inesortega · inesortega · Dec 3, 2025 · Oct 8, 2025 · Oct 15, 2025 · Oct 15, 2025
diff --git a/.Rbuildignore b/.Rbuildignore
@@ -19,3 +19,4 @@
 ^__pycache__/
 ^.cache/
 ^python-req\.txt$
+examples.R
diff --git a/CRAN-SUBMISSION b/CRAN-SUBMISSION
@@ -0,0 +1,3 @@
+Version: 2.0.1
+Date: 2025-12-02 22:24:02 UTC
+SHA: 88496b0c320e65d5bc0ad8ab6e6cee661b3306ba
diff --git a/DESCRIPTION b/DESCRIPTION
@@ -1,7 +1,7 @@
 Package: neuralGAM
 Type: Package
 Title: Interpretable Neural Network Based on Generalized Additive Models
-Version: 2.0
+Version: 2.0.1
 Authors@R: c(person("Ines", "Ortega-Fernandez", role = c("aut", "cre", "cph"),
                      email = "iortega@gradiant.org", 
                      comment = c(ORCID = "0000-0002-8041-6860")),
@@ -25,7 +25,7 @@ Imports:
   patchwork,
   rlang
 SystemRequirements: python (>= 3.10), keras (== 2.15), tensorflow (== 2.15)
-RoxygenNote: 7.3.2
+RoxygenNote: 7.3.3
 Roxygen: list(markdown = TRUE)
 Suggests: 
     covr,

diff --git a/NAMESPACE b/NAMESPACE
@@ -6,10 +6,12 @@ S3method(predict,neuralGAM)
 S3method(print,neuralGAM)
 S3method(summary,neuralGAM)
 export(autoplot)
+export(autoplot.neuralGAM)
 export(diagnose)
 export(install_neuralGAM)
 export(neuralGAM)
 export(plot_history)
+export(predict.neuralGAM)
 export(sim_neuralGAM_data)
 import(patchwork)
 importFrom(formula.tools,lhs)

diff --git a/NEWS.md b/NEWS.md
@@ -1,4 +1,9 @@
-# neuralGAM 2.0
+# neuralGAM 2.0.1
+
+* Minor bug fix related to the exportation of S3 methods (autoplot, predict). 
+* Minor corrections in documentation
+
+# neuralGAM 2.0.0
 
 * **Major update** with expanded flexibility, improved diagnosis tools, and uncertainty quantification.
 * **Additional distribution families**: now supports `poisson` in addition to `gaussian` and `binomial`.

diff --git a/R/NeuralGAM.R b/R/NeuralGAM.R
@@ -24,7 +24,7 @@
 #'   }
 #' @param loss Loss function to use. Can be any Keras built-in (e.g., `"mse"`, `"mae"`,
 #'     `"huber"`, `"logcosh"`) or a custom function, passed directly to `keras::compile()`.
-#' @param alpha Significance level for prediction intervals, e.g. \code{0.05} for 95% coverage.
+#' @param alpha Significance level for confidence intervals, e.g. \code{0.05} for 95% coverage.
 #' @param dropout_rate Dropout probability in smooth-term NNs (0,1).
 #'   \itemize{
 #'     \item During training: acts as a regularizer.
@@ -355,10 +355,7 @@ neuralGAM <-
         f[formula$p_terms] <- predict(linear_model, type = "terms", verbose = verbose)
         eta <- eta0 + rowSums(f)
 
-        # Do NOT create per-term PIs for parametric effects
-        # (prediction intervals are response-level; not meaningful per term)
-
-        # Instead, store epistemic variance for CI of the mean contribution
+        # Store epistemic variance for CI of the mean contribution
         # using predict.lm(type="terms", se.fit=TRUE)
         for (tm in formula$p_terms) {
           pr_tm <- stats::predict(linear_model,
@@ -487,7 +484,7 @@ neuralGAM <-
                                     alpha = alpha,
                                     forward_passes = forward_passes)
       if(build_pi == TRUE){
-        # Update prediction intervals
+        # Update CI
         lwr[[term]] <- preds$lwr - term_center[[term]]
         upr[[term]] <- preds$upr - term_center[[term]]
       }
@@ -572,6 +569,7 @@ neuralGAM <-
 
 .onLoad <- function(libname, pkgname) {
   .disable_tf_logs_env_only()
+  requireNamespace("neuralGAM", quietly = TRUE)
 }
 
 .onAttach <- function(libname, pkgname) {

diff --git a/R/autoplot.neuralGAM.R b/R/autoplot.neuralGAM.R
@@ -71,6 +71,7 @@ ggplot2::autoplot
 #' @importFrom stats predict qnorm
 #' @author Ines Ortega-Fernandez, Marta Sestelo
 #' @method autoplot neuralGAM
+#' @export autoplot.neuralGAM
 #' @export
 autoplot.neuralGAM <- function(object,
                                newdata = NULL,

diff --git a/R/plot.NeuralGAM.R b/R/plot.NeuralGAM.R
@@ -3,7 +3,7 @@
 #' @description
 #' Visualization of a fitted \code{neuralGAM}. Plots learned partial effects, either as
 #' scatter/line plots for continuous covariates or s for factor covariates.
-#' Confidence and/or prediction intervals can be added if available.
+#' Confidence intervals can be added if available.
 #'
 #' @param x A fitted \code{neuralGAM} object as produced by \code{neuralGAM()}.
 #' @param select Character vector of terms to plot. If \code{NULL} (default),

diff --git a/R/predict.NeuralGAM.R b/R/predict.NeuralGAM.R
@@ -79,6 +79,7 @@
 #' }
 #' @importFrom stats predict qnorm setNames
 #' @method predict neuralGAM
+#' @export predict.neuralGAM
 #' @export
 #' @author Ines Ortega-Fernandez, Marta Sestelo
 predict.neuralGAM <- function(object,

diff --git a/cran-comments.md b/cran-comments.md
@@ -1,3 +1,34 @@
+# neuralGAM 2.0.1
+
+This version fixes a minor bug which caused S3 methods to don't be properly exported. 
+
+`predict.neuralGAM` and `autoplot.neuralGAM` are now properly exported, while keeping the S3 registrations. 
+
+## R CMD check results
+
+── Local R CMD check results ──── neuralGAM 2.0.1 ────
+
+❯ checking for future file timestamps ... NOTE
+  unable to verify current time
+
+0 errors ✔ | 0 warnings ✔ | 1 notes ✖
+
+── devtools::check_win_devel() ──── neuralGAM 2.0.1 ──── 
+
+> Installation time in seconds: 52
+> Check time in seconds: 493
+> Status: OK
+> R Under development (unstable) (2025-12-01 r89083 ucrt)
+
+── devtools::check_mac_release() ──── neuralGAM 2.0.1 ──── 
+
+> OK
+
+* Local: `R CMD check` passed on Windows 11 (x86_64, mingw32, R 4.4.1 2024-06-14 ucrt).
+* NOTE related to unability of R to verify current time, which is a known issue reported [here](https://forum.posit.co/t/r-devel-r-cmd-check-failing-because-of-time-unable-to-verify-current-time/25589):
+* GitHub Actions: [R-CMD-check](https://github.com/inesortega/neuralGAM/actions/workflows/R-CMD-check.yaml) passes on macOS, Windows, and Ubuntu (R-release, R-devel, R-oldrel).
+* Coverage: >75% test coverage confirmed with [codecov](https://app.codecov.io/gh/inesortega/neuralGAM).
+
 # neuralGAM 2.0.0
 
 This version introduces major new functionality and internal improvements: