CRAN Package Check Results for Package portvine

Last updated on 2025-12-22 05:51:07 CET.

Flavor	Version	T_install	T_check	T_total	Status
r-devel-linux-x86_64-debian-clang	1.0.3	108.11	207.63	315.74	ERROR
r-devel-linux-x86_64-debian-gcc	1.0.3	85.99	149.14	235.13	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	235.00	333.03	568.03	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	147.00	510.00	657.00	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(0.348122725036255, 0.180050368658598, 0.163040087697795, 0.239780464530158, 0.409545299963893, 0.87538275636445, 0.419793951914838, 0.915846119756196, 0.599025049396638, 0.400982347213519), AMZN = c(0.186684017203946, 0.0855292140383457, 0.387436832964032, 0.537281340698176, 0.714514565692335, 0.998931666914048, 0.522206360186115, 0.123823480052256, 0.702127835515931, 0.768347567210545), GOOG = c(0.422229178482667, 0.600279432488605, 0.315684728324413, 0.808051274158061, 0.149527403060347, 0.999198323115706, 0.552730201277882, 0.759011649992317, 0.867773549864069, 0.425683172652498)), row.names = c(NA, -10L), class = c("data.table", "data.frame"), .internal.selfref = <pointer: 0x557f71197fe0>))))], 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(0.50486557071823, 0.792552248661389, 0.630355426564063, 0.00341242190806179, 0.982451260524746, 0.216868556132105, 0.851159608581003, 0.563418331262778, 0.355199964913762, 0.574965822502272, 0.168553502716933, 0.232815152848903, 0.631352903369893, 0.806443280835265, 0.937194422915023, 0.749923794806915, 0.676637255864209, 0.469814638238153, 0.80329877169219, 0.572070475594395), GOOG = c(0.442945597288965, 0.842609804724835, 0.456468686605616, 0.00199739283028677, 0.893893093735092, 0.297093098806597, 0.538011748857401, 0.539864365065393, 0.128368596214774, 0.459233653959128, 0.267040465716021, 0.239602673651291, 0.860581641192949, 0.85891348971742, 0.894250363080761, 0.921214145028252, 0.428183154452324, 0.386390354519513, 0.804122674378596, 0.584351406745643)), row.names = c(NA, -20L), class = c("data.table", "data.frame"), .internal.selfref = <pointer: 0x557f71197fe0>))))], 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(0.614034877943048, 0.831750493902743, 0.43621126950343, 0.099211325052056, 0.540573581550013, 0.69483166911663, 0.221215241060226, 0.167628672469617, 0.840824740116602, 0.518529570641668), AMZN = c(0.398167524665616, 0.961073625414861, 0.414771971563106, 0.539280923233098, 0.199745624755126, 0.481508081461799, 0.4850073109027, 0.0272308351757838, 0.978517347003656, 0.0759227529596271), GOOG = c(0.351116159697995, 0.935714936815202, 0.436500692507252, 0.274984018877149, 0.137727055698633, 0.694072728045285, 0.591159790055826, 0.373751058941707, 0.892811226891354, 0.131084441673011)), row.names = c(NA, -10L), class = c("data.table", "data.frame"), .internal.selfref = <pointer: 0x557f71197fe0>))))], 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(0.142334243392975, 0.0433727052733784, 0.214661688318323, 0.880695042436217, 0.0950697819514598, 0.0583017140863114, 0.374599727186896, 0.123788237046788, 0.0495809492417316, 0.233641313541844, 0.0567869675497495, 0.798115015297313, 0.63924777516204, 0.59312804122628, 0.990513755329153, 0.160701363476496, 0.570278939683211, 0.287475845228448, 0.602704960573836, 0.498523693089613, 0.922297316708726, 0.349539669327752, 0.772428786346805, 0.416484816149735, 0.912815193010136, 0.993924889396276, 0.48231855957724, 0.18804551718091, 0.755647906184204, 0.175345632417213, 0.58846482872135, 0.624034049315488, 0.868954360844109), AMZN = c(0.0802782064502701, 0.0864776404379262, 0.0676866090973341, 0.149721422886136, 0.135541155053647, 0.0550926380116578, 0.428337509071645, 0.0481979944059553, 0.0295965191198419, 0.0537648798116051, 0.0105145002049057, 0.783512409901613, 0.619267852025706, 0.212038330298411, 0.493065808986012, 0.558183469336935, 0.461579776859543, 0.269453225203402, 0.832291104437232, 0.493370924436178, 0.606244123957244, 0.41886144082921, 0.563509582667885, 0.812624075361407, 0.630909356727256, 0.709222826638664, 