CRAN Package Check Results for Package portvine

Last updated on 2025-12-24 09:49:51 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	90.80	145.61	236.41	ERROR
r-devel-linux-x86_64-fedora-clang	1.0.3	176.00	327.04	503.04	ERROR
r-devel-linux-x86_64-fedora-gcc	1.0.3	245.00	358.86	603.86	ERROR
r-devel-windows-x86_64	1.0.3	114.00	377.00	491.00	OK
r-patched-linux-x86_64	1.0.3	124.76	382.89	507.65	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	120.00	384.00	504.00	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’ [54s/66s] 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(0.103294937666565, 0.0201466377441613, 0.558949878493885, 0.157632560307026, 0.583124730864003, 0.00471906513949975, 0.844069277494296, 0.66254948403128, 0.489543817179155, 0.1145579296449), AMZN = c(0.343413769549578, 0.0109895299595582, 0.32942947674982, 0.641361667378946, 0.652645710276041, 0.0104758752301111, 0.30327149049647, 0.249804961546054, 0.138690066028013, 0.0295472115427942), GOOG = c(0.240591500885785, 0.0140851261094213, 0.535984480753541, 0.748588654212654, 0.590804300969467, 0.0112526998855174, 0.42154498025775, 0.338113415287808, 0.138944401638582, 0.0544274521525949)), row.names = c(NA, -10L), class = c("data.table", "data.frame"), .internal.selfref = <pointer: 0x5585a129a070>))))], 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(0.470560425527504, 0.584798877348132, 0.513541778036054, 0.0516509161391466, 0.571815319508871, 0.563452814198207, 0.103256384969731, 0.476762341760245, 0.484599677972583, 0.702309605178956, 0.63341583124543, 0.946948629233084, 0.285051895488782, 0.541655121216514, 0.807994282291838, 0.79477955413014, 0.567024158763904, 0.939197151018146, 0.518774881681219, 0.601353071761318), GOOG = c(0.788217537775325, 0.220662095161435, 0.406463246616197, 0.0402604522507692, 0.598248375570471, 0.45028725593883, 0.229724382131041, 0.610879358746003, 0.429417885440605, 0.865175134529795, 0.568904950304932, 0.926071905104834, 0.852722084712922, 0.936453586081918, 0.787547778934578, 0.605656679804133, 0.443830922386154, 0.714053099380145, 0.461760603601286, 0.894975636089316)), row.names = c(NA, -20L), class = c("data.table", "data.frame"), .internal.selfref = <pointer: 0x5585a129a070>))))], 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(0.155661713832628, 0.8069244726263, 0.104545243704895, 0.829118872797093, 0.767984560557012, 0.318223621381585, 0.277106501169414, 0.994400833376912, 0.0188610098482864, 0.537719874501637), AMZN = c(0.592063672652504, 0.700687627949273, 0.0263473821688384, 0.964406583693967, 0.0585822082386244, 0.54323471679353, 0.530240272773937, 0.853816579745772, 0.110564746352164, 0.338684423091089), GOOG = c(0.0674264119006693, 0.612281360896304, 0.30476849176921, 0.927465525921434, 0.30575927067548, 0.54110717237927, 0.638076891656965, 0.981474695494398, 0.00267581618390977, 0.212413465371355)), row.names = c(NA, -10L), class = c("data.table", "data.frame"), .internal.selfref = <pointer: 0x5585a129a070>))))], 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(0.163073676732456, 0.0485782287697687, 0.104490289427267, 0.0531389576856833, 0.233080612793196, 0.465789794327971, 0.048507895501978, 0.271166209624208, 0.0373893088873405, 0.494625875252758, 0.375611189813018, 0.304108992961715, 0.277590370671634, 0.40748848057576, 0.456681766729297, 0.352841812102526, 0.659134878760122, 0.992590714884607, 0.148649582625346, 0.161469529298211, 0.788898430998239, 0.7533529849809, 0.707456895449426, 0.804577130598051, 0.687719353583725, 0.875701548235877, 0.306744987256436, 0.308402125516677, 0.855869302707829, 0.245472519758194, 0.825571200542259, 0.974244886430119, 0.669420154875875), AMZN = c(0.0260006825590624, 0.110878346113174, 0.0983182922461488, 0.387616419066672, 0.106489019117297, 0.684264451232037, 0.193912845130371, 0.251530850564651, 0.219771032995312, 0.430321438763334, 0.30811118549709, 0.296897372766485, 0.321993850545662, 0.533677999595723, 0.354154342758572, 0.390096809104023, 0.867657900258402, 0.292622199508847, 0.185085727705197, 0.487255320928444, 0.578913296214616, 0.434597979535634, 0.882260207259635, 0.574568380688283, 0.498338009852136, 0.543537101102639, 0.883264180540189, 0.837998030457898, 0.749460574510543, 0.397509294905507, 0.342355754086322, 0.877083307841792, 0.526476022157738), 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: 0x5585a129a070>))))], 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: tests
