* using log directory ‘/data/localhost/ripley/R/packages/tests-donttest/gkwdist.Rcheck’ * using R Under development (unstable) (2026-08-24 r90445) * using platform: x86_64-pc-linux-gnu * R was compiled by gcc (GCC) 16.1.1 20260515 (Red Hat 16.1.1-2) GNU Fortran (GCC) 16.1.1 20260515 (Red Hat 16.1.1-2) * running under: Fedora Linux 44 (Server Edition) * using session charset: UTF-8 * current time: 2026-08-25 07:54:48 UTC * checking for file ‘gkwdist/DESCRIPTION’ ... OK * this is package ‘gkwdist’ version ‘1.1.5’ * package encoding: UTF-8 * checking package namespace information ... OK * checking package dependencies ... OK * checking if this is a source package ... OK * checking if there is a namespace ... OK * checking for executable files ... OK * checking for hidden files and directories ... OK * checking for portable file names ... OK * checking for sufficient/correct file permissions ... OK * checking whether package ‘gkwdist’ can be installed ... [95s/113s] OK * used C++ compiler: ‘g++ (GCC) 16.2.1 20260819 (Red Hat 16.2.1-2)’ * checking package directory ... OK * checking ‘build’ directory ... OK * checking DESCRIPTION meta-information ... OK * checking top-level files ... OK * checking for left-over files ... OK * checking index information ... OK * checking package subdirectories ... OK * checking code files for non-ASCII characters ... OK * checking R files for syntax errors ... OK * checking whether the package can be loaded ... OK * checking whether the package can be loaded with stated dependencies ... OK * checking whether the package can be unloaded cleanly ... OK * checking whether the namespace can be loaded with stated dependencies ... OK * checking whether the namespace can be unloaded cleanly ... OK * checking loading without being on the library search path ... OK * checking use of S3 registration ... OK * checking dependencies in R code ... OK * checking S3 generic/method consistency ... OK * checking replacement functions ... OK * checking foreign function calls ... OK * checking R code for possible problems ... [6s/16s] OK * checking Rd files ... OK * checking Rd metadata ... OK * checking Rd line widths ... OK * checking Rd cross-references ... OK * checking for missing documentation entries ... OK * checking for code/documentation mismatches ... OK * checking Rd \usage sections ... OK * checking Rd contents ... OK * checking for unstated dependencies in examples ... OK * checking line endings in C/C++/Fortran sources/headers ... OK * checking line endings in Makefiles ... OK * checking compilation flags in Makevars ... OK * checking for GNU extensions in Makefiles ... OK * checking for portable use of $(BLAS_LIBS) and $(LAPACK_LIBS) ... OK * checking use of PKG_*FLAGS in Makefiles ... OK * checking use of SHLIB_OPENMP_*FLAGS in Makefiles ... OK * checking pragmas in C/C++ headers and code ... OK * checking compilation flags used ... OK * checking compiled code ... OK * checking installed files from ‘inst/doc’ ... OK * checking files in ‘vignettes’ ... OK * checking examples ... OK * checking examples with --run-donttest ... [8s/16s] ERROR Running examples in ‘gkwdist-Ex.R’ failed The error most likely occurred in: > ### Name: grgkw > ### Title: Gradient of the Negative Log-Likelihood for the GKw Distribution > ### Aliases: grgkw > ### Keywords: distribution gradient likelihood optimize > > ### ** Examples > > ## No test: > ## Example 1: Basic Gradient Evaluation > > > # Generate sample data > set.seed(123) > n <- 1000 > true_params <- c(alpha = 2.0, beta = 3.0, gamma = 1.5, delta = 2.0, lambda = 1.8) > data <- rgkw(n, + alpha = true_params[1], beta = true_params[2], + gamma = true_params[3], delta = true_params[4], + lambda = true_params[5] + ) > > # Evaluate gradient at true