* using log directory ‘/data/localhost/ripley/R/packages/tests-MKL/eDMA.Rcheck’
* using R Under development (unstable) (2026-10-28 r90786)
* using platform: x86_64-pc-linux-gnu
* R was compiled by
    gcc (GCC) 16.2.1 20260819 (Red Hat 16.2.1-2)
    GNU Fortran (GCC) 16.2.1 20260819 (Red Hat 16.2.1-2)
* running under: Fedora Linux 44 (Server Edition)
* using session charset: UTF-8
* current time: 2026-10-29 22:20:07 UTC
* using option ‘--no-stop-on-test-error’
* checking for file ‘eDMA/DESCRIPTION’ ... OK
* checking extension type ... Package
* this is package ‘eDMA’ version ‘1.5-5’
* 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 ‘eDMA’ can be installed ... [62s/133s] OK
* used C++ compiler: ‘g++ (GCC) 16.2.1 20260819 (Red Hat 16.2.1-2)’
* checking package 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 ... [4s/11s] 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 contents of ‘data’ directory ... OK
* checking data for non-ASCII characters ... OK
* checking LazyData ... OK
* checking data for ASCII and uncompressed saves ... 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 examples ... OK
* checking for unstated dependencies in ‘tests’ ... OK
* checking tests ...
  Running ‘regression.R’
 ERROR
Running the tests in ‘tests/regression.R’ failed.
Complete output:
  > library(eDMA)
  Loading required package: zoo
  
  Attaching package: 'zoo'
  
  The following objects are masked from 'package:base':
  
      as.Date, as.Date.numeric
  
  Loading required package: xts
  Loading required package: parallel
  > checks <- 0L
  > check <- function(ok, label) {
  +   if (!isTRUE(ok)) stop(label, call. = FALSE)
  +   checks <<- checks + 1L
  +   cat(sprintf("PASS %03d %s\n", checks, label))
  + }
  > close <- function(a, b, tolerance = 2e-9) isTRUE(all.equal(as.numeric(a), as.numeric(b), tolerance = tolerance))
  > fails <- function(expr, pattern = NULL) {
  +   result <- tryCatch({force(expr); NULL}, error = identity)
  +   inherits(result, "error") && (is.null(pattern) || grepl(pattern, conditionMessage(result)))
  + }
  > 
  > # Independent small-model implementation: ordinary probability arithmetic,
  > # base R dt(), and the subtractive Kalman covariance formula.
  > reference <- function(y, X, deltas, alpha, beta, g = 4, S0 = 1, n0 = 2, required = integer()) {
  +   K <- ncol(X); T <- length(y); D <- length(deltas)
  +   models <- lapply(seq_len(2^K - 1L), function(mask) which(as.logical(intToBits(mask)[seq_len(K)])))
  +   models <- Filter(function(idx) all(required %in% idx), models)
  +   M <- length(models)
  +   states <- lapply(seq_len(M * D), function(h) {
  +     p <- length(models[[(h - 1L) %% M + 1L]])
  +     list(m = numeric(p), C = diag(g, p), S = S0)
  +   })
  +   a <- matrix(1/M, M, D); b <- rep(1/D, D); n <- n0
  +   output <- list(vyhat = numeric(T), vLpdfhat = numeric(T), mmhat = matrix(0,T,K),
  +                  mcovhat = matrix(0,T,K), mincpmt = matrix(0,T,K), mpmt = matrix(0,T,D),
