R Under development (unstable) (2023-12-20 r85713 ucrt) -- "Unsuffered Consequences" Copyright (C) 2023 The R Foundation for Statistical Computing Platform: x86_64-w64-mingw32/x64 R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > #### doRUnit.R --- Run RUnit tests > ####------------------------------------------------------------------------ > > ### Originally follows Gregor Gojanc's example in CRAN package 'gdata' > ### and the corresponding section in the R Wiki: > ### http://wiki.r-project.org/rwiki/doku.php?id=developers:runit > > ### MM: Vastly changed: This should also be "runnable" for *installed* > ## package which has no ./tests/ > ## ----> put the bulk of the code e.g. in ../inst/unitTests/runTests.R : > > if(require("RUnit", quietly = TRUE)) { + + ## --- Setup --- + + wd <- getwd() + pkg <- sub("\\.Rcheck$", '', basename(dirname(wd))) + + library(package=pkg, character.only = TRUE) + + path <- system.file("unitTests", package = pkg) + + stopifnot(file.exists(path), file.info(path.expand(path))$isdir) + + # Make sure that required packages are loaded + library(fBasics) + library(timeDate) + library(timeSeries) + + source(file.path(path, "runTests.R"), echo = TRUE) + } > pkg <- "fExtremes" > if (require("RUnit", quietly = TRUE)) { + library(package = pkg, character.only = TRUE) + if (!(exists("path") && file.exists(path))) + .... [TRUNCATED] Executing test function test.blockMaxima ... GMT max.Close 2000-09-28 1.127639 2000-10-19 17.869179 2000-11-14 3.512376 2000-12-22 6.678420 2001-01-03 10.001484 2001-02-26 4.837053 2001-03-22 7.571182 2001-04-05 8.861458 2001-05-01 3.509641 2001-06-20 3.057358 2001-07-12 7.389313 2001-08-24 4.837124 2001-09-24 4.522989 GMT max.Close 2000-09-28 1.127639 2000-10-19 17.869179 2001-01-03 10.001484 2001-04-05 8.861458 2001-07-12 7.389313 GMT max.Close 2000-10-19 17.869179 2000-10-26 5.073191 2000-12-11 6.446676 2001-01-03 10.001484 2001-01-24 3.846628 2001-02-26 4.837053 2001-04-05 8.861458 2001-05-01 3.509641 2001-05-24 2.856933 2001-07-12 7.389313 2001-07-19 2.794649 2001-08-24 4.837124 2001-09-24 4.522989 16 1 12 7 1 3 11 8 17.869179 5.073191 6.446676 10.001484 3.846628 4.837053 8.861458 3.509641 5 18 3 9 5 2.856933 7.389313 2.794649 4.837124 4.522989 done successfully. Executing test function test.deCluster ... GMT max.Close 2000-10-19 17.8691789 2000-12-26 0.9377162 2001-01-03 10.0014841 2001-01-19 9.4490843 2001-04-05 8.8614584 2001-04-18 6.2268950 2001-07-12 7.3893126 2001-09-10 3.8595691 2001-09-24 4.5229888 done successfully. Executing test function test.findThreshold ... done successfully. Executing test function test.pointProcess ... done successfully. Executing test function test.exindexPlot ... done successfully. Executing test function test.exindexesPlot ... done successfully. Executing test function test.fTHETA ... done successfully. Executing test function test.thetaFit ... done successfully. Executing test function test.thetaSim ... Time Series: Start = 1 End = 1000 Frequency = 1 [1] 0.9234298 0.4617149 0.3815855 2.1592272 1.0796136 1.7261814 [7] 0.8630907 3.8835909 1.9417955 0.9708977 0.4854489 0.2877250 [13] 0.2265420 0.3882941 0.9582421 0.4791210 2.9077894 1.4538947 [19] 0.7269474 0.5855292 0.3436029 0.1720509 3.7072528 1.8536264 [25] 2.8647072 1.4323536 0.7161768 0.3580884 1.1249671 0.5624835 [31] 0.6023163 0.3011582 1.0701928 0.5350964 1.2069436 0.9242477 [37] 0.8702509 0.4351254 0.3970739 1.6164549 5.3353365 2.6676683 [43] 1.3338341 0.6669171 5.6229773 2.8114887 1.7878835 7.2039748 [49] 3.6019874 3.9179296 2.0239411 4.1596284 2.0798142 3.9824006 [55] 3.3163470 1.6581735 0.8290868 0.5247942 1.7033123 0.8516561 [61] 0.4258281 0.3953131 12.5805411 6.2902705 3.1451353 4.2665363 [67] 2.1332682 10.3154891 10.0765838 5.0382919 2.5191459 8.8964955 [73] 4.4482478 2.2241239 1.2430213 0.6215107 3.1919953 1.5959977 [79] 0.7979988 1.5984266 2.3663116 1.5558439 0.7779220 0.5113500 [85] 0.3915684 0.4240913 0.4496788 12.2341719 6.1170860 3.0585430 [91] 4.0262038 2.0131019 1.0065510 0.6468049 0.3838016 0.6894194 [97] 0.3539074 0.7720911 0.3860456 