Running 'testthat.R' [5s]
Running the tests in 'tests/testthat.R' failed.
Complete output:
  > library(testthat)
  > library(vtreat)
  > 
  > test_check("vtreat")
  -- 1. Error: testBO: Works As Expected (@testBO.R#6)  --------------------------
  'getDefaultCluster' is not an exported object from 'namespace:parallel'
  1: suppressWarnings({
         synthFrame <- function(n, censorD) {
             stringReps = c(rep("a", 100), rep("b", 100), "c", rep("d", 20))
             d <- data.frame(xN1 = runif(n), xN2 = runif(n), xN3 = 0, xL1 = sample(c(TRUE, 
                 FALSE), size = n, replace = TRUE), xL2 = sample(c(TRUE, FALSE, NA), size = n, 
                 replace = TRUE), xL3 = FALSE, xS1 = sample(stringReps, size = n, replace = TRUE), 
                 xS2 = sample(c(stringReps, NA), size = n, replace = TRUE), xS3 = "a", 
                 xF1 = sample(as.factor(c(100:200)), size = n, replace = TRUE), xF2 = sample(as.factor(c("a", 
                     "b", "c", "d", NA)), size = n, replace = TRUE), xF3 = as.factor(c("a")), 
                 xI1 = sample(as.integer(c(1, 2, 3)), size = n, replace = TRUE), xI2 = sample(as.integer(c(1, 
                     2, 3, NA)), size = n, replace = TRUE), xI3 = as.integer(c(1)), xU1 = NA, 
                 stringsAsFactors = FALSE)
             now <- Sys.time()
             d$t1 <- as.POSIXct(now + ceiling(as.numeric(now) * runif(n)/10))
             d$t2 <- as.POSIXlt(now + ceiling(as.numeric(now) * runif(n)/10))
             d[sample(1:n, 5, replace = T), "xN2"] <- NA
             d[sample(1:n, 5, replace = T), "xN2"] <- NaN
             d[sample(1:n, 5, replace = T), "t1"] <- NA
             d[sample(1:n, 5, replace = T), "t2"] <- NA
             if (censorD) {
                 dFree <- rowSums(as.matrix(sapply(d, function(c) {
                     ifelse(is.na(c), 0, ifelse(as.character(c) == "d", 1, 0))
                 }))) <= 0
                 d <- d[dFree, ]
                 n <- dim(d)[[1]]
             }
             toNum <- function(v) {
                 if (class(v)[[1]] == "character") {
                     v <- as.factor(v)
                 }
                 v <- as.numeric(v)
                 meanY <- mean(v, na.rm = TRUE)
                 if (is.na(meanY)) {
                     meanY <- 0
                 }
                 v[is.na(v)] <- meanY
                 v <- v - meanY
                 range <- max(1, max(v) - min(v))
                 v <- v/range
                 v
             }
             dN <- as.matrix(sapply(d, toNum))
             d$yN <- rowSums(dN) + runif(n)
             d$yC <- d$yN >= median(d$yN)
             d
         }
         set.seed(26236)
         dTrain <- synthFrame(200, TRUE)
         dTest <- synthFrame(20, FALSE)
         vars <- setdiff(colnames(dTrain), c("yN", "yC"))
         verbose = FALSE
         for (smFactor in c(0, 0.5)) {
             for (scale in c(FALSE, TRUE)) {
                 if (verbose) {
                     print(paste("**********************", smFactor, scale))
                     print("# numeric example")
                 }
                 treatmentsN <- designTreatmentsN(dTrain, vars, "yN", smFactor = smFactor, 
                     rareCount = 2, rareSig = 0.5, verbose = verbose)
                 dTrainNTreated <- prepare(treatmentsN, dTrain, pruneSig = 0.99, scale = scale)
                 nvars <- setdiff(colnames(dTrainNTreated), "yN")
                 if (scale) {
                     expect_true(max(abs(vapply(dTrainNTreated[, nvars], mean, numeric(1)))) < 
                       1e-05)
                     expect_true(max(abs(1 - vapply(nvars, function(c) {
                       lm(paste("yN", c, sep = "~"), data = dTrainNTreated)$coefficients[[2]]
                     }, numeric(1)))) < 1e-05)
                 }
                 modelN <- lm(paste("yN", paste(nvars, collapse = " + "), sep = " ~ "), 
                     data = dTrainNTreated)
                 dTestNTreated <- prepare(treatmentsN, dTest, pruneSig = 0.99, scale = scale)
                 dTestNTreated$pred <- predict(modelN, newdata = dTestNTreated)
                 if (verbose) {
                     print(summary(modelN))
                 }
                 if (verbose) {
                     print("# caterogic example")
                 }
                 treatmentsC <- designTreatmentsC(dTrain, vars, "yC", TRUE, smFactor = smFactor, 
                     catScaling = TRUE, verbose = verbose)
                 dTrainCTreated <- prepare(treatmentsC, dTrain, pruneSig = 0.99, scale = scale)
                 cvars <- setdiff(colnames(dTrainCTreated), "yC")
                 if (scale) {
                     expect_true(max(abs(vapply(dTrainCTreated[, cvars], mean, numeric(1)))) < 
                       1e-05)
                     expect_true(max(abs(1 - vapply(cvars, function(c) {
                       glm(paste("yC", c, sep = "~"), family = binomial, data = dTrainCTreated)$coefficients[[2]]
                     }, numeric(1)))) < 1e-05)
                 }
                 modelC <- glm(paste("yC", paste(cvars, collapse = " + "), sep = " ~ "), 
                     data = dTrainCTreated, family = binomial(link = "logit"))
                 dTestCTreated <- prepare(treatmentsC, dTest, pruneSig = 0.99, scale = scale)
                 dTestCTreated$pred <- predict(modelC, newdata = dTestCTreated, type = "response")
                 if (verbose) {
                     print(summary(modelC))
                 }
             }
         }
     }) at testthat/testBO.R:6
  2: withCallingHandlers(expr, warning = function(w) invokeRestart("muffleWarning"))
  3: designTreatmentsN(dTrain, vars, "yN", smFactor = smFactor, rareCount = 2, rareSig = 0.5, 
         verbose = verbose) at testthat/testBO.R:72
  4: .designTreatmentsX(dframe, varlist, outcomename, ycol, c(), c(), weights, minFraction, 
         smFactor, rareCount, rareSig, collarProb, codeRestriction, customCoders, splitFunction, 
         ncross, forceSplit, catScaling, verbose = verbose, parallelCluster = parallelCluster, 
         use_parallel = use_parallel)
  5: .designTreatmentsXS(dframe, varlist, outcomename, zoY, zC, zTarget, weights, minFraction, 
         smFactor, rareCount, rareSig, collarProb, codeRestriction, customCoders, FALSE, 
         catScaling, verbose = verbose, parallelCluster = parallelCluster, use_parallel = use_parallel)
  6: lapply(varlist, function(v) {
         levRestriction <- c()
         hasRange <- FALSE
         vcolOrig <- dframe[[v]]
         vcol <- NULL
         if (length(vcolOrig) != nRows) {
             warning(paste("wrong column length", v))
         }
         else {
             vcol <- .cleanColumn(vcolOrig, nRows)
             if (.has.range(vcol)) {
                 hasRange <- TRUE
                 colclass <- class(vcol)
                 if ((colclass == "character") || (colclass == "factor")) {
                     if (!is.null(zC)) {
                       levRestriction <- .safeLevelsC(vcol, zC, zTarget, weights, minFraction, 
                         rareCount, rareSig, parallelCluster = parallelCluster, use_parallel = use_parallel)
                     }
                     else {
                       levRestriction <- .safeLevelsR(vcol, zoY, weights, minFraction, 
                         rareCount, rareSig, parallelCluster = parallelCluster, use_parallel = use_parallel)
                     }
                 }
             }
         }
         list(v = v, vcolOrig = vcolOrig, vcol = vcol, hasRange = hasRange, levRestriction = levRestriction)
     })
  7: FUN(X[[i]], ...)
  8: .safeLevelsR(vcol, zoY, weights, minFraction, rareCount, rareSig, parallelCluster = parallelCluster, 
         use_parallel = use_parallel)
  9: plapply(safeLevs, aovCalc, parallelCluster = parallelCluster, use_parallel = use_parallel)
  10: parallel::getDefaultCluster
  11: getExportedValue(pkg, name)
  12: stop(gettextf("'%s' is not an exported object from 'namespace:%s'", name, getNamespaceName(ns)), 
         call. = FALSE, domain = NA)
  