  +                  mpmt_pred = matrix(0,T,D), mLpdf = matrix(0,T,D), mvdec = matrix(0,T,5),
  +                  vShat = numeric(T), vsize = numeric(T), vdeltahat = numeric(T),
  +                  vyhat_DMS = numeric(T), vLpdfhat_DMS = numeric(T),
  +                  vsize_DMS = numeric(T), vhighmp_DMS = numeric(T), vhighmpTop01_DMS = numeric(T))
  +   for (t in seq_len(T)) {
  +     ap <- a^alpha; ap <- sweep(ap, 2, colSums(ap), "/")
  +     bp <- b^alpha / sum(b^alpha)
  +     output$mpmt_pred[t, ] <- bp
  +     nu <- beta * n + 1
  +     f <- obs <- coeff <- density <- matrix(0, M, D)
  +     means <- variances <- array(0, c(K, M, D))
  +     for (d in seq_len(D)) for (j in seq_len(M)) {
  +       h <- (d-1L)*M+j; s <- states[[h]]; idx <- models[[j]]; x <- X[t, idx]
  +       R <- s$C / if (t == 1L) 1 else deltas[d]
  +       f[j,d] <- sum(x*s$m); coeff[j,d] <- drop(t(x)%*%R%*%x); obs[j,d] <- s$S
  +       q <- coeff[j,d]+s$S; e <- y[t]-f[j,d]
  +       density[j,d] <- dt(e/sqrt(q), df=nu)/sqrt(q)
  +       gain <- drop(R%*%x)/q
  +       s$m <- s$m+gain*e
  +       s$C <- R-tcrossprod(gain)*q
  +       s$S <- s$S+(s$S/nu)*(e^2/q-1)
  +       states[[h]] <- s
  +       means[idx,j,d] <- s$m; variances[idx,j,d] <- diag(s$C)
  +     }
  +     mu <- colSums(ap*f); overall <- sum(bp*mu)
  +     output$vyhat[t] <- overall
  +     vo <- sum(bp*colSums(ap*obs))*nu/(nu-2)
  +     vc <- sum(bp*colSums(ap*coeff))*nu/(nu-2)
  +     vm <- sum(bp*colSums(ap*sweep(f,2,mu,"-")^2))
  +     vt <- sum(bp*(mu-overall)^2)
  +     output$mvdec[t,] <- c(vo+vc+vm+vt,vo,vc,vm,vt)
  +     ds <- which.max(bp); js <- which.max(ap[,ds])
  +     output$vyhat_DMS[t] <- f[js,ds]
  +     output$vLpdfhat_DMS[t] <- log(density[js,ds])
  +     ld <- colSums(ap*density); normalizer <- sum(bp*ld)
  +     output$vLpdfhat[t] <- log(normalizer); output$mLpdf[t,] <- log(ld)
  +     a <- sweep(ap*density,2,ld,"/"); b <- bp*ld/normalizer
  +     joint <- sweep(a,2,b,"*"); marginal <- rowSums(joint)
  +     for (k in seq_len(K)) {
  +       output$mmhat[t,k] <- sum(joint*means[k,,])
  +       output$mcovhat[t,k] <- sum(joint*(variances[k,,]+(means[k,,]-output$mmhat[t,k])^2))
  +       output$mincpmt[t,k] <- sum(marginal[vapply(models,function(idx) k %in% idx,logical(1))])
  +     }
  +     output$mpmt[t,] <- b
  +     output$vShat[t] <- sum(as.vector(joint)*vapply(states,`[[`,numeric(1),"S"))
  +     output$vsize[t] <- sum(marginal*lengths(models))
  +     output$vdeltahat[t] <- sum(b*deltas)
  +     output$vsize_DMS[t] <- lengths(models)[which.max(marginal)]
  +     output$vhighmp_DMS[t] <- max(marginal)
  +     output$vhighmpTop01_DMS[t] <- sum(sort(marginal,decreasing=TRUE)[seq_len(ceiling(.1*M))])
  +     n <- nu
  +   }
  +   output
  + }
  > set.seed(181)
  > dat <- data.frame(y = rnorm(45), x = rnorm(45), z = rnorm(45), w = rnorm(45))
  > args <- list(formula = y~x+z+w, data = dat, vDelta = c(.8,.97), dAlpha = .83,
  +              dBeta = .96, dG = 4, bParallelize = FALSE)
  > fit <- do.call(DMA,args)
