CRAN Package Check Results for Package portvine

Last updated on 2025-12-25 18:48:52 CET.

Flavor	Version	T_install	T_check	T_total	Status
r-devel-linux-x86_64-debian-clang	1.0.3	100.80	206.18	306.98	ERROR
r-devel-linux-x86_64-debian-gcc	1.0.3	84.70	151.51	236.21	ERROR
r-devel-linux-x86_64-fedora-clang	1.0.3	172.00	697.03	869.03	OK
r-devel-linux-x86_64-fedora-gcc	1.0.3	240.00	729.19	969.19	OK
r-devel-windows-x86_64	1.0.3	114.00	377.00	491.00	OK
r-patched-linux-x86_64	1.0.3	120.91	380.98	501.89	OK
r-release-linux-x86_64	1.0.3	127.56	380.41	507.97	OK
r-release-macos-arm64	1.0.3				OK
r-release-macos-x86_64	1.0.3	70.00	348.00	418.00	OK
r-release-windows-x86_64	1.0.3	117.00	380.00	497.00	OK
r-oldrel-macos-arm64	1.0.3				NOTE
r-oldrel-macos-x86_64	1.0.3	66.00	224.00	290.00	NOTE
r-oldrel-windows-x86_64	1.0.3	150.00	529.00	679.00	NOTE

Check Details

Version: 1.0.3
Check: tests
Result: ERROR Running ‘testthat.R’ [86s/101s] Running the tests in ‘tests/testthat.R’ failed. Complete output: > library(testthat) > library(portvine) > data("sample_returns_small") > > test_check("portvine", reporter = "summary") S4accessors: WW1WW2 default_garch_spec: ........ dvine_ordering: .......... estimate_dependence_and_risk: SS estimate_marginal_models: ........................ estimate_risk_roll: .......................WW3. Fit marginal models: AAPL GOOG AMZN Fit vine copula models and estimate risk. Vine windows: (1/4) WW4S marginal_settings: ................ rcondvinecop: ............................. risk_measures: .................... utils: ..... vine_settings: ............. ══ Skipped ═════════════════════════════════════════════════════════════════════ 1. unconditional case ('test-estimate_dependence_and_risk.R:30:3') - Reason: On CRAN 2. conditional case ('test-estimate_dependence_and_risk.R:148:3') - Reason: On CRAN 3. parallel functionality ('test-estimate_risk_roll.R:558:3') - Reason: On CRAN ══ Warnings ════════════════════════════════════════════════════════════════════ 1. risk_estimates() basic functionality & input checks ('test-S4accessors.R:14:3') - Caught simpleError. Canceling all iterations ... 2. risk_estimates() basic functionality & input checks ('test-S4accessors.R:14:3') - Caught simpleError. Canceling all iterations ... 3. fitted_vines() & fitted_marginals() basic functionality ('test-S4accessors.R:312:3') - Caught simpleError. Canceling all iterations ... 4. fitted_vines() & fitted_marginals() basic functionality ('test-S4accessors.R:312:3') - Caught simpleError. Canceling all iterations ... 5. basic functionality (unconditionally) ('test-estimate_risk_roll.R:331:3') - Caught simpleError. Canceling all iterations ... 6. basic functionality (unconditionally) ('test-estimate_risk_roll.R:331:3') - Caught simpleError. Canceling all iterations ... 7. basic functionality (conditionally) ('test-estimate_risk_roll.R:449:3') - Caught simpleError. Canceling all iterations ... 8. basic functionality (conditionally) ('test-estimate_risk_roll.R:449:3') - Caught simpleError. Canceling all iterations ... ══ Failed ══════════════════════════════════════════════════════════════════════ ── 1. Error ('test-S4accessors.R:14:3'): risk_estimates() basic functionality & Error in ``[.data.table`(melt(copy(`_DT20`)[, `:=`(sample_id = seq(nrow(structure(list(AAPL = c(0.810607714710254, 0.407077608421067, 0.400047707410517, 0.426624000909891, 0.886629638230492, 0.390319945459875, 0.841073264677281, 0.753297374243674, 0.317374249679273, 0.311444209346356), AMZN = c(0.615317667620359, 0.895450788917563, 0.77583320226003, 0.351589313696444, 0.940400517277914, 0.233763942389714, 0.734883742854768, 0.378795761702627, 0.425276253821377, 