  -- 2. Error: testCar: Numeric Var Scores as expected car (@testCar.R#14)  ------
  'getDefaultCluster' is not an exported object from 'namespace:parallel'
  1: designTreatmentsC(uci.car.data, pvars, dYName, dYTarget, verbose = FALSE) at testthat/testCar.R:14
  2: .designTreatmentsX(dframe, varlist, outcomename, zoY, dframe[[outcomename]], outcometarget, 
         weights, minFraction, smFactor, rareCount, rareSig, collarProb, codeRestriction, 
         customCoders, splitFunction, ncross, forceSplit, catScaling, verbose = verbose, 
         parallelCluster = parallelCluster, use_parallel = use_parallel)
  3: .designTreatmentsXS(dframe, varlist, outcomename, zoY, zC, zTarget, weights, minFraction, 
         smFactor, rareCount, rareSig, collarProb, codeRestriction, customCoders, FALSE, 
         catScaling, verbose = verbose, parallelCluster = parallelCluster, use_parallel = use_parallel)
  4: plapply(workList, worker, parallelCluster = parallelCluster, use_parallel = use_parallel)
  5: parallel::getDefaultCluster
  6: getExportedValue(pkg, name)
  7: stop(gettextf("'%s' is not an exported object from 'namespace:%s'", name, getNamespaceName(ns)), 
         call. = FALSE, domain = NA)
  