  > ref <- reference(dat$y,model.matrix(y~x+z+w,dat),args$vDelta,args$dAlpha,args$dBeta)
  > for (nm in names(ref)) check(close(fit@Est[[nm]],ref[[nm]]),paste("Independent numerical reference:",nm))
  PASS 001 Independent numerical reference: vyhat
  PASS 002 Independent numerical reference: vLpdfhat
  PASS 003 Independent numerical reference: mmhat
  PASS 004 Independent numerical reference: mcovhat
  PASS 005 Independent numerical reference: mincpmt
  PASS 006 Independent numerical reference: mpmt
  PASS 007 Independent numerical reference: mpmt_pred
  PASS 008 Independent numerical reference: mLpdf
  PASS 009 Independent numerical reference: mvdec
  PASS 010 Independent numerical reference: vShat
  PASS 011 Independent numerical reference: vsize
  PASS 012 Independent numerical reference: vdeltahat
  PASS 013 Independent numerical reference: vyhat_DMS
  PASS 014 Independent numerical reference: vLpdfhat_DMS
  PASS 015 Independent numerical reference: vsize_DMS
  PASS 016 Independent numerical reference: vhighmp_DMS
  PASS 017 Independent numerical reference: vhighmpTop01_DMS
  > check(all(fit@Est$vhighmpTop01_DMS > fit@Est$vhighmp_DMS),"F11 top ten percent includes more than the largest model")
  PASS 018 F11 top ten percent includes more than the largest model
  > parargs <- args; parargs$bParallelize <- TRUE; parargs$iCores <- 2L
  > parallel <- suppressWarnings(do.call(DMA,parargs))
  > for (nm in names(ref)) check(close(fit@Est[[nm]],parallel@Est[[nm]],0),paste("Sequential/parallel equality:",nm))
  PASS 019 Sequential/parallel equality: vyhat
  PASS 020 Sequential/parallel equality: vLpdfhat
  PASS 021 Sequential/parallel equality: mmhat
  PASS 022 Sequential/parallel equality: mcovhat
  PASS 023 Sequential/parallel equality: mincpmt
  PASS 024 Sequential/parallel equality: mpmt
  PASS 025 Sequential/parallel equality: mpmt_pred
  PASS 026 Sequential/parallel equality: mLpdf
  PASS 027 Sequential/parallel equality: mvdec
  PASS 028 Sequential/parallel equality: vShat
  PASS 029 Sequential/parallel equality: vsize
  PASS 030 Sequential/parallel equality: vdeltahat
  PASS 031 Sequential/parallel equality: vyhat_DMS
  PASS 032 Sequential/parallel equality: vLpdfhat_DMS
  PASS 033 Sequential/parallel equality: vsize_DMS
  PASS 034 Sequential/parallel equality: vhighmp_DMS
  PASS 035 Sequential/parallel equality: vhighmpTop01_DMS
  > fixedargs <- args; fixedargs$vKeep <- c(1,3)
  > fixed <- do.call(DMA,fixedargs)
  > fixedref <- reference(dat$y,model.matrix(y~x+z+w,dat),args$vDelta,args$dAlpha,args$dBeta,required=c(1,3))
  > check(close(fixed@Est$vyhat,fixedref$vyhat) && fixed@Est$iM==4,"Direct required-column enumeration")
  PASS 036 Direct required-column enumeration
  > check(all(abs(fixed@Est$mincpmt[,c(1,3)]-1)<1e-12),"Required columns have unit inclusion probability")
  PASS 037 Required columns have unit inclusion probability
  > 
  > # Alter the current/future response without affecting any current prediction moments.
