CRAN Package Check Results for Package portvine

Last updated on 2025-12-26 07:48:50 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	86.84	291.79	378.63	OK
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	119.00	228.00	347.00	ERROR
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	126.00	395.00	521.00	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	153.00	518.00	671.00	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 Flavor: r-devel-linux-x86_64-debian-clang

Version: 1.0.3
Check: tests
Result: ERROR Running 'testthat.R' [84s] 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(0.851133729626506, 0.817469859115347, 0.34042323248208, 0.829174218071684, 0.22686556005964, 0.483288469139079, 0.285627467545038, 0.733278908764717, 0.473968298956092, 0.532111978525248), AMZN = c(0.579666443382277, 0.346415140784956, 0.377089811023051, 0.65345650127477, 0.00901509278503613, 0.896138125633004, 0.106173893632115, 0.627137469995011, 0.833611344191598, 0.948425463949372), GOOG = c(0.698342850198969, 0.305415803100914, 0.498109691776335, 0.700399791589007, 0.00173484021797776, 0.550074147991836, 0.193374787922949, 0.702251970302314, 0.781957285711542, 0.268312602769583)), row.names = c(NA, -10L), class = c("data.table", "data.frame"), .internal.selfref = <pointer: 0x000001617e051720>))))], 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(0.43419742679402, 0.527043992133521, 0.729322618498196, 0.160354888708514, 0.313807639368571, 0.431732126719258, 0.218423752004696, 0.285624676048191, 0.341459038088653, 0.62007922110501, 0.914476060937018, 0.294682064893172, 0.967176963375982, 0.452008565099714, 0.968167567309044, 0.956476943708804, 0.37489714650074, 0.949351411704406, 0.745206108859294, 0.65961871814322), GOOG = c(0.18438389758325, 0.148838790436291, 0.656374270848538, 0.502388472884081, 0.25578224293204, 0.208373117605576, 0.183778962748295, 0.403398089039455, 0.21701122336441, 0.547059566600781, 0.868812716744743, 0.382470010130231, 0.917286533553562, 0.535238309916164, 0.92868551606237, 0.858010595366338, 0.338381529468178, 0.824190497576203, 0.594766456348915, 0.346333884963595)), row.names = c(NA, -20L), class = c("data.table", "data.frame"), .internal.selfref = <pointer: 0x000001617e051720>))))], 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(0.314918979046757, 0.899069667349924, 0.933115583153673, 0.608976138565978, 0.639555570455537, 0.259831318841244, 0.103189261932428, 0.896792292571362, 0.571084077044327, 0.182312809327622), AMZN = c(0.0843033295557694, 0.656588191456108, 0.862148704699873, 0.112875777087018, 0.772568583251836, 0.568329611564962, 0.888242430386166, 0.431975055030328, 0.448933279569626, 0.806590901702923), GOOG = c(0.368395902914926, 0.827139122178778, 0.545264427317306, 0.240561558166519, 0.591966148698702, 0.715501645347103, 0.647069754777476, 0.571875439956784, 0.582404206739739, 0.250119536183774)), row.names = c(NA, -10L), class = c("data.table", "data.frame"), .internal.selfref = <pointer: 0x000001617e051720>))))], 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(0.0158760267956106, 0.0492902581517949, 0.0272822908314138, 0.041443676665894, 0.115607395752768, 0.139344453242549, 0.237110205888, 0.54616142939117, 0.0980636886058451, 0.311710715544133, 0.0795978882402194, 0.597109700697452, 0.790154329608633, 0.954653504375768, 0.987661589514458, 0.520035857296455, 0.203790915593028, 0.582342113981039, 0.156414743620083, 0.489661971077101, 0.681975477747511, 0.567111795545474, 0.310490327262913, 0.206299730045439, 0.548495861927884, 0.51528236372171, 0.890860320540014, 0.663332292315449, 0.221119188387323, 0.326642600503673, 0.895946892531268, 0.16868153407414, 0.867548271666544), AMZN = c(0.0433573178804106, 0.0376613555113354, 0.0277947484646826, 0.0612228287119474, 0.706671884404236, 0.0968687452481607, 0.147529752533335, 0.553899715267037, 0.205134144252485, 0.0839218277026682, 0.0582225669330417, 0.789255381413218, 0.408540929088664, 0.575166948196988, 0.320421938558271, 0.407717559069338, 0.281869878794391, 0.133908975810383, 0.623879951055088, 0.311878159479511, 0.410385714057985, 0.401843196622008, 0.321302049913061, 0.489904015154591, 0.558256970197951, 0.617428415054142, 0.25379703086321, 0.602034186152955, 0.639839311642034, 0.582479560819898, 0.596158138469941, 0.934376583130243, 0.188394914846784), 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: 0x000001617e051720>))))], 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-windows-x86_64

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 Flavor: r-devel-windows-x86_64

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