  -- 3. Error: testNoY: Can transform without Y (@testNoY.R#10)  -----------------
  'getDefaultCluster' is not an exported object from 'namespace:parallel'
  1: designTreatmentsZ(dTrainZ, colnames(dTrainZ), rareCount = 0, verbose = FALSE) at testthat/testNoY.R:10
  2: .designTreatmentsX(dframe, varlist, outcomename, ycol, c(), c(), weights, minFraction, 
         smFactor = 0, rareCount, rareSig = 1, collarProb, codeRestriction, customCoders, 
         FALSE, NULL, 3, catScaling, verbose = verbose, parallelCluster = parallelCluster, 
         use_parallel = use_parallel)
  3: .designTreatmentsXS(dframe, varlist, outcomename, zoY, zC, zTarget, weights, minFraction, 
         smFactor, rareCount, rareSig, collarProb, codeRestriction, customCoders, FALSE, 
         catScaling, verbose = verbose, parallelCluster = parallelCluster, use_parallel = use_parallel)
  4: plapply(workList, worker, parallelCluster = parallelCluster, use_parallel = use_parallel)
  5: parallel::getDefaultCluster
  6: getExportedValue(pkg, name)
  7: stop(gettextf("'%s' is not an exported object from 'namespace:%s'", name, getNamespaceName(ns)), 
         call. = FALSE, domain = NA)
  
  [1] "vtreat 1.2.2 start initial treatment design Tue Jul 10 15:52:58 2018"
  [1] " start cross frame work Tue Jul 10 15:52:58 2018"
  [1] " vtreat::mkCrossFrameNExperiment done Tue Jul 10 15:52:58 2018"
  -- 4. Error: testParallel: Parallel works (@testParallel.R#30)  ----------------
  'getDefaultCluster' is not an exported object from 'namespace:parallel'
  1: designTreatmentsC(uci.car.data, pvars, dYName, dYTarget, verbose = FALSE) at testthat/testParallel.R:30
  2: .designTreatmentsX(dframe, varlist, outcomename, zoY, dframe[[outcomename]], outcometarget, 
         weights, minFraction, smFactor, rareCount, rareSig, collarProb, codeRestriction, 
         customCoders, splitFunction, ncross, forceSplit, catScaling, verbose = verbose, 
         parallelCluster = parallelCluster, use_parallel = use_parallel)
  3: .designTreatmentsXS(dframe, varlist, outcomename, zoY, zC, zTarget, weights, minFraction, 
         smFactor, rareCount, rareSig, collarProb, codeRestriction, customCoders, FALSE, 
         catScaling, verbose = verbose, parallelCluster = parallelCluster, use_parallel = use_parallel)
  4: plapply(workList, worker, parallelCluster = parallelCluster, use_parallel = use_parallel)
  5: parallel::getDefaultCluster
  6: getExportedValue(pkg, name)
  7: stop(gettextf("'%s' is not an exported object from 'namespace:%s'", name, getNamespaceName(ns)), 
         call. = FALSE, domain = NA)
  