0.0995922458253906), GOOG = c(0.766488737892359, 0.830865777097642, 0.571533417562023, 0.134496232029051, 0.957499846350402, 0.446940286783502, 0.526918233605102, 0.392552558798343, 0.278815471101552, 0.664138429332525)), row.names = c(NA, -10L), class = c("data.table", "data.frame"), .internal.selfref = <pointer: 0x55ef83022fe0>))))], measure.vars = c("AAPL", "AMZN", "GOOG"), variable.name = "asset", value.name = "sample", variable.factor = FALSE)[, `:=`(sample = trans_vals[["mu"]][trans_vals[["asset"]] == asset] + trans_vals[["sigma"]][trans_vals[["asset"]] == asset] * rugarch::qdist(distribution = trans_vals[["marg_dist"]][trans_vals[["asset"]] == asset], p = sample, skew = trans_vals[["skew"]][trans_vals[["asset"]] == asset], shape = trans_vals[["shape"]][trans_vals[["asset"]] == asset])), by = .(asset)], , `:=`(weight = structure(c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1), dim = 4:3, dimnames = list(NULL, c("AAPL", "GOOG", "AMZN")))[1L, asset]), by = .(asset))`: attempt access index 3/3 in VECTOR_ELT Backtrace: ▆ 1. └─portvine::estimate_risk_roll(...) at test-S4accessors.R:14:3 2. └─portvine:::estimate_dependence_and_risk(...) 3. └─future.apply::future_lapply(...) 4. └─future.apply:::future_xapply(...) 5. └─base::tryCatch(...) 6. └─base (local) tryCatchList(expr, classes, parentenv, handlers) 7. └─base (local) tryCatchOne(...) 8. └─value[[3L]](cond) 9. └─future.apply:::onError(e, futures = fs, debug = debug) ── 2. Error ('test-S4accessors.R:312:3'): fitted_vines() & fitted_marginals() ba Error in ``[.data.table`(melt(copy(`_DT40`)[, `:=`(sample_id = seq(nrow(structure(list(AAPL = c(0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.727447098696852, 0.727447098696852, 0.727447098696852, 0.727447098696852, 0.727447098696852, 0.727447098696852, 0.727447098696852, 0.727447098696852, 0.727447098696852, 0.727447098696852), AMZN = c(0.293175863458917, 0.785903747855321, 0.289446799936519, 0.64723343435812, 0.596163062200383, 0.821407349133576, 0.126162857516692, 0.400302037306605, 0.50441212972194, 0.832445741506612, 0.748301180662518, 0.633281621594218, 0.689173293212055, 0.939940790395202, 0.964876306775914, 0.414580952888676, 0.411500723079085, 0.544799442714959, 0.162505812627388, 0.618808746030832), GOOG = c(0.418921741469634, 0.702994264376683, 0.395311279669759, 0.45137875009, 0.874150174858789, 0.483352751528072, 0.19257159092659, 0.492557711759154, 0.707740168152825, 0.720813165695748, 0.869857480932488, 0.607216735378671, 0.981995118328851, 0.913945282028791, 0.940490234188998, 0.456726822076143, 0.47678908698532, 0.863958460379851, 0.22467168641327, 0.810912321910637)), row.names = c(NA, -20L), class = c("data.table", "data.frame"), .internal.selfref = <pointer: 0x55ef83022fe0>))))], measure.vars = c("AAPL", "AMZN", "GOOG"), variable.name = "asset", value.name = "sample", variable.factor = FALSE)[, `:=`(sample = trans_vals[["mu"]][trans_vals[["asset"]] == asset] + trans_vals[["sigma"]][trans_vals[["asset"]] == asset] * rugarch::qdist(distribution = trans_vals[["marg_dist"]][trans_vals[["asset"]] == asset], p = sample, skew = trans_vals[["skew"]][trans_vals[["asset"]] == asset], shape = trans_vals[["shape"]][trans_vals[["asset"]] == asset])), by = .(asset)], , `:=`(weight = structure(c(0, 0, 1, 1, 1, 1), dim = 2:3, dimnames = list(NULL, c("AAPL", "GOOG", "AMZN")))[1L, asset]), by = .(asset))`: attempt access index 3/3 in VECTOR_ELT Backtrace: ▆ 1. └─portvine::estimate_risk_roll(...) at test-S4accessors.R:312:3 2. └─portvine:::estimate_dependence_and_risk(...) 3. └─future.apply::future_lapply(...) 4. └─future.apply:::future_xapply(...) 5. └─base::tryCatch(...) 