Harden EstemPMM for JSS submission

Author: SZabolotnii

.gitignore

@@ -15,3 +15,6 @@ mc_results_comprehensive/
 
 .claude
 .env.env
+
+*.Rcheck/
+tests/testthat/Rplots.pdf

EstemPMM_0.3.2.tar.gz

@@ -59,6 +59,7 @@ exportClasses(SMAPMM2)
 exportClasses(TS2fit)
 exportClasses(TS3fit)
 exportMethods(AIC)
+exportMethods(BIC)
 exportMethods(coef)
 exportMethods(confint)
 exportMethods(fitted)

NAMESPACE

@@ -23,8 +23,8 @@
 #'
 #' Fuel consumption and vehicle characteristics for 398 automobiles
 #' from the 1970s and 1980s. This dataset is used in published PMM research
-#' to demonstrate both PMM2 (asymmetric residuals: MPG vs Weight) and
-#' PMM3 (symmetric platykurtic residuals: MPG vs Horsepower).
+#' to demonstrate PMM2 on asymmetric regression residuals and to illustrate
+#' dispatcher diagnostics on practical automotive data.
 #'
 #' @format A data frame with 398 rows and 9 variables:
 #' \describe{
@@ -40,14 +40,15 @@
 #' }
 #'
 #' @details
-#' Three regression examples from published PMM papers:
+#' Practical regression examples:
 #' \itemize{
 #'   \item \strong{MPG vs Acceleration} (PMM2, linear): residuals have
 #'     gamma3 = 0.49, g2 = 0.86 (Zabolotnii et al., 2018)
 #'   \item \strong{MPG vs Weight} (PMM2, quadratic): residuals have
 #'     gamma3 = 0.8, g2 = 0.83 (Zabolotnii et al., 2025)
-#'   \item \strong{MPG vs Horsepower} (PMM3, quadratic): residuals have
-#'     gamma3 ~ 0.2, gamma4 = 1.3, g3 = 0.89 (Zabolotnii et al., 2025)
+#'   \item \strong{MPG vs Horsepower} (diagnostic, quadratic): residuals are
+#'     nearly symmetric but have positive excess kurtosis, so the current
+#'     \code{pmm_dispatch()} rule does not treat this as a PMM3 platykurtic case.
 #' }
 #'
 #' @source UCI Machine Learning Repository

R/data.R

@@ -139,6 +139,9 @@ lm_pmm2 <- function(formula, data,
     cat("  Iterations:", iter, "\n")
   }
 
+  # Restore coefficient names from design matrix columns
+  names(b_est) <- colnames(X)
+
   # Return S4 object with all results
   ans <- new("PMM2fit",
              coefficients = b_est,
@@ -215,6 +218,22 @@ setMethod("AIC", "PMM2fit",
             -2 * ll + k * p
           })
 
+#' Calculate BIC for PMM2fit object
+#'
+#' @param object PMM2fit object
+#' @param ... Additional arguments (not used)
+#'
+#' @return BIC value
+#' @export
+setMethod("BIC", "PMM2fit",
+          function(object, ...) {
+            res <- object@residuals
+            n <- length(res)
+            p <- length(object@coefficients)
+            ll <- -n/2 * log(sum(res^2)/n) - n/2 * (1 + log(2*pi))
+            -2 * ll + log(n) * p
+          })
+
 #' Extract log-likelihood from PMM2fit object
 #'
 #' Returns a Gaussian approximate log-likelihood, consistent with the AIC method.
@@ -230,7 +249,7 @@ setMethod("logLik", "PMM2fit",
             n   <- length(res)
             p   <- length(object@coefficients)
             ll  <- -n/2 * log(sum(res^2)/n) - n/2 * (1 + log(2*pi))
-            attr(ll, "df")   <- p + 1L
+            attr(ll, "df")   <- p   # consistent with the AIC method (sigma not counted)
             attr(ll, "nobs") <- n
             class(ll) <- "logLik"
             ll