Result: ERROR Running ‘testthat.R’ [144s/246s] 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(0.718526606436132, 0.475833436610043, 0.412313807859543, 0.0413088490476586, 0.264754283048327, 0.147318070178771, 0.308031697651182, 0.0524908230571793, 0.247831249898646, 0.106815355637443), AMZN = c(0.969039768207111, 0.750929720353668, 0.667926191224881, 0.312971944303855, 0.0574310920039367, 0.34805695284484, 0.255674251839462, 0.117643167359739, 0.0432653992964187, 0.352708479138105), GOOG = c(0.974323860835284, 0.835823182249442, 0.339577862294391, 0.230673882411793, 0.00366069958545268, 0.0836545054335147, 0.508594640064985, 0.180221837712452, 0.0564712160266936, 0.281191827729344)), row.names = c(NA, -10L), class = c("data.table", "data.frame"), .internal.selfref = <pointer: 0x556ae6e34d10>))))], 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(0.825695284946677, 0.655505338927785, 0.887488186692416, 0.701234704695342, 0.924937575638415, 0.152555059777903, 0.756022870712266, 0.320059807102566, 0.13681825007663, 0.367096454841227, 0.972890765941204, 0.418325751468481, 0.469053773119616, 0.329709911270878, 0.987518561445917, 0.500328764274174, 0.471440705798752, 0.994093203042766, 0.426633674652524, 0.47701833571023), GOOG = c(0.71531444133358, 0.53433257385978, 0.802152882730699, 0.727987320907963, 0.914021155182108, 0.300997356173101, 0.642549070794169, 0.45861552107405, 0.0960497744170484, 0.253807090581922, 0.919972897303793, 0.483778045278035, 0.748785572429732, 0.26482453089679, 0.98518294286714, 0.583221769364675, 0.250351653269404, 0.944582764963066, 0.465590177284757, 0.865426906703334)), row.names = c(NA, -20L), class = c("data.table", "data.frame"), .internal.selfref = <pointer: 0x556ae6e34d10>))))], 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(0.282699765535377, 0.0381585150924177, 0.656742228526312, 0.567724234336142, 0.745679573467504, 0.778639787117334, 0.595001351262847, 0.422014253159912, 0.543008178627907, 0.847914759511941), AMZN = c(0.390961113559897, 0.0552335052898831, 0.747264768809378, 0.859719849374102, 0.390111608241735, 0.764847483242016, 0.608642674533241, 0.868848007763797, 0.335473787166811, 0.54283666236452), GOOG = c(0.286428900901228, 0.0561732288915664, 0.163319104816765, 0.805340382736176, 0.819574760971591, 0.525371882598847, 0.507191417738795, 0.68498287955299, 0.278877399628982, 0.543300217948854)), row.names = c(NA, -10L), class = c("data.table", "data.frame"), .internal.selfref = <pointer: 0x556ae6e34d10>))))], 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(0.19185045963516, 0.244540019320445, 0.156068741507922, 0.389366047228772, 0.508178213882051, 0.208594799966003, 0.127441903324975, 0.0950219730617204, 0.835838639140958, 0.0699385004398883, 0.164091707797963, 0.34310989102025, 0.621312572260519, 0.273983894623184, 0.589991500148622, 0.588234847797333, 0.152688413101007, 0.251512780517547, 0.230182690690897, 0.686086491143567, 0.521191142915592, 0.667476890479493, 0.973842923639935, 0.613520714140452, 0.832351913458827, 0.84576337391864, 0.701710303490789, 0.237742473035078, 0.999232362829045, 0.0941717831471827, 0.2326125982126, 0.736067518193621, 0.0427822865887598), AMZN = c(0.145130173899164, 0.017652901140712, 0.202039037964811, 0.272432695561184, 0.0840294703282516, 0.0238828718958994, 0.311340657464489, 0.143601010498087, 0.0713595686250142, 0.0395755360242773, 0.100285776596697, 0.452545970258498, 0.944242708278853, 0.529644313143013, 0.324797166871501, 0.372614177705597, 0.402254002014107, 0.773345062976102, 0.0673234089457325, 0.622880849668136, 0.603948825306637, 0.449257848021354, 0.670074479604711, 0.455167775713569, 0.693064061762711, 0.639959342147089, 0.794674322612641, 0.290082313877256, 0.770037592373215, 0.161878580171191, 0.693976741819808, 0.712089590611978, 0.55142703527453), 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, 0.60420106897784, 0.60420106897784, 0.60420106897784, 0.60420106897784, 0.60420106897784, 0.60420106897784, 0.60420106897784, 0.60420106897784, 0.60420106897784, 0.60420106897784, 0.60420106897784)), row.names = c(NA, -33L), class = c("data.table", "data.frame"), .internal.selfref = <pointer: 0x556ae6e34d10>))))], 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 ════════════════════════════════════════════════════════════════════════ Don't worry, you'll get it. Error: ! Test failures. Execution halted Flavor: r-devel-linux-x86_64-fedora-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-fedora-clang, r-devel-linux-x86_64-fedora-gcc