6. └─base (local) tryCatchList(expr, classes, parentenv, handlers) 7. └─base (local) tryCatchOne(...) 8. └─value[[3L]](cond) 9. └─future.apply:::onError(e, futures = fs, debug = debug) ── 3. Error ('test-estimate_risk_roll.R:331:3'): basic functionality (unconditio Error in ``[.data.table`(melt(copy(`_DT128`)[, `:=`(sample_id = seq(nrow(structure(list(AAPL = c(0.118788820510204, 0.00658700392869827, 0.927702131770059, 0.662504547628689, 0.537346369381258, 0.900367098718323, 0.439307413678304, 0.377420387177186, 0.324440813118715, 0.014739214899268), AMZN = c(0.452218840827196, 0.162461424245455, 0.866664614419352, 0.756069068064378, 0.902044114242331, 0.732302184444001, 0.784377794526194, 0.0145975125143389, 0.675177695178233, 0.651995968753478), GOOG = c(0.0506899717729539, 0.392755394103006, 0.690200256416574, 0.837051088223234, 0.492706334684044, 0.312967439647764, 0.826566410483792, 0.492748861201108, 0.686855713836849, 0.0212156251072884)), row.names = c(NA, -10L), class = c("data.table", "data.frame"), .internal.selfref = <pointer: 0x55ef83022fe0>))))], measure.vars = c("AAPL", "AMZN", "GOOG"), variable.name = "asset", value.name = "sample", variable.factor = FALSE)[, `:=`(sample = trans_vals[["mu"]][trans_vals[["asset"]] == asset] + trans_vals[["sigma"]][trans_vals[["asset"]] == asset] * rugarch::qdist(distribution = trans_vals[["marg_dist"]][trans_vals[["asset"]] == asset], p = sample, skew = trans_vals[["skew"]][trans_vals[["asset"]] == asset], shape = trans_vals[["shape"]][trans_vals[["asset"]] == asset])), by = .(asset)], , `:=`(weight = structure(c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1), dim = 4:3, dimnames = list(NULL, c("AAPL", "GOOG", "AMZN")))[1L, asset]), by = .(asset))`: attempt access index 3/3 in VECTOR_ELT Backtrace: ▆ 1. └─portvine::estimate_risk_roll(...) at test-estimate_risk_roll.R:331:3 2. └─portvine:::estimate_dependence_and_risk(...) 3. └─future.apply::future_lapply(...) 4. └─future.apply:::future_xapply(...) 5. └─base::tryCatch(...) 6. └─base (local) tryCatchList(expr, classes, parentenv, handlers) 7. └─base (local) tryCatchOne(...) 8. └─value[[3L]](cond) 9. └─future.apply:::onError(e, futures = fs, debug = debug) ── 4. Error ('test-estimate_risk_roll.R:449:3'): basic functionality (conditiona Error in ``[.data.table`(melt(copy(`_DT148`)[, `:=`(sample_id = seq(nrow(structure(list(AAPL = c(0.063756994616236, 0.181499921319531, 0.601235124658506, 0.0178294667867004, 0.162638454819774, 0.095196289503367, 0.160854672913014, 0.0195309153647054, 0.0210493118253517, 0.0334783701531182, 0.0126547364976479, 0.707285165208548, 0.0899559218768428, 0.612239188913503, 0.426502746239003, 0.162928073304437, 0.846215447840346, 0.692916099286004, 0.327358000657169, 0.78641262243067, 0.390853883112392, 0.572988137345556, 0.740424983995262, 0.408021933123939, 0.688535327720902, 0.578161744976596, 0.520510457178766, 0.20029118565266, 0.354231752018171, 0.509641943106971, 0.802720277957555, 0.668121676428417, 0.472069596707188), AMZN = c(0.00507520351278257, 0.0431056571941877, 0.0278531716177613, 0.0912665402725797, 0.0741895720251953, 0.0667046069779821, 0.20351749279155, 0.0209079133817135, 0.0371142917834336, 0.0283466831909877, 0.0215204118015282, 0.375528324386546, 0.0409812387240993, 0.247473507155635, 0.326428599893086, 0.694059418252486, 0.44033419171185, 0.226804921721476, 0.336619022141231, 0.3615833255363, 0.732801527383828, 0.265145143581959, 0.319521569080767, 0.699641570158421, 0.529539770902631, 0.548317384931096, 0.623796642807858, 0.1041002129314, 0.224848573776543, 0.600025893258099, 0.42086507303692, 0.531860601751563, 0.79592528312703), GOOG = c(0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.60416480751054, 