  > changed <- args; changed$data$y[25:45] <- changed$data$y[25:45]+20
  > altered <- do.call(DMA,changed)
  > check(close(fit@Est$vyhat[1:25],altered@Est$vyhat[1:25]),"F1 point forecast causality")
  PASS 038 F1 point forecast causality
  > check(close(fit@Est$mvdec[1:25,],altered@Est$mvdec[1:25,]),"F5 predictive variance causality")
  PASS 039 F5 predictive variance causality
  > check(!close(fit@Est$mmhat[25,],altered@Est$mmhat[25,]),"F6 filtered means react to the current observation")
  PASS 040 F6 filtered means react to the current observation
  > check(close(fit@Est$mvdec[,1],rowSums(fit@Est$mvdec[,-1,drop=FALSE])),"Predictive variance components add up")
  PASS 041 Predictive variance components add up
  > 
  > futureargs <- args; futureargs$data <- rbind(dat,data.frame(y=NA_real_,x=6,z=-2,w=3))
  > future <- do.call(DMA,futureargs)
  > for (nm in names(ref)) check(close(fit@Est[[nm]],future@Est[[nm]],0),paste("F3 terminal prediction preserves history:",nm))
  PASS 042 F3 terminal prediction preserves history: vyhat
  PASS 043 F3 terminal prediction preserves history: vLpdfhat
  PASS 044 F3 terminal prediction preserves history: mmhat
  PASS 045 F3 terminal prediction preserves history: mcovhat
  PASS 046 F3 terminal prediction preserves history: mincpmt
  PASS 047 F3 terminal prediction preserves history: mpmt
  PASS 048 F3 terminal prediction preserves history: mpmt_pred
  PASS 049 F3 terminal prediction preserves history: mLpdf
  PASS 050 F3 terminal prediction preserves history: mvdec
  PASS 051 F3 terminal prediction preserves history: vShat
  PASS 052 F3 terminal prediction preserves history: vsize
  PASS 053 F3 terminal prediction preserves history: vdeltahat
  PASS 054 F3 terminal prediction preserves history: vyhat_DMS
  PASS 055 F3 terminal prediction preserves history: vLpdfhat_DMS
  PASS 056 F3 terminal prediction preserves history: vsize_DMS
  PASS 057 F3 terminal prediction preserves history: vhighmp_DMS
  PASS 058 F3 terminal prediction preserves history: vhighmpTop01_DMS
  > scoredargs <- futureargs; scoredargs$data$y[46] <- 8
  > scored <- do.call(DMA,scoredargs)
  > last <- getLastForecast(future)
  > check(close(last$PointForecast,scored@Est$vyhat[46]),"Future mean equals the pre-update forecast")
  PASS 059 Future mean equals the pre-update forecast
  > check(close(last$VarianceDecomposition,scored@Est$mvdec[46,]),"F4 variance belongs to the future predictor row")
  PASS 060 F4 variance belongs to the future predictor row
  > check(!close(last$VarianceDecomposition,fit@Est$mvdec[45,]),"Future variance is not the prior row's variance")
  PASS 061 Future variance is not the prior row's variance
  > native <- eDMA:::funcEstimate_Eff(c(dat$y,NA_real_), model.matrix(~x+z+w,futureargs$data), args$vDelta,.83,-9999,.96,FALSE,4)
  > check(all(is.na(native$veps[46,])) && is.na(native$vLpdfhat[46]),"Missing response is not scored")
  PASS 062 Missing response is not scored
  > check(close(native$mpmt[46,],native$mpmt[45,]),"Missing response does not update posterior probabilities")
  PASS 063 Missing response does not update posterior probabilities
  > check(close(native$mmhat[46,],native$mmhat[45,]),"Missing response does not update coefficients")
  PASS 064 Missing response does not update coefficients
  > 
  > # Quadrature of the initial predictive mixture, independent of decomposition formulas.