0.316949027776179, 0.356874639428122, 0.738156531688809, 0.536623146664786, 0.527927782158837, 0.240498071343486, 0.293490013184478), 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: 0x557f71197fe0>))))], 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/71s] 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(0.11320508373437, 0.353133529058954, 0.251513357556628, 0.164917046858801, 0.0784556226074841, 0.838804328766313, 0.686315569281068, 0.0979465097584953, 0.34807506586374, 0.361144889790834), AMZN = c(0.275111751384275, 0.644370884432225, 0.174615757859687, 0.826464425976389, 0.108434638726204, 0.21231474244931, 0.226252730223658, 0.345403743002767, 0.479975964810068, 0.21760180935855), GOOG = c(0.290130169596523, 0.4875311285723, 0.219786477508023, 0.840008921455592, 0.156906319083646, 0.046023960923776, 0.103542649187148, 0.316780486609787, 0.719187538605183, 0.293448276119307)), row.names = c(NA, -10L), class = c("data.table", "data.frame"), .internal.selfref = <pointer: 0x55e0d1402070>))))], 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(0.0915560036742176, 0.569580774726042, 0.108258257599089, 0.786725886098348, 0.860599542125202, 0.966956015047692, 0.600756313973079, 0.191544701563371, 0.0698650622848646, 0.745065090664168, 0.573885748182902, 0.970280410528648, 0.602779656497948, 0.953552282225145, 0.27205722406378, 0.391727762286329, 0.937410160130404, 0.442883871511641, 0.879609492963208, 0.54415434492208), GOOG = c(0.109132198713656, 0.639630256836064, 0.793517322768959, 0.776701034379472, 0.598636341344906, 0.965394529974875, 0.332439287704595, 0.240893062802225, 0.136720853993768, 0.575950775758978, 0.539162944135981, 0.967994850566658, 0.468528192975295, 0.934712562101459, 0.35444723620878, 0.141252078757051, 0.810384081849477, 0.930790751760416, 0.49836364620747, 0.282984529110901)), row.names = c(NA, -20L), class = c("data.table", "data.frame"), .internal.selfref = <pointer: 0x55e0d1402070>))))], 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(0.0256330410918296, 0.0271962670234349, 0.435298437589248, 0.631711931181425, 0.684785315182151, 0.255912475991657, 0.942440034880124, 0.723376928909745, 0.819664856632702, 0.165220154068971), AMZN = c(0.099109304495614, 0.117541454792004, 0.438199402318242, 0.655323611235747, 0.2927058501967, 0.230406730619839, 0.418564905612232, 0.473437282773323, 0.218540417301504, 0.0785080733926567), GOOG = c(0.0819008240941912, 0.143792133079842, 0.480850358726457, 0.735384982312098, 0.73632126650773, 0.0814002742990851, 0.845068693161011, 0.641973408637568, 0.767669097986072, 0.165615647099912)), row.names = c(NA, -10L), class = c("data.table", "data.frame"), .internal.selfref = <pointer: 0x55e0d1402070>))))], 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(0.200815614758383, 0.00459534787895854, 0.947227049932701, 0.00718139291769393, 0.961081156835415, 0.43833072594677, 0.0256912450934778, 0.0961104575412268, 0.638272334365815, 0.17811140342001, 0.32280255790819, 0.176696388198437, 0.929878611601768, 0.754818629959014, 0.936088855829927, 0.378951952709197, 0.656682220311385, 0.950450652699109, 0.488338808405443, 0.898907980650302, 0.722108307955095, 0.549041727539989, 0.490802749440027, 0.493703298692991, 0.641327171262453, 0.528702282721628, 0.997469781629378, 0.53948893438628, 0.804953428650141, 0.637426490933021, 0.58547579940707, 0.979359278376031, 0.480902617999898), AMZN = c(0.168867324084273, 0.0237192182905867, 0.28685661095296, 0.266836432123925, 0.156600829728797, 0.0409481000556147, 0.145877891666199, 0.0793488217447616, 0.541780270386481, 0.101587202714717, 0.0700308958868752, 0.77304933067811, 0.270627204353339, 0.473862254748937, 0.783724419034938, 0.918244427205542, 0.310401367485534, 0.538289575543527, 0.399240751753844, 0.794122726430907, 0.44117241009117, 0.746705733946846, 0.928018630988026, 0.449077569281997, 0.713296891385252, 0.316889756082928, 0.887023897530032, 0.392736313355736, 0.208885654135472, 0.910134586433005, 0.892215953300201, 0.752896007817028, 0.915829052592296), 