0.60416480751054, 0.60416480751054, 0.60416480751054, 0.60416480751054, 0.60416480751054, 0.60416480751054, 0.60416480751054, 0.60416480751054, 0.60416480751054, 0.60416480751054)), row.names = c(NA, -33L), class = c("data.table", "data.frame"), .internal.selfref = <pointer: 0x55ef83022fe0>))))], measure.vars = c("AAPL", "AMZN", "GOOG"), variable.name = "asset", value.name = "sample", variable.factor = FALSE)[, `:=`(sample = trans_vals[["mu"]][trans_vals[["asset"]] == asset] + trans_vals[["sigma"]][trans_vals[["asset"]] == asset] * rugarch::qdist(distribution = trans_vals[["marg_dist"]][trans_vals[["asset"]] == asset], p = sample, skew = trans_vals[["skew"]][trans_vals[["asset"]] == asset], shape = trans_vals[["shape"]][trans_vals[["asset"]] == asset])), by = .(asset)], , `:=`(weight = structure(c(1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1), dim = 4:3, dimnames = list(NULL, c("AAPL", "GOOG", "AMZN")))[1L, asset]), by = .(asset))`: attempt access index 3/3 in VECTOR_ELT Backtrace: ▆ 1. └─portvine::estimate_risk_roll(...) at test-estimate_risk_roll.R:449:3 2. └─portvine:::estimate_dependence_and_risk(...) 3. └─future.apply::future_lapply(...) 4. └─future.apply:::future_xapply(...) 5. └─base::tryCatch(...) 6. └─base (local) tryCatchList(expr, classes, parentenv, handlers) 7. └─base (local) tryCatchOne(...) 8. └─value[[3L]](cond) 9. └─future.apply:::onError(e, futures = fs, debug = debug) ══ DONE ════════════════════════════════════════════════════════════════════════ Error: ! Test failures. Execution halted Flavor: r-devel-linux-x86_64-debian-clang

Version: 1.0.3
Check: re-building of vignette outputs
Result: ERROR Error(s) in re-building vignettes: ... --- re-building ‘get_started.Rmd’ using rmarkdown Quitting from get_started.Rmd:142-157 [unnamed-chunk-9] ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ <error/rlang_error> Error in `[.data.table`: ! attempt access index 3/3 in VECTOR_ELT --- Backtrace: ▆ 1. └─portvine::estimate_risk_roll(...) 2. └─portvine:::estimate_dependence_and_risk(...) 3. └─future.apply::future_lapply(...) 4. └─future.apply:::future_xapply(...) 5. └─base::tryCatch(...) 6. └─base (local) tryCatchList(expr, classes, parentenv, handlers) 7. └─base (local) tryCatchOne(...) 8. └─value[[3L]](cond) 9. └─future.apply:::onError(e, futures = fs, debug = debug) ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Error: processing vignette 'get_started.Rmd' failed with diagnostics: attempt access index 3/3 in VECTOR_ELT --- failed re-building ‘get_started.Rmd’ SUMMARY: processing the following file failed: ‘get_started.Rmd’ Error: Vignette re-building failed. Execution halted Flavors: r-devel-linux-x86_64-debian-clang, r-devel-linux-x86_64-debian-gcc

Version: 1.0.3
Check: tests
Result: ERROR Running ‘testthat.R’ [58s/79s] Running the tests in ‘tests/testthat.R’ failed. Complete output: > library(testthat) > library(portvine) > data("sample_returns_small") > > test_check("portvine", reporter = "summary") S4accessors: WW1WW2 default_garch_spec: ........ dvine_ordering: .......... estimate_dependence_and_risk: SS estimate_marginal_models: ........................ estimate_risk_roll: .......................WW3. Fit marginal models: AAPL GOOG AMZN Fit vine copula models and estimate risk. Vine windows: (1/4) WW4S marginal_settings: ................ rcondvinecop: ............................. risk_measures: .................... utils: ..... vine_settings: ............. ══ Skipped ═════════════════════════════════════════════════════════════════════ 1. unconditional case ('test-estimate_dependence_and_risk.R:30:3') - Reason: On CRAN 2. conditional case ('test-estimate_dependence_and_risk.R:148:3') - Reason: On CRAN 3. parallel functionality ('test-estimate_risk_roll.R:558:3') - Reason: On CRAN ══ Warnings ════════════════════════════════════════════════════════════════════ 1. risk_estimates() basic functionality & input checks ('test-S4accessors.R:14:3') - Caught simpleError. Canceling all iterations ... 