R/pmm2_main.R

@@ -963,7 +963,7 @@ setMethod("logLik", "TS2fit",
             n   <- length(res)
             p   <- length(object@coefficients)
             ll  <- -n/2 * log(sum(res^2)/n) - n/2 * (1 + log(2*pi))
-            attr(ll, "df")   <- p + 1L
+            attr(ll, "df")   <- p   # consistent with compare_ts_methods AIC convention
             attr(ll, "nobs") <- n
             class(ll) <- "logLik"
             ll
@@ -1041,3 +1041,34 @@ setMethod("confint", "TS2fit",
             if (!missing(parm)) ci <- ci[parm, , drop = FALSE]
             ci
           })
+
+#' AIC for TS2fit objects
+#'
+#' @param object TS2fit (or subclass) object
+#' @param ... Ignored
+#' @param k Penalty per parameter (default 2)
+#' @return Numeric AIC value
+#' @export
+setMethod("AIC", "TS2fit",
+          function(object, ..., k = 2) {
+            res <- object@residuals[is.finite(object@residuals)]
+            n   <- length(res)
+            p   <- length(object@coefficients)
+            ll  <- -n/2 * log(sum(res^2)/n) - n/2 * (1 + log(2*pi))
+            -2 * ll + k * p
+          })
+
+#' BIC for TS2fit objects
+#'
+#' @param object TS2fit (or subclass) object
+#' @param ... Additional arguments (not used)
+#' @return Numeric BIC value
+#' @export
+setMethod("BIC", "TS2fit",
+          function(object, ...) {
+            res <- object@residuals[is.finite(object@residuals)]
+            n   <- length(res)
+            p   <- length(object@coefficients)
+            ll  <- -n/2 * log(sum(res^2)/n) - n/2 * (1 + log(2*pi))
+            -2 * ll + log(n) * p
+          })

R/pmm2_ts_methods.R

@@ -308,6 +308,55 @@ setMethod("AIC", "PMM3fit",
             -2 * ll + k * p
           })
 
+#' Calculate BIC for PMM3fit object
+#'
+#' @param object PMM3fit object
+#' @param ... Additional arguments (not used)
+#'
+#' @return BIC value
+#' @export
+setMethod("BIC", "PMM3fit",
+          function(object, ...) {
+            res <- object@residuals
+            n <- length(res)
+            p <- length(object@coefficients)
+            ll <- -n/2 * log(sum(res^2)/n) - n/2 * (1 + log(2*pi))
+            -2 * ll + log(n) * p
+          })
+
+#' Extract log-likelihood from PMM3fit object
+#'
+#' Returns a Gaussian approximate log-likelihood, consistent with the AIC method.
+#'
+#' @param object PMM3fit object
+#' @param ... Additional arguments (not used)
+#'
+#' @return Object of class \code{logLik}
+#' @export
+setMethod("logLik", "PMM3fit",
+          function(object, ...) {
+            res <- object@residuals
+            n <- length(res)
+            p <- length(object@coefficients)
+            ll <- -n/2 * log(sum(res^2)/n) - n/2 * (1 + log(2*pi))
+            attr(ll, "df") <- p
+            attr(ll, "nobs") <- n
+            class(ll) <- "logLik"
+            ll
+          })
+
+#' Number of observations in PMM3fit object
+#'
+#' @param object PMM3fit object
+#' @param ... Additional arguments (not used)
+#'
+#' @return Integer number of observations
+#' @export
+setMethod("nobs", "PMM3fit",
+          function(object, ...) {
+            length(object@residuals)
+          })
+
 #' Plot diagnostic plots for PMM3fit object
 #'
 #' @param x PMM3fit object

R/pmm3_main.R

@@ -169,6 +169,57 @@ setMethod("AIC", "TS3fit",
             -2 * ll + k * p
           })
 
+#' BIC for TS3fit objects
+#'
+#' @param object TS3fit (or subclass) object
+#' @param ... Additional arguments (not used)
+#'
+#' @return Numeric BIC value
+#' @export
+setMethod("BIC", "TS3fit",
+          function(object, ...) {
+            res <- object@residuals
+            res <- res[is.finite(res)]
+            n <- length(res)
+            p <- length(object@coefficients)
+            ll <- -n/2 * log(sum(res^2)/n) - n/2 * (1 + log(2*pi))
+            -2 * ll + log(n) * p
+          })
+
+#' Extract log-likelihood from TS3fit object
+#'
+#' Returns a Gaussian approximate log-likelihood, consistent with the AIC method.
+#'
+#' @param object TS3fit (or subclass) object
+#' @param ... Additional arguments (not used)
+#'
+#' @return Object of class \code{logLik}
+#' @export
+setMethod("logLik", "TS3fit",
+          function(object, ...) {
+            res <- object@residuals
+            res <- res[is.finite(res)]
+            n <- length(res)
+            p <- length(object@coefficients)
+            ll <- -n/2 * log(sum(res^2)/n) - n/2 * (1 + log(2*pi))
+            attr(ll, "df") <- p
+            attr(ll, "nobs") <- n
+            class(ll) <- "logLik"
+            ll
+          })
+
+#' Number of observations in TS3fit object
+#'
+#' @param object TS3fit (or subclass) object
+#' @param ... Additional arguments (not used)
+#'
+#' @return Integer effective sample size
+#' @export
+setMethod("nobs", "TS3fit",
+          function(object, ...) {
+            sum(is.finite(object@residuals))
+          })
+
 #' Plot diagnostic plots for TS3fit object
 #'
 #' @param x TS3fit object