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: 0x55e0d1402070>))))], 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/225s] 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(0.209526518594617, 0.888801740485287, 0.880086159491367, 0.982563181147936, 0.851817354623519, 0.327326762054149, 0.864145719693954, 0.9058113201714, 0.506971232891706, 0.104884515229914), AMZN = c(0.171110863125932, 0.413130016297171, 0.347645071950379, 0.520931748390738, 0.639416839798479, 0.249509386838978, 0.478589034472945, 0.990310112714462, 0.437671833052118, 0.0526139439824615), GOOG = c(0.719122365582734, 0.585058780387044, 0.867405500262976, 0.834015868371353, 0.762314558960497, 0.3776250469964, 0.728003450203687, 0.340336070163175, 0.58039515465498, 0.0901855339761823)), row.names = c(NA, -10L), class = c("data.table", "data.frame"), .internal.selfref = <pointer: 0x39f1c4a0>))))], 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(0.444866215751782, 0.628593477283579, 0.520855319984916, 0.156222642167859, 0.553944506416007, 0.777213271501424, 0.398388293741356, 0.47505402040871, 0.509443814204446, 0.384254462128248, 0.407129909701973, 0.484859761166073, 0.192622567955219, 0.16835877792208, 0.738648191114862, 0.814797735537648, 0.294067885505825, 0.25719934846251, 0.840337610427712, 0.530428837903932), GOOG = c(0.311061521802173, 0.520483637116283, 0.737218004132263, 0.362397672958788, 0.79210034775356, 0.784367600471135, 0.474584164317475, 0.716676449213835, 0.758024230334118, 0.643883593599405, 0.58079529751331, 0.647216157711304, 0.3471484341708, 0.15752329822806, 0.70369533616663, 0.586169478934378, 0.344716040724017, 0.300914784812098, 0.690035567183748, 0.634055257675292)), row.names = c(NA, -20L), class = c("data.table", "data.frame"), .internal.selfref = <pointer: 0x39f1c4a0>))))], 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(0.316017737483609, 0.984920103473055, 0.616457431950822, 0.546045223958843, 0.0460986991532727, 0.706085798114056, 0.0205603667530673, 0.10892143728145, 0.688835805351214, 0.611960919412703), AMZN = c(0.190530428182826, 0.558974788455794, 0.881708455507389, 0.298013812668616, 0.0957197921039991, 0.427908683264522, 0.144343779480111, 0.764404012126395, 0.776145371201081, 0.840432336231161), GOOG = c(0.126073424238712, 0.465829518157989, 0.742902743397281, 0.333654104731977, 0.0120420018211007, 0.272319915238768, 0.00978341395966709, 0.625954698538408, 0.569327579112723, 0.333761575864628)), row.names = c(NA, -10L), class = c("data.table", "data.frame"), .internal.selfref = <pointer: 0x39f1c4a0>))))], 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(0.0521097794712437, 0.0457964223086946, 0.00640723535007526, 0.382560855578069, 0.388009270394654, 0.0182644498775305, 0.998658854776191, 0.0565972795053729, 0.544422455653062, 0.0849078667134904, 0.422868781326148, 0.67725439700215, 0.82571413233788, 0.985152530979974, 0.954835187647013, 0.49481362453126, 0.455316346367263, 0.234017881147352, 0.544291014828739, 0.564694186098919, 0.146658953906134, 0.643361299392134, 0.37363677394706, 0.763151489204603, 0.352505876189958, 0.478443762560652, 0.291368700119701, 0.761586188810779, 0.825929654963075, 0.639989906752788, 0.460761545834917, 0.891116650296842, 0.913862712288067), AMZN = c(0.090486280940577, 0.083564726359194, 0.0282303647368748, 0.0632207038088541, 0.566266436349274, 0.0291773090197369, 0.535153081258915, 0.0305919759434213, 0.215770669138778, 0.0629827078234699, 0.339614405764356, 0.856401403756023, 0.617636711208802, 0.582300403600638, 0.958777111989064, 0.45080054176498, 0.241327265066479, 0.598577492363748, 0.469945257030295, 0.933513443739573, 0.514584199848292, 0.171974737152868, 0.948132457634868, 0.623369315065158, 0.684340401846625, 0.630598956273145, 0.261470170390827, 0.450934506641964, 0.825286874133172, 0.252728002384227, 0.569850002999883, 0.575907441988026, 0.897178524131068), 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: 0x39f1c4a0>))))], 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-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