2. risk_estimates() basic functionality & input checks ('test-S4accessors.R:14:3') - Caught simpleError. Canceling all iterations ... 3. fitted_vines() & fitted_marginals() basic functionality ('test-S4accessors.R:312:3') - Caught simpleError. Canceling all iterations ... 4. fitted_vines() & fitted_marginals() basic functionality ('test-S4accessors.R:312:3') - Caught simpleError. Canceling all iterations ... 5. basic functionality (unconditionally) ('test-estimate_risk_roll.R:331:3') - Caught simpleError. Canceling all iterations ... 6. basic functionality (unconditionally) ('test-estimate_risk_roll.R:331:3') - Caught simpleError. Canceling all iterations ... 7. basic functionality (conditionally) ('test-estimate_risk_roll.R:449:3') - Caught simpleError. Canceling all iterations ... 8. basic functionality (conditionally) ('test-estimate_risk_roll.R:449:3') - Caught simpleError. Canceling all iterations ... ══ Failed ══════════════════════════════════════════════════════════════════════ ── 1. Error ('test-S4accessors.R:14:3'): risk_estimates() basic functionality & Error in ``[.data.table`(melt(copy(`_DT20`)[, `:=`(sample_id = seq(nrow(structure(list(AAPL = c(0.282518812339592, 0.587483129889476, 0.433595233237209, 0.429692240900023, 0.639252548572771, 0.594144981180756, 0.810478761540737, 0.23463646642182, 0.561532178219991, 0.762592107840013), AMZN = c(0.296503029153914, 0.934220414716254, 0.203435819632282, 0.138423641315969, 0.889062618473071, 0.649730877969201, 0.513010033010351, 0.367841900643841, 0.414536684740482, 0.396665104859958), GOOG = c(0.31741358153522, 0.987532870378345, 0.0326276556588709, 0.512191934511065, 0.773881126893684, 0.411681323079392, 0.605103926267475, 0.0826111440546811, 0.253834546776488, 0.852539789397269)), row.names = c(NA, -10L), class = c("data.table", "data.frame"), .internal.selfref = <pointer: 0x56027d04b070>))))], measure.vars = c("AAPL", "AMZN", "GOOG"), variable.name = "asset", value.name = "sample", variable.factor = FALSE)[, `:=`(sample = trans_vals[["mu"]][trans_vals[["asset"]] == asset] + trans_vals[["sigma"]][trans_vals[["asset"]] == asset] * rugarch::qdist(distribution = trans_vals[["marg_dist"]][trans_vals[["asset"]] == asset], p = sample, skew = trans_vals[["skew"]][trans_vals[["asset"]] == asset], shape = trans_vals[["shape"]][trans_vals[["asset"]] == asset])), by = .(asset)], , `:=`(weight = structure(c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1), dim = 4:3, dimnames = list(NULL, c("AAPL", "GOOG", "AMZN")))[1L, asset]), by = .(asset))`: attempt access index 3/3 in VECTOR_ELT Backtrace: ▆ 1. └─portvine::estimate_risk_roll(...) at test-S4accessors.R:14:3 2. └─portvine:::estimate_dependence_and_risk(...) 3. └─future.apply::future_lapply(...) 4. └─future.apply:::future_xapply(...) 5. └─base::tryCatch(...) 6. └─base (local) tryCatchList(expr, classes, parentenv, handlers) 7. └─base (local) tryCatchOne(...) 