R/pmm3_ts_methods.R

@@ -49,7 +49,8 @@ pmm_dispatch(residuals(fit_ols))
 
 fit_ols2 <- lm(mpg ~ horsepower + I(horsepower^2), data = na.omit(auto_mpg))
 pmm_dispatch(residuals(fit_ols2))
-# -> Recommends PMM3 (symmetric platykurtic residuals, gamma4 ≈ -1.3)
+# -> Recommends OLS: near-symmetric but positive-kurtosis residuals are not
+#    the symmetric platykurtic regime targeted by PMM3
 ```
 
 ### PMM2 — Asymmetric Errors

README.md

@@ -1,100 +1,19 @@
-═══════════════════════════════════════════════════════════════
-  EstemPMM v0.2.0 - READY FOR CRAN SUBMISSION
-═══════════════════════════════════════════════════════════════
+EstemPMM v0.3.2 -- LOCAL CHECK SUMMARY
+======================================
 
-✅ Package Status: PRODUCTION READY
-✅ R CMD check: 0 errors | 0 warnings | 0 notes
-✅ Documentation: Complete with 3 vignettes
-✅ Tests: All passing (35+ test cases)
-✅ Monte Carlo: Validated (144 configs, >10k model fits)
+Date: 2026-05-14
 
-───────────────────────────────────────────────────────────────
-📦 PACKAGE FILES
-───────────────────────────────────────────────────────────────
-Main files in repository root:
-  ✓ DESCRIPTION           - v0.2.0 metadata
-  ✓ NAMESPACE             - Clean exports (fixed)
-  ✓ NEWS.md               - Version changelog
-  ✓ README.md             - Updated documentation
-  ✓ cran-comments.md      - CRAN submission notes
-  ✓ MONTE_CARLO_README.md - Validation guide
+Local package candidate:
+  EstemPMM_0.3.2.tar.gz
 
-Directory structure:
-  R/              - Source code
-  man/            - Documentation
-  tests/          - Unit tests
-  vignettes/      - 3 vignettes + 1 draft
-  data/           - Example datasets
-  demo/           - Demo scripts
-  dev_scripts/    - Development tools (not in package)
+Latest local check:
+  R CMD check --as-cran EstemPMM_0.3.2.tar.gz
+  Status: 0 ERROR, 0 WARNING, 2 NOTEs
 
-───────────────────────────────────────────────────────────────
-🎯 MONTE CARLO VALIDATION RESULTS
-───────────────────────────────────────────────────────────────
-SAR(1,1)_12:
-  • Avg variance reduction: 32.16%
-  • Peak performance: 61.7% (exponential, n=200)
-  • RMSE improvement: 18.73%
-  • Convergence: 100.0%
+Notes:
+  1. unable to verify current time
+  2. HTML manual validation/math rendering skipped because local HTML Tidy is
+     not recent enough and package V8 is unavailable
 
-SMA(1)_12:
-  • Avg variance reduction: 12.51%
-  • Peak performance: 47.3% (gamma, n=100)
-  • RMSE improvement: 6.95%
-  • Convergence: 99.9%
-
-SARMA(1,0,1,1)_12:
-  • Avg variance reduction: 26.09%
-  • Peak performance: 59.0% (exponential, n=200)
-  • RMSE improvement: 15.49%
-  • Convergence: 99.9%
-
-Innovation type ranking:
-  1. Exponential: 43.2% mean reduction
-  2. Lognormal:   33.5% mean reduction
-  3. Gamma:       29.2% mean reduction
-  4. Gaussian:    baseline
-
-───────────────────────────────────────────────────────────────
-📝 SUBMISSION STEPS
-───────────────────────────────────────────────────────────────
-Option 1 - Using devtools (recommended):
-  R> devtools::submit_cran()
-
-Option 2 - Manual upload:
-  1. Go to: https://cran.r-project.org/submit.html
-  2. Upload tarball (will be built automatically)
-  3. Or build locally:
-     $ R CMD build .
-     $ R CMD check --as-cran EstemPMM_0.2.0.tar.gz
-  4. Upload EstemPMM_0.2.0.tar.gz
-  5. Copy/paste cran-comments.md content
-
-───────────────────────────────────────────────────────────────
-📊 GIT STATUS
-───────────────────────────────────────────────────────────────
-Latest commit: 438be98
-Branch: main
-Status: Clean (all changes committed)
-
-Changes in this release:
-  • Comprehensive Monte Carlo validation
-  • Fixed NAMESPACE exports
-  • Fixed vignette bugs
-  • Cleaned repository structure
-  • Updated all documentation
-
-───────────────────────────────────────────────────────────────
-🚀 NEXT ACTION
-───────────────────────────────────────────────────────────────
-Run in R console:
-  devtools::submit_cran()
-
-Or build tarball:
-  R CMD build .
-
-Then submit at CRAN portal.
-
-═══════════════════════════════════════════════════════════════
-Generated: $(date)
-═══════════════════════════════════════════════════════════════
+This file is an internal local-check summary, not a record of a CRAN upload.
+External CRAN submission still requires explicit authorization.