  > X <- model.matrix(y~x+z+w,dat); xx <- X[1,]
  > subsets <- lapply(1:15,function(mask) which(as.logical(intToBits(mask)[1:4])))
  > q <- vapply(subsets,function(idx) 1+4*sum(xx[idx]^2),numeric(1))
  > nu <- .96*2+1
  > mixdensity <- function(y) vapply(y,function(value) mean(dt(value/sqrt(q),nu)/sqrt(q)),numeric(1))
  > check(abs(integrate(mixdensity,-Inf,Inf)$value-1)<1e-7,"F2 predictive density integrates to one")
  PASS 065 F2 predictive density integrates to one
  > second <- integrate(function(y) y^2*mixdensity(y),-Inf,Inf,rel.tol=1e-7)$value
  > check(abs(second-fit@Est$mvdec[1,1])<1e-5,"F5 variance agrees with Student-t density integration")
  PASS 066 F5 variance agrees with Student-t density integration
  > 
  > # Startup, dimensions, NA, time indexes, parameter boundaries and helper contracts.
  > single <- DMA(y~1,dat,vDelta=.95,bParallelize=FALSE)
  > check(nrow(coef(single,iBurnPeriod=0))==45,"Zero burn-in retains all observations")
  PASS 067 Zero burn-in retains all observations
  > check(identical(dim(coef(single,iBurnPeriod=44)),c(1L,1L)),"Single-row single-column extraction")
  PASS 068 Single-row single-column extraction
  > check(identical(dim(as.data.frame(single,which="mpmt",iBurnPeriod=5)),c(40L,1L)),"Single-delta extraction after burn-in")
  PASS 069 Single-delta extraction after burn-in
  > check(close(BacktestDMA(single,0)[1,1],mean(single@Est$veps^2)),"Zero burn-in backtest")
  PASS 070 Zero burn-in backtest
  > warned <- FALSE
  > invisible(withCallingHandlers(BacktestDMA(single),warning=function(w) {warned <<- TRUE; invokeRestart("muffleWarning")}))
  > check(warned,"Default burn-in emits a warning")
  PASS 071 Default burn-in emits a warning
  > check(fails(getLastForecast(single),"No future forecast"),"Useful error for absent future forecast")
  PASS 072 Useful error for absent future forecast
  > check(fails(coef(single,iBurnPeriod=45)) && fails(coef(single,iBurnPeriod=-1)) && fails(coef(single,iBurnPeriod=.5)),"Burn-in boundaries validated")
  PASS 073 Burn-in boundaries validated
  > check(fails(residuals(single,type="bad")) && fails(pred.like(single,type="bad")),"Residual and likelihood types validated")
  PASS 074 Residual and likelihood types validated
  > check(close(sd(as.numeric(residuals(single,standardize=TRUE))),1),"Residual standardization uses global sample SD")
  PASS 075 Residual standardization uses global sample SD
  > one <- DMA(y~1,data.frame(y=0),bParallelize=FALSE,vDelta=1)
  > check(is.finite(one@Est$mvdec[1,1]) && one@Est$vyhat[1]==0 && one@Est$mmhat[1]==0,"F8 zero first response and one observation are defined")
  PASS 076 F8 zero first response and one observation are defined
  > zero <- DMA(y~0+x,data.frame(y=c(0,1,0),x=c(0,1,2)),vDelta=1,bParallelize=FALSE)
  > check(all(is.finite(zero@Est$mvdec)) && all(is.finite(zero@Est$vLpdfhat)),"F8 zero initial design is supported")
  PASS 077 F8 zero initial design is supported
  > smallnu <- DMA(y~1,dat,vDelta=1,dBeta=.1,bParallelize=FALSE)
  > check(is.infinite(smallnu@Est$vtotal[1]),"Infinite predictive variance for degrees of freedom at most two")
  PASS 078 Infinite predictive variance for degrees of freedom at most two
  > large <- dat; large$y[1] <- 1e50
  > largefit <- DMA(y~x+z+w,large,bParallelize=FALSE)