8. └─value[[3L]](cond) 9. └─future.apply:::onError(e, futures = fs, debug = debug) ── 2. Error ('test-S4accessors.R:312:3'): fitted_vines() & fitted_marginals() ba Error in ``[.data.table`(melt(copy(`_DT40`)[, `:=`(sample_id = seq(nrow(structure(list(AAPL = c(0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.727447098696852, 0.727447098696852, 0.727447098696852, 0.727447098696852, 0.727447098696852, 0.727447098696852, 0.727447098696852, 0.727447098696852, 0.727447098696852, 0.727447098696852), AMZN = c(0.969075018916905, 0.757124353455085, 0.120927351832202, 0.306024206801311, 0.577430848788784, 0.224498310395557, 0.51368624979703, 0.585964387383714, 0.578104158779533, 0.15757375964427, 0.515599899689993, 0.991616162759347, 0.291377774586145, 0.749302561361692, 0.184698438332065, 0.842154182473103, 0.546337099837596, 0.345506057106863, 0.898852506845356, 0.689242448167068), GOOG = c(0.560213819776012, 0.799358721197903, 0.187365955246645, 0.657831370927835, 0.464341709733349, 0.400263061788626, 0.0828831365467844, 0.692805209745208, 0.513883877056128, 0.443968765340844, 0.713277915530453, 0.849279253100613, 0.736551514012559, 0.989258534565638, 0.198098940618499, 0.774859070784683, 0.699159810057358, 0.273361014521616, 0.893290195613287, 0.699618258997458)), row.names = c(NA, -20L), class = c("data.table", "data.frame"), .internal.selfref = <pointer: 0x56027d04b070>))))], measure.vars = c("AAPL", "AMZN", "GOOG"), variable.name = "asset", value.name = "sample", variable.factor = FALSE)[, `:=`(sample = trans_vals[["mu"]][trans_vals[["asset"]] == asset] + trans_vals[["sigma"]][trans_vals[["asset"]] == asset] * rugarch::qdist(distribution = trans_vals[["marg_dist"]][trans_vals[["asset"]] == asset], p = sample, skew = trans_vals[["skew"]][trans_vals[["asset"]] == asset], shape = trans_vals[["shape"]][trans_vals[["asset"]] == asset])), by = .(asset)], , `:=`(weight = structure(c(0, 0, 1, 1, 1, 1), dim = 2:3, dimnames = list(NULL, c("AAPL", "GOOG", "AMZN")))[1L, asset]), by = .(asset))`: attempt access index 3/3 in VECTOR_ELT Backtrace: ▆ 1. └─portvine::estimate_risk_roll(...) at test-S4accessors.R:312:3 2. └─portvine:::estimate_dependence_and_risk(...) 3. └─future.apply::future_lapply(...) 4. └─future.apply:::future_xapply(...) 5. └─base::tryCatch(...) 6. └─base (local) tryCatchList(expr, classes, parentenv, handlers) 7. └─base (local) tryCatchOne(...) 8. └─value[[3L]](cond) 9. └─future.apply:::onError(e, futures = fs, debug = debug) ── 3. Error ('test-estimate_risk_roll.R:331:3'): basic functionality (unconditio Error in ``[.data.table`(melt(copy(`_DT128`)[, `:=`(sample_id = seq(nrow(structure(list(AAPL = c(0.608778650788359, 0.728397510067545, 0.494965168990907, 0.572337951268481, 0.341903836013129, 0.343323532291012, 0.526905578949989, 0.809270641287296, 0.649196852579373, 0.359027624150798), AMZN = c(0.215658562661231, 0.39790979364024, 0.888044264828239, 0.892644906705346, 0.913423316329817, 0.402023056351919, 0.366099013796964, 0.590947699281299, 0.483174474350532, 0.50035268191187), GOOG = c(0.575369447469711, 0.775972301373258, 0.867885766085237, 0.812464581802487, 0.812591799534857, 0.166031770175323, 0.462097370764241, 0.621798584936187, 0.292949931463227, 0.163355872035027)), row.names = c(NA, -10L), class = c("data.table", "data.frame"), .internal.selfref = <pointer: 0x56027d04b070>))))], measure.vars = c("AAPL", "AMZN", "GOOG"), variable.name = "asset", value.name = "sample", variable.factor = FALSE)[, `:=`(sample = trans_vals[["mu"]][trans_vals[["asset"]] == asset] + trans_vals[["sigma"]][trans_vals[["asset"]] == asset] * rugarch::qdist(distribution = trans_vals[["marg_dist"]][trans_vals[["asset"]] == asset], p = sample, skew = trans_vals[["skew"]][trans_vals[["asset"]] == asset], shape = trans_vals[["shape"]][trans_vals[["asset"]] == asset])), by = .(asset)], , `:=`(weight = structure(c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1), dim = 4:3, dimnames = list(NULL, c("AAPL", "GOOG", "AMZN")))[1L, asset]), by = .(asset))`: attempt access index 3/3 in VECTOR_ELT Backtrace: ▆ 1. └─portvine::estimate_risk_roll(...) at test-estimate_risk_roll.R:331:3 2. └─portvine:::estimate_dependence_and_risk(...) 