,
+        is.logical(verbose) || is.numeric(verbose),
+        is.null(seed) || is.numeric(seed),
+        is.numeric(rt_coef) || is.character(rt_coef), length(rt_coef) == 1, !is.na(rt_coef)
+    )
+    rt_coef <- try_as_numeric(rt_coef, fallback = rt_coef)
     logf <- if (verbose >= 1) catf else null
 
     logf("Starting Selective Measuring")
@@ -91,25 +102,31 @@ selective_measuring <- function(raw_data,
     df <- df[, nonmeta, drop = FALSE]
     dfz <- as.data.frame(scale(df)) # z-score standardized (mean 0, sd 1)
 
-    logf("Training Ridge Regression model")
-    Xz <- as.matrix(dfz[, colnames(dfz) != "RT", drop = FALSE])
-    y <- as.numeric(dfz[, "RT"])
-    model <- fit_glmnet(Xz, y, method = "ridge", seed = seed)
+    if (rt_coef %in% c(Inf, "inf")) {
+        logf("Setting CDs to zero because rt_coef is Inf")
+        coefs <- rep(0, ncol(dfz) - 1)
+        names(coefs) <- colnames(dfz)[colnames(dfz) != "RT"]
+        model <- NULL
+        dfzb <- data.frame(RT = dfz$RT)
+    } else {
+        logf("Training Ridge Regression model")
+        Xz <- as.matrix(dfz[, colnames(dfz) != "RT", drop = FALSE])
+        y <- as.numeric(dfz[, "RT"])
+        model <- fit_glmnet(Xz, y, method = "ridge", seed = seed)
 
-    logf("Scaling features by coefficients of Ridge Regression model")
-    coef_mat <- glmnet::coef.glmnet(model) # (m+1) x 1 matrix
-    coefs <- as.numeric(coef_mat)[-1] # remove intercept
-    coefs <- setNames(coefs, rownames(coef_mat)[-1])
-    coefs <- coefs[colnames(Xz)] # ensure correct order
-    Xzb <- sweep(Xz, 2, coefs, `*`) # z-score standardized and beta-scaled
+        logf("Scaling features by coefficients of Ridge Regression model")
+        coef_mat <- glmnet::coef.glmnet(model) # (m+1) x 1 matrix
+        coefs <- as.numeric(coef_mat)[-1] # remove intercept
+        coefs <- setNames(coefs, rownames(coef_mat)[-1])
+        coefs <- coefs[colnames(Xz)] # ensure correct order
+        Xzb <- sweep(Xz, 2, coefs, `*`) # z-score standardized and beta-scaled
 
-    logf("Scaling RT by %s before clustering", rt_coef)
-    rtc <- if (rt_coef == "max_ridge_coef") {
-        max(abs(coefs), na.rm = TRUE)
-    } else {
-        as.numeric(rt_coef)
+        logf("Scaling RT by %s before clustering", rt_coef)
+        rtc <- if (grepl("max", rt_coef)) max(abs(coefs), na.rm = TRUE)
+            else if (is.numeric(rt_coef)) as.numeric(rt_coef)
+            else stop("Invalid value for rt_coef: ", rt_coef)
+        dfzb <- data.frame(RT = dfz$RT * rtc, Xzb)
     }
-    dfzb <- data.frame(RT = dfz$RT * rtc, Xzb)
 
     logf("Applying PAM clustering")
     clobj <- cluster::pam(dfzb, k = as.numeric(k_cluster))
@@ -124,3 +141,9 @@ selective_measuring <- function(raw_data,
     )
     ret
 }
+
+try_as_numeric <- function(x, fallback = x) {
+    if (length(x) != 1) stop("scalar expected")
+    y <- suppressWarnings(as.numeric(x))
+    if (!is.na(y)) y else fallback
+}