parameters > grad_true <- grgkw(par = true_params, data = data) > cat("Gradient at true parameters:\n") Gradient at true parameters: > print(grad_true) [1] -34.386342 12.010575 -19.736267 7.392701 -22.078415 > cat("Norm:", sqrt(sum(grad_true^2)), "\n") Norm: 47.52161 > > # Evaluate at different parameter values > test_params <- rbind( + c(1.5, 2.5, 1.2, 1.5, 1.5), + c(2.0, 3.0, 1.5, 2.0, 1.8), + c(2.5, 3.5, 1.8, 2.5, 2.0) + ) > > grad_norms <- apply(test_params, 1, function(p) { + g <- grgkw(p, data) + sqrt(sum(g^2)) + }) > > results <- data.frame( + Alpha = test_params[, 1], + Beta = test_params[, 2], + Gamma = test_params[, 3], + Delta = test_params[, 4], + Lambda = test_params[, 5], + Grad_Norm = grad_norms + ) > print(results, digits = 4) Alpha Beta Gamma Delta Lambda Grad_Norm 1 1.5 2.5 1.2 1.5 1.5 1504.78 2 2.0 3.0 1.5 2.0 1.8 47.52 3 2.5 3.5 1.8 2.5 2.0 1402.07 > > > ## Example 2: Gradient in Optimization > > # Optimization with analytical gradient > fit_with_grad <- optim( + par = c(1.5, 2.5, 1.2, 1.5, 1.5), + fn = llgkw, + gr = grgkw, + data = data, + method = "BFGS", + hessian = TRUE, + control = list(trace = 0, maxit = 1000) + ) > > # Optimization without gradient > fit_no_grad <- optim( + par = c(1.5, 2.5, 1.2, 1.5, 1.5), + fn = llgkw, + data = data, + method = "BFGS", + hessian = TRUE, + control = list(trace = 0, maxit = 1000) + ) > > comparison <- data.frame( + Method = c("With Gradient", "Without Gradient"), + Alpha = c(fit_with_grad$par[1], fit_no_grad$par[1]), + Beta = c(fit_with_grad$par[2], fit_no_grad$par[2]), + Gamma = c(fit_with_grad$par[3], fit_no_grad$par[3]), + Delta = c(fit_with_grad$par[4], fit_no_grad$par[4]), + Lambda = c(fit_with_grad$par[5], fit_no_grad$par[5]), + NegLogLik = c(fit_with_grad$value, fit_no_grad$value), + Iterations = c(fit_with_grad$counts[1], fit_no_grad$counts[1]) + ) > print(comparison, digits = 4, row.names = FALSE) Method Alpha Beta Gamma Delta Lambda NegLogLik Iterations With Gradient 1.341 3.594 0.3551 1.273 13.14 -704.3 363 Without Gradient 1.256 3.386 0.3747 1.403 13.43 -704.3 367 > > > ## Example 3: Verifying Gradient at MLE > > mle <- fit_with_grad$par > names(mle) <- c("alpha", "beta", "gamma", "delta", "lambda") > > # At MLE, gradient should be approximately zero > gradient_at_mle <- grgkw(par = mle, data = data) > cat("\nGradient at MLE:\n") Gradient at MLE: > print(gradient_at_mle) [1] -0.092119735 0.133003627 0.057241796 0.123876850 -0.009348356 > cat("Max absolute component:", max(abs(gradient_at_mle)), "\n") Max absolute component: 0.1330036 > cat("Gradient norm:", sqrt(sum(gradient_at_mle^2)), "\n") Gradient norm: 0.211862 > > > ## Example 4: Numerical vs Analytical Gradient > > # Manual finite difference gradient > numerical_gradient <- function(f, x, data, h = 1e-7) { + grad <- numeric(length(x)) + for (i in seq_along(x)) { + x_plus <- x_minus <- x + x_plus[i] <- x[i] + h + x_minus[i] <- x[i] - h + grad[i] <- (f(x_plus, data) - f(x_minus, data)) / (2 * h) + } + return(grad) + } > > # Compare at MLE > grad_analytical <- grgkw(par = mle, data = data) > grad_numerical <- numerical_gradient(llgkw, mle, data) > > comparison_grad <- data.frame( + Parameter = c("alpha", "beta", "gamma", "delta", "lambda"), + Analytical = grad_analytical, + Numerical = grad_numerical, + Abs_Diff = abs(grad_analytical - grad_numerical), + Rel_Error = abs(grad_analytical - grad_numerical) / + (abs(grad_analytical) + 1e-10) + ) > print(comparison_grad, digits = 8) Parameter Analytical Numerical Abs_Diff Rel_Error 1 alpha -0.0921197354 -0.0921141918 5.5436166e-06 6.0178382e-05 2 beta 0.1330036272 0.1330005261 3.1010248e-06 2.3315340e-05 3 gamma 0.0572417962 0.0572413228 4.7341246e-07 8.2703983e-06 4 delta 0.1238768504 0.1238799996 3.1491757e-06 2.5421826e-05 5 lambda -0.0093483564 -0.0093518793 3.5228696e-06 3.7684373e-04 > > > ## Example 5: Score Test Statistic > > # Score test for H0: theta = theta0 > theta0 <- c(1.8, 2.8, 1.3, 1.8, 1.6) > score_theta0 <- grgkw(par = theta0, data = data) > > # Fisher information at theta0 > fisher_info <- hsgkw(par = theta0, data = data) > > # Score test statistic > score_stat <- t(score_theta0) %*% solve(fisher_info) %*% score_theta0 > p_value <- pchisq(score_stat, df = 5, lower.tail = FALSE) > > cat("\nScore Test:\n") Score Test: > cat("H0: alpha=1.8, beta=2.8, gamma=1.3, delta=1.8, lambda=1.6\n") H0: alpha=1.8, beta=2.8, gamma=1.3, delta=1.8, lambda=1.6 > cat("Test statistic:", score_stat, "\n") Test statistic: 258.9207 > cat("P-value:", format.pval(p_value, digits = 4), "\n") P-value: < 2.2e-16 > > > ## Example 6: Confidence Ellipse (Alpha vs Beta) > > # Observed information > obs_info <- hsgkw(par = mle, data = data) > vcov_full <- solve(obs_info) > vcov_2d <- vcov_full[1:2, 1:2] > > # Create confidence ellipse > theta <- seq(0, 2 * pi, length.out = round(n / 4)) > chi2_val <- qchisq(0.95, df = 2) > > eig_decomp <- eigen(vcov_2d) > ellipse <- matrix(NA, nrow = round(n / 4), ncol = 2) > for (i in 1:round(n / 4)) { + v <- c(cos(theta[i]), sin(theta[i])) + ellipse[i, ] <- mle[1:2] + sqrt(chi2_val) * + (eig_decomp$vectors %*% diag(sqrt(eig_decomp$values)) %*% v) + } Warning in sqrt(eig_decomp$values) : NaNs produced Warning in sqrt(eig_decomp$values) : NaNs produced Warning in sqrt(eig_decomp$values) : NaNs produced Warning in 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sqrt(eig_decomp$values) : NaNs produced Warning in sqrt(eig_decomp$values) : NaNs produced Warning in sqrt(eig_decomp$values) : NaNs produced Warning in sqrt(eig_decomp$values) : NaNs produced Warning in sqrt(eig_decomp$values) : NaNs produced Warning in sqrt(eig_decomp$values) : NaNs produced Warning in sqrt(eig_decomp$values) : NaNs produced Warning in sqrt(eig_decomp$values) : NaNs produced Warning in sqrt(eig_decomp$values) : NaNs produced Warning in sqrt(eig_decomp$values) : NaNs produced Warning in sqrt(eig_decomp$values) : NaNs produced Warning in sqrt(eig_decomp$values) : NaNs produced Warning in sqrt(eig_decomp$values) : NaNs produced > > # Marginal confidence intervals > se_2d <- sqrt(diag(vcov_2d)) Warning in sqrt(diag(vcov_2d)) : NaNs produced > ci_alpha <- mle[1] + c(-1, 1) * 1.96 * se_2d[1] > ci_beta <- mle[2] + c(-1, 1) * 1.96 * se_2d[2] > > # Plot > plot(ellipse[, 1], ellipse[, 2], + type = "l", lwd = 2, col = "#2E4057", + xlab = expression(alpha), ylab = expression(beta), + main = "95% Confidence Region (Alpha vs Beta)", las = 1 + ) Warning in min(x) : no non-missing arguments to min; returning Inf Warning in max(x) : no non-missing arguments to max; returning -Inf Warning in min(x) : no non-missing arguments to min; returning Inf Warning in max(x) : no non-missing arguments to max; returning -Inf Error in plot.window(...) : need finite 'xlim' values Calls: plot -> plot.default -> localWindow -> plot.window Execution halted * checking for unstated dependencies in ‘tests’ ... OK * checking tests ... Running ‘testthat.R’ [44s/63s] [44s/64s] OK * checking for unstated dependencies in vignettes ... OK * checking package vignettes ... OK * checking re-building of vignette outputs ... [11s/35s] OK * checking PDF version of manual ... [8s/21s] OK * checking HTML version of manual ... [7s/16s] OK * checking for non-standard things in the check directory ... OK * checking for detritus in the temp directory ... OK * checking for new files in some other directories ... OK * DONE Status: 1 ERROR See ‘/data/localhost/ripley/R/packages/tests-donttest/gkwdist.Rcheck/00check.log’ for details. Command exited with non-zero status 1 Time 7:29.65, 208.02 + 35.50