3. └─future.apply::future_lapply(...) 4. └─future.apply:::future_xapply(...) 5. └─base::tryCatch(...) 6. └─base (local) tryCatchList(expr, classes, parentenv, handlers) 7. └─base (local) tryCatchOne(...) 8. └─value[[3L]](cond) 9. └─future.apply:::onError(e, futures = fs, debug = debug) ── 4. Error ('test-estimate_risk_roll.R:449:3'): basic functionality (conditiona Error in ``[.data.table`(melt(copy(`_DT148`)[, `:=`(sample_id = seq(nrow(structure(list(AAPL = c(0.0613485322009202, 0.168760096024497, 0.00689991431931636, 0.0127095030595025, 0.642999273151422, 0.297928170027925, 0.143504166990126, 0.0304779107384516, 0.915293573815578, 0.102040927990706, 0.800302337542099, 0.120144338651111, 0.656460247403975, 0.963183932505658, 0.715236166194567, 0.453656031503121, 0.681845680615316, 0.777101627697189, 0.658268929320314, 0.749055320068361, 0.0943238891730175, 0.604111778192723, 0.678736110068086, 0.47032369650503, 0.361275400320829, 0.458263171557098, 0.751763550536301, 0.158572221033382, 0.460004364147454, 0.3666157427502, 0.751932246340719, 0.595281766098122, 0.713520801386788), AMZN = c(0.011672748648583, 0.25897156411148, 0.0416021545831238, 0.0583480814099919, 0.345186176237811, 0.155275137964098, 0.118471086837298, 0.044987185470943, 0.0371193147612771, 0.0285998844898167, 0.493580236378251, 0.347424397994771, 0.712637914546448, 0.46965466346258, 0.886793193648034, 0.487642501362469, 0.655606090945879, 0.888876194715753, 0.302025833725249, 0.45452528925771, 0.325606171179382, 0.692605158335958, 0.835148016930854, 0.994947522944307, 0.885457208608447, 0.800639479669557, 0.989922018538633, 0.324842483537793, 0.775384039369992, 0.175600950386628, 0.62638295435201, 0.800907051100306, 0.406746104490583), GOOG = c(0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.60416480751054, 0.60416480751054, 0.60416480751054, 0.60416480751054, 0.60416480751054, 0.60416480751054, 0.60416480751054, 0.60416480751054, 0.60416480751054, 0.60416480751054, 0.60416480751054)), row.names = c(NA, -33L), class = c("data.table", "data.frame"), .internal.selfref = <pointer: 0x56027d04b070>))))], measure.vars = c("AAPL", "AMZN", "GOOG"), variable.name = "asset", value.name = "sample", variable.factor = FALSE)[, `:=`(sample = trans_vals[["mu"]][trans_vals[["asset"]] == asset] + trans_vals[["sigma"]][trans_vals[["asset"]] == asset] * rugarch::qdist(distribution = trans_vals[["marg_dist"]][trans_vals[["asset"]] == asset], p = sample, skew = trans_vals[["skew"]][trans_vals[["asset"]] == asset], shape = trans_vals[["shape"]][trans_vals[["asset"]] == asset])), by = .(asset)], , `:=`(weight = structure(c(1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1), dim = 4:3, dimnames = list(NULL, c("AAPL", "GOOG", "AMZN")))[1L, asset]), by = .(asset))`: attempt access index 3/3 in VECTOR_ELT Backtrace: ▆ 1. └─portvine::estimate_risk_roll(...) at test-estimate_risk_roll.R:449:3 2. └─portvine:::estimate_dependence_and_risk(...) 3. └─future.apply::future_lapply(...) 4. └─future.apply:::future_xapply(...) 5. └─base::tryCatch(...) 6. └─base (local) tryCatchList(expr, classes, parentenv, handlers) 7. └─base (local) tryCatchOne(...) 8. └─value[[3L]](cond) 9. └─future.apply:::onError(e, futures = fs, debug = debug) ══ DONE ════════════════════════════════════════════════════════════════════════ Error: ! Test failures. Execution halted Flavor: r-devel-linux-x86_64-debian-gcc

Version: 1.0.3
Check: installed package size
Result: NOTE installed size is 39.8Mb sub-directories of 1Mb or more: libs 38.6Mb Flavors: r-oldrel-macos-arm64, r-oldrel-macos-x86_64, r-oldrel-windows-x86_64