Version: 1.0.3
Check: tests
Result: ERROR Running ‘testthat.R’ [137s/312s] 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(0.933337830602557, 0.336796003349739, 0.38633781595391, 0.935920688412351, 0.947761514701936, 0.41443401242785, 0.424234557670904, 0.967153235557407, 0.927003222215084, 0.658316855713163), AMZN = c(0.665203034651981, 0.274778527558551, 0.956871474081642, 0.870738702147374, 0.92215662060385, 0.302593031732017, 0.371281213363441, 0.5730019348021, 0.731846721254196, 0.933046534258727), GOOG = c(0.643214521696791, 0.446635798783973, 0.933941656956449, 0.904548884136602, 0.954211223637685, 0.146562018897384, 0.473660506540909, 0.863887916551903, 0.836849274346605, 0.906978344311938)), row.names = c(NA, -10L), class = c("data.table", "data.frame"), .internal.selfref = <pointer: 0xbfc4a0>))))], 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(0.559057756683558, 0.898607140538241, 0.122769543534366, 0.627467335712493, 0.315410196740377, 0.35378657779538, 0.951525348694046, 0.64148074490455, 0.388703190242298, 0.109716364004691, 0.644406256496419, 0.91919702821498, 0.771045732634795, 0.283701502111569, 0.614507274647021, 0.470103548014585, 0.287255488621603, 0.347732515795125, 0.584889075729598, 0.347963956176981), GOOG = c(0.356660004506925, 0.450288563364447, 0.192072648336399, 0.790831746249447, 0.691118407074415, 0.0474663115363301, 0.434865260317017, 0.983141460826723, 0.521597805366181, 0.0893271535078499, 0.823710074811187, 0.780645794769494, 0.851604192172723, 0.538882994690141, 0.837152002872798, 0.556738839843399, 0.330073397366495, 0.278291564299887, 0.564225600626884, 0.296950463750482)), row.names = c(NA, -20L), class = c("data.table", "data.frame"), .internal.selfref = <pointer: 0xbfc4a0>))))], 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(0.701498820183552, 0.595195984803552, 0.670640850947968, 0.0343084546289792, 0.980404308456794, 0.734339999004332, 0.0185065324366697, 0.748399565662588, 0.570071951616576, 0.198031247840211), AMZN = c(0.766815243918829, 0.709971761976644, 0.957312824025433, 0.239081276063353, 0.942470927514406, 0.709034736604399, 0.565856146821749, 0.529498240315648, 0.626896730742687, 0.524763220939923), GOOG = c(0.756997119868174, 0.39860128541477, 0.976977629121393, 0.0268001514486969, 0.974043536931276, 0.339376325020567, 0.114894159603864, 0.58225199719891, 0.790727607673034, 0.616314009064808)), row.names = c(NA, -10L), class = c("data.table", "data.frame"), .internal.selfref = <pointer: 0xbfc4a0>))))], 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(0.0315401226290121, 0.299837505141465, 0.0611061159413786, 0.17715612002223, 0.0763432736554657, 0.0950058460818962, 0.295430398015383, 0.023911759124858, 0.421608000087063, 0.336492268983291, 0.400314138165895, 0.204684958429436, 0.0492985814472654, 0.430479496105065, 0.571289664914532, 0.30076243493934, 0.398715052212254, 0.408210336088904, 0.376264713175056, 0.65660947242467, 0.323444922626321, 0.570027965356956, 0.841236546487585, 0.443916984149143, 0.89736854847446, 0.783411066224348, 0.830340725252729, 0.882086223125173, 0.117969521526175, 0.684378833038382, 0.821449083651907, 0.601899203074604, 0.89174632410857), AMZN = c(0.0488346400981804, 0.816247379816491, 0.0330315975452013, 0.125064591761923, 0.00364321085687793, 0.200371825398839, 0.321360111081694, 0.0587594854814392, 0.154610368292114, 0.0210612807094843, 0.31347843632979, 0.703145833265986, 0.166479364550941, 0.758882465811673, 0.890405610163027, 0.785172707252862, 0.172373788023061, 0.270213249570636, 0.333065734306199, 0.404025102620663, 0.401907732521372, 0.424258927887141, 0.635152113908331, 0.52677790767442, 0.671338323749991, 0.463546651483495, 0.859279361222997, 0.914644623616566, 0.828914104083503, 0.80306381747423, 0.475385804062065, 0.625769973967935, 0.859073288363561), 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, 0.60420106897784, 0.60420106897784, 0.60420106897784, 0.60420106897784, 0.60420106897784, 0.60420106897784, 0.60420106897784, 0.60420106897784, 0.60420106897784, 0.60420106897784, 0.60420106897784)), row.names = c(NA, -33L), class = c("data.table", "data.frame"), .internal.selfref = <pointer: 0xbfc4a0>))))], 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 ════════════════════════════════════════════════════════════════════════ Don't worry, you'll get it. Error: ! Test failures. Execution halted Flavor: r-devel-linux-x86_64-fedora-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