  -- 5. Error: testScale: check corner case on scale (@testScale.R#10)  ----------
  'getDefaultCluster' is not an exported object from 'namespace:parallel'
  1: designTreatmentsC(dTrainC, colnames(dTrainC), "y", TRUE, catScaling = TRUE, verbose = FALSE) at testthat/testScale.R:10
  2: .designTreatmentsX(dframe, varlist, outcomename, zoY, dframe[[outcomename]], outcometarget, 
         weights, minFraction, smFactor, rareCount, rareSig, collarProb, codeRestriction, 
         customCoders, splitFunction, ncross, forceSplit, catScaling, verbose = verbose, 
         parallelCluster = parallelCluster, use_parallel = use_parallel)
  3: .designTreatmentsXS(dframe, varlist, outcomename, zoY, zC, zTarget, weights, minFraction, 
         smFactor, rareCount, rareSig, collarProb, codeRestriction, customCoders, FALSE, 
         catScaling, verbose = verbose, parallelCluster = parallelCluster, use_parallel = use_parallel)
  4: plapply(workList, worker, parallelCluster = parallelCluster, use_parallel = use_parallel)
  5: parallel::getDefaultCluster
  6: getExportedValue(pkg, name)
  7: stop(gettextf("'%s' is not an exported object from 'namespace:%s'", name, getNamespaceName(ns)), 
         call. = FALSE, domain = NA)
  
  -- 6. Error: testStability: Stability of estimates (@testStability.R#46)  ------
  'getDefaultCluster' is not an exported object from 'namespace:parallel'
  1: vtreat::designTreatmentsC(d, "x", "y", TRUE, rareSig = 1, verbose = FALSE) at testthat/testStability.R:46
  2: .designTreatmentsX(dframe, varlist, outcomename, zoY, dframe[[outcomename]], outcometarget, 
         weights, minFraction, smFactor, rareCount, rareSig, collarProb, codeRestriction, 
         customCoders, splitFunction, ncross, forceSplit, catScaling, verbose = verbose, 
         parallelCluster = parallelCluster, use_parallel = use_parallel)
  3: .designTreatmentsXS(dframe, varlist, outcomename, zoY, zC, zTarget, weights, minFraction, 
         smFactor, rareCount, rareSig, collarProb, codeRestriction, customCoders, FALSE, 
         catScaling, verbose = verbose, parallelCluster = parallelCluster, use_parallel = use_parallel)
  4: .mkCatInd_scales(ti, zoY, zC, zTarget, weights, catScaling, parallelCluster = parallelCluster, 
         use_parallel = use_parallel)
  5: plapply(seq_len(length(newVarNames)), worker, parallelCluster = parallelCluster, 
         use_parallel = use_parallel)
  6: parallel::getDefaultCluster
  7: getExportedValue(pkg, name)
  8: stop(gettextf("'%s' is not an exported object from 'namespace:%s'", name, getNamespaceName(ns)), 
         call. = FALSE, domain = NA)
  
  -- 7. Error: testUniqValue: Can work with unique values (@testUniqValue.R#11)  -
  'getDefaultCluster' is not an exported object from 'namespace:parallel'
  1: designTreatmentsN(dTrainN, colnames(dTrainN), "y", rareCount = 0, rareSig = 1, verbose = FALSE) at testthat/testUniqValue.R:11
  2: .designTreatmentsX(dframe, varlist, outcomename, ycol, c(), c(), weights, minFraction, 
         smFactor, rareCount, rareSig, collarProb, codeRestriction, customCoders, splitFunction, 
         ncross, forceSplit, catScaling, verbose = verbose, parallelCluster = parallelCluster, 
         use_parallel = use_parallel)
  3: .designTreatmentsXS(dframe, varlist, outcomename, zoY, zC, zTarget, weights, minFraction, 
         smFactor, rareCount, rareSig, collarProb, codeRestriction, customCoders, FALSE, 
         catScaling, verbose = verbose, parallelCluster = parallelCluster, use_parallel = use_parallel)
  4: plapply(workList, worker, parallelCluster = parallelCluster, use_parallel = use_parallel)
  5: parallel::getDefaultCluster
  6: getExportedValue(pkg, name)
  7: stop(gettextf("'%s' is not an exported object from 'namespace:%s'", name, getNamespaceName(ns)), 
         call. = FALSE, domain = NA)
  