  > check(all(is.finite(largefit@Est$vLpdfhat)) && all(is.finite(largefit@Est$mpmt)),"F9 extreme observation does not underflow probability normalization")
  PASS 079 F9 extreme observation does not underflow probability normalization
  > for (keep in list(character(),"unknown",c("x","unknown"),c(1,1),0,5,.5,NA_real_,c("KS","x")))
  +   check(fails(DMA(y~x,dat,vKeep=keep,bParallelize=FALSE)),paste("Invalid vKeep rejected:",paste(keep,collapse=",")))
  PASS 080 Invalid vKeep rejected: 
  PASS 081 Invalid vKeep rejected: unknown
  PASS 082 Invalid vKeep rejected: x,unknown
  PASS 083 Invalid vKeep rejected: 1,1
  PASS 084 Invalid vKeep rejected: 0
  PASS 085 Invalid vKeep rejected: 5
  PASS 086 Invalid vKeep rejected: 0.5
  PASS 087 Invalid vKeep rejected: NA
  PASS 088 Invalid vKeep rejected: KS,x
  > for (entry in list(list(vDelta=numeric()),list(vDelta=0),list(vDelta=NA_real_),list(dAlpha=1.1),list(dBeta=0),
  +                    list(dG=0),list(dS0=0),list(dN0=0),list(iCores=0),list(iCores=1.2),list(bParallelize=NA),list(bZellnerPrior=1))) {
  +   bad <- utils::modifyList(args,entry)
  +   check(fails(do.call(DMA,bad)),paste("Invalid parameter rejected:",names(entry)))
  + }
  PASS 089 Invalid parameter rejected: vDelta
  PASS 090 Invalid parameter rejected: vDelta
  PASS 091 Invalid parameter rejected: vDelta
  PASS 092 Invalid parameter rejected: dAlpha
  PASS 093 Invalid parameter rejected: dBeta
  PASS 094 Invalid parameter rejected: dG
  PASS 095 Invalid parameter rejected: dS0
  PASS 096 Invalid parameter rejected: dN0
  PASS 097 Invalid parameter rejected: iCores
  PASS 098 Invalid parameter rejected: iCores
  PASS 099 Invalid parameter rejected: bParallelize
  PASS 100 Invalid parameter rejected: bZellnerPrior
  > check(fails(DMA(y~0,dat)),"Zero-column design rejected")
  PASS 101 Zero-column design rejected
  > check(fails(DMA(y~x,data.frame(y=c(NA,NA),x=1:2))),"All-missing response rejected")
  PASS 102 All-missing response rejected
  > check(fails(DMA(y~x,data.frame(y=factor(c("a","b")),x=1:2))),"Nonnumeric response rejected")
  PASS 103 Nonnumeric response rejected
  > check(fails(DMA(y~x,data.frame(y=c(1,Inf),x=1:2))),"Nonfinite observed response rejected")
  PASS 104 Nonfinite observed response rejected
  > check(fails(DMA(y~x,data.frame(y=c(1,NA),x=c(1,NA)))),"Missing future predictor rejected")
  PASS 105 Missing future predictor rejected
  > check(fails(DMA(y~x,data.frame(y=1:5,x=rep(1,5)),bZellnerPrior=TRUE)),"Rank-deficient Zellner prior rejected before workers")
  PASS 106 Rank-deficient Zellner prior rejected before workers
  > zargs <- args; zargs$bZellnerPrior <- TRUE
  > zfit <- do.call(DMA,zargs); zargs$data <- futureargs$data; zfuture <- do.call(DMA,zargs)
  > check(close(zfit@Est$vyhat,zfuture@Est$vyhat,0),"Zellner prior excludes the forecast-only row")
  Error: Zellner prior excludes the forecast-only row
  Execution halted
* checking PDF version of manual ... [4s/11s] OK
* checking HTML version of manual ... OK
* checking for non-standard things in the check directory ... OK
* checking for detritus in the temp directory ... OK
* DONE

Status: 1 ERROR
See
  ‘/data/localhost/ripley/R/packages/tests-MKL/eDMA.Rcheck/00check.log’
for details.

Command exited with non-zero status 1
Time 3:32.62, 80.31 + 11.02