  -- 8. Error: testW1: test out of sample scoring defeats nested model bias (@test
  'getDefaultCluster' is not an exported object from 'namespace:parallel'
  1: designTreatmentsN(d, c("zip", "zip2"), "y", verbose = FALSE, rareCount = 2, rareSig = 0.5) at testthat/testW1.R:20
  2: .designTreatmentsX(dframe, varlist, outcomename, ycol, c(), c(), weights, minFraction, 
         smFactor, rareCount, rareSig, collarProb, codeRestriction, customCoders, splitFunction, 
         ncross, forceSplit, catScaling, verbose = verbose, parallelCluster = parallelCluster, 
         use_parallel = use_parallel)
  3: .designTreatmentsXS(dframe, varlist, outcomename, zoY, zC, zTarget, weights, minFraction, 
         smFactor, rareCount, rareSig, collarProb, codeRestriction, customCoders, FALSE, 
         catScaling, verbose = verbose, parallelCluster = parallelCluster, use_parallel = use_parallel)
  4: lapply(varlist, function(v) {
         levRestriction <- c()
         hasRange <- FALSE
         vcolOrig <- dframe[[v]]
         vcol <- NULL
         if (length(vcolOrig) != nRows) {
             warning(paste("wrong column length", v))
         }
         else {
             vcol <- .cleanColumn(vcolOrig, nRows)
             if (.has.range(vcol)) {
                 hasRange <- TRUE
                 colclass <- class(vcol)
                 if ((colclass == "character") || (colclass == "factor")) {
                     if (!is.null(zC)) {
                       levRestriction <- .safeLevelsC(vcol, zC, zTarget, weights, minFraction, 
                         rareCount, rareSig, parallelCluster = parallelCluster, use_parallel = use_parallel)
                     }
                     else {
                       levRestriction <- .safeLevelsR(vcol, zoY, weights, minFraction, 
                         rareCount, rareSig, parallelCluster = parallelCluster, use_parallel = use_parallel)
                     }
                 }
             }
         }
         list(v = v, vcolOrig = vcolOrig, vcol = vcol, hasRange = hasRange, levRestriction = levRestriction)
     })
  5: FUN(X[[i]], ...)
  6: .safeLevelsR(vcol, zoY, weights, minFraction, rareCount, rareSig, parallelCluster = parallelCluster, 
         use_parallel = use_parallel)
  7: plapply(safeLevs, aovCalc, parallelCluster = parallelCluster, use_parallel = use_parallel)
  8: parallel::getDefaultCluster
  9: getExportedValue(pkg, name)
  10: stop(gettextf("'%s' is not an exported object from 'namespace:%s'", name, getNamespaceName(ns)), 
         call. = FALSE, domain = NA)
  
  -- 9. Error: testWeirdTypes: Protect from odd columns types (and warn) (@testWei
  'getDefaultCluster' is not an exported object from 'namespace:parallel'
  1: suppressWarnings({
         d <- data.frame(xInteger = 1:4, xNumeric = 0, xCharacter = "a", xFactor = as.factor("b"), 
             xPOSIXct = Sys.time(), xRaw = raw(4), xLogical = TRUE, xArrayNull = as.array(list(NULL, 
                 NULL, NULL, NULL)), stringsAsFactors = FALSE)
         d$xPOSIXlt <- as.POSIXlt(c(Sys.time(), Sys.time() + 100, Sys.time() + 200, Sys.time() + 
             300))
         d$xArray <- as.array(c(17, 18, 19, 20))
         d$xMatrix1 <- matrix(data = c(88, 89, 90, 91), nrow = 4, ncol = 1)
         d$xMatrix2 <- matrix(data = c(1, 2, 3, 4, 5, 6, 7, 8), nrow = 4, ncol = 2)
         d$xMatrixC1 <- matrix(data = c("88", "89", "90", "91"), nrow = 4, ncol = 1)
         d$xMatrixC2 <- matrix(data = c("1", "2", "3", "4", "5", "6", "7", "8"), nrow = 4, 
             ncol = 2)
         d$xListH <- list(10, 20, "thirty", "forty")
         d$xListR <- list(list(), list("a"), list("a", "b"), list("a", "b", "c"))
         d$xData.Frame <- data.frame(xData.FrameA = 6:9, xData.FrameB = 11:14)
         d$xListR2 <- I(list(NULL, "a", c("a", "b"), 1))
         d$xFunctions = I(c(function() {
         }, function() {
         }, function() {
         }, function() {
         }))
         d$y <- c(1, 1, 0, 0)
         yVar <- "y"
         yTarget <- 1
         xVars <- setdiff(colnames(d), yVar)
         treatmentsC <- designTreatmentsC(d, xVars, yVar, yTarget, verbose = FALSE)
         treatmentsN <- designTreatmentsN(d, xVars, yVar, verbose = FALSE)
         expect_true(!is.null(treatmentsC))
         expect_true(!is.null(treatmentsN))
     }) at testthat/testWeirdTypes.R:6
  2: withCallingHandlers(expr, warning = function(w) invokeRestart("muffleWarning"))
  3: designTreatmentsC(d, xVars, yVar, yTarget, verbose = FALSE) at testthat/testWeirdTypes.R:31
  4: .designTreatmentsX(dframe, varlist, outcomename, zoY, dframe[[outcomename]], outcometarget, 
         weights, minFraction, smFactor, rareCount, rareSig, collarProb, codeRestriction, 
         customCoders, splitFunction, ncross, forceSplit, catScaling, verbose = verbose, 
         parallelCluster = parallelCluster, use_parallel = use_parallel)
  5: .designTreatmentsXS(dframe, varlist, outcomename, zoY, zC, zTarget, weights, minFraction, 
         smFactor, rareCount, rareSig, collarProb, codeRestriction, customCoders, FALSE, 
         catScaling, verbose = verbose, parallelCluster = parallelCluster, use_parallel = use_parallel)
  6: plapply(workList, worker, parallelCluster = parallelCluster, use_parallel = use_parallel)
  7: parallel::getDefaultCluster
  8: getExportedValue(pkg, name)
  9: stop(gettextf("'%s' is not an exported object from 'namespace:%s'", name, getNamespaceName(ns)), 
         call. = FALSE, domain = NA)
  
  -- 10. Error: testZW: Test Zero Weights Don't Crash (@testZW.R#14)  ------------
  'getDefaultCluster' is not an exported object from 'namespace:parallel'
  1: designTreatmentsC(dTrainC, colnames(dTrainC), "y", TRUE, catScaling = TRUE, weights = trainCWeights, 
         verbose = FALSE) at testthat/testZW.R:14
  2: .designTreatmentsX(dframe, varlist, outcomename, zoY, dframe[[outcomename]], outcometarget, 
         weights, minFraction, smFactor, rareCount, rareSig, collarProb, codeRestriction, 
         customCoders, splitFunction, ncross, forceSplit, catScaling, verbose = verbose, 
         parallelCluster = parallelCluster, use_parallel = use_parallel)
  3: .designTreatmentsXS(dframe, varlist, outcomename, zoY, zC, zTarget, weights, minFraction, 
         smFactor, rareCount, rareSig, collarProb, codeRestriction, customCoders, FALSE, 
         catScaling, verbose = verbose, parallelCluster = parallelCluster, use_parallel = use_parallel)
  4: plapply(workList, worker, parallelCluster = parallelCluster, use_parallel = use_parallel)
  5: parallel::getDefaultCluster
  6: getExportedValue(pkg, name)
  7: stop(gettextf("'%s' is not an exported object from 'namespace:%s'", name, getNamespaceName(ns)), 
         call. = FALSE, domain = NA)
  
  == testthat results  ===========================================================
  OK: 149 SKIPPED: 0 FAILED: 10
  1.  Error: testBO: Works As Expected (@testBO.R#6) 
  2.  Error: testCar: Numeric Var Scores as expected car (@testCar.R#14) 
  3.  Error: testNoY: Can transform without Y (@testNoY.R#10) 
  4.  Error: testParallel: Parallel works (@testParallel.R#30) 
  5.  Error: testScale: check corner case on scale (@testScale.R#10) 
  6.  Error: testStability: Stability of estimates (@testStability.R#46) 
  7.  Error: testUniqValue: Can work with unique values (@testUniqValue.R#11) 
  8.  Error: testW1: test out of sample scoring defeats nested model bias (@testW1.R#20) 
  9.  Error: testWeirdTypes: Protect from odd columns types (and warn) (@testWeirdTypes.R#6) 
  10. Error: testZW: Test Zero Weights Don't Crash (@testZW.R#14) 
  
  Error: testthat unit tests failed
  Execution halted