0.6700247 0.4879041 2.5566123 [103] 6.4072151 3.2036076 1.6018038 0.8009019 0.4004509 0.6782264 [109] 0.8530718 0.7469631 0.4465994 0.8690644 2.3831987 1.1915994 [115] 1.8224502 0.9876368 0.4938184 0.4016758 3.9318295 1.9659148 [121] 0.9829574 0.4914787 2.0262399 1.0131200 0.5065600 0.3854749 [127] 0.3783741 0.3344593 1.8229313 0.9114656 1.4178580 1.5360333 [133] 1.2032867 0.7615944 0.3807972 1.1454648 1.2177746 0.6088873 [139] 1.1658002 0.5858003 0.9392072 0.9301307 0.5745252 2.3181253 [145] 1.1590626 1.8171991 71.1807950 35.5903975 17.7951987 8.8975994 [151] 4.4487997 2.2243998 1.1121999 3.6706026 1.8353013 4.7521725 [157] 2.3760863 1.1880431 0.5940216 8.2465079 4.1232539 2.0616270 [163] 1.2398301 3.8165945 1.9082972 0.9541486 0.6590265 7.0382676 [169] 3.5191338 1.7595669 0.8797835 0.4398917 0.2199459 1.3317936 [175] 0.6658968 0.3329484 0.5310341 0.8091669 2.5440910 1.3648654 [181] 3.9666309 2.6836323 2.7726189 1.4200092 0.7100046 0.7687373 [187] 0.3843686 0.2492497 0.3675214 1.8360915 0.9180457 1.4430069 [193] 3.0910184 1.5455092 2.6575687 1.5141357 7.5729155 8.9293403 [199] 4.4646701 2.2323351 1.1161675 1.3706469 5.1235555 2.5617777 [205] 10.5907357 5.2953679 2.6476839 1.3238420 0.6619210 2.9916976 [211] 1.4958488 0.7479244 0.8568089 0.4922619 0.2650350 105.4923561 [217] 52.7461781 26.3730890 13.1865445 6.5932723 3.2966361 1.6483181 [223] 0.8241590 0.4120795 2.5093156 1.2546578 0.6273289 0.3136644 [229] 0.3867374 0.2292789 1.1169199 0.6874848 0.4738576 0.3750678 [235] 0.4362205 2.7343935 1.3671967 1.2558752 0.6279376 0.5394234 [241] 0.4263790 0.9786213 24.4286803 12.2143401 6.1071701 3.0535850 [247] 1.5267925 1.2606891 0.6303446 1.0908896 0.6640274 2.3204628 [253] 5.4058896 2.7029448 1.3514724 13.4214157 6.7107078 3.3553539 [259] 1.6776770 0.8388385 14.7145126 7.3572563 3.6786281 1.8393141 [265] 1.2960354 0.7160681 0.3580340 0.3365950 1.1542918 1.9892175 [271] 1.1754938 7.4143037 3.7071518 23.2342221 11.6171110 5.8085555 [277] 2.9042778 1.4521389 1.8993308 0.9496654 1.0769402 0.5618097 [283] 0.2809048 0.6952105 28.1244670 14.0622335 7.0311167 3.5155584 [289] 1.7577792 0.9651384 1.0351609 37.8204552 18.9102276 9.4551138 [295] 4.7275569 2.3637784 1.1818892 2.8730789 1.4365395 0.7182697 [301] 0.3591349 2.5495441 1.2747720 0.6373860 0.3212712 0.1763549 [307] 0.3302557 0.5258994 0.9056570 6.0906849 3.0453424 1.5226712 [313] 0.7613356 0.3806678 0.2863850 1.0167952 1.7333487 0.8666743 [319] 11.8786893 5.9393446 2.9696723 1.4848362 3.1155395 1.5577698 [325] 0.7788849 1.0298160 2.6444638 1.3222319 0.6611159 6.5667225 [331] 3.2833612 60.4772626 30.2386313 15.1193156 7.5596578 3.7798289 [337] 1.8899145 1.4339418 11.2544031 5.6272016 2.8136008 1.4068004 [343] 12.9200214 6.4600107 3.2300053 1.6150027 0.8075013 0.4037507 [349] 1.7656329 0.8828165 0.4414082 1.3130155 0.6565077 4.5177744 [355] 2.2588872 1.1294436 0.5647218 0.9154307 0.8548925 0.7591195 [361] 15.2297537 7.6148769 3.8074384 2.4287893 1.2143947 0.6071973 [367] 0.3035987 0.2366523 0.5257524 0.8925958 0.5991199 1.9378381 [373] 0.9689190 0.6326720 0.3163360 0.2861997 0.3916979 1.4394521 [379] 0.7197261 0.3598630 1.4152834 1.5692454 0.7846227 2.4791020 [385] 1.2395510 0.6197755 2.5888884 1.2944442 0.6472221 1.5209784 [391] 0.7604892 0.6307502 0.7269286 5.2458643 2.6229321 1.3114661 [397] 0.6557330 0.3278665 0.1804947 1.3860055 1.6268643 0.8134322 [403] 0.9376727 4.5695168 2.2847584 1.1423792 0.5711896 1.2281447 [409] 0.6140724 0.6698750 1.0221554 0.5110777 1.5884154 0.7942077 [415] 5.3618289 2.6809144 2.9094035 1.4547018 0.7273509 0.3636754 [421] 3.0665762 1.5332881 1.4570900 0.7285450 0.5385478 0.3397408 [427] 0.6223537 2.7902735 1.3951367 2.6813261 1.3406630 1.1225107 [433] 0.9804213 0.4902106 23.3323947 11.6661973 5.8330987 2.9165493 [439] 1.4582747 0.7291373 0.9912704 2.3448258 1.1724129 0.5862065 [445] 2.1113096 1.0556548 0.5278274 0.3289746 0.5997953 2.2991581 [451] 1.2048685 0.8101968 0.4050984 0.3583783 0.8915191 0.4457596 [457] 0.2268241 0.9840541 0.6864954 0.3432477 0.6227121 0.3113561 [463] 0.6818711 0.6118751 1.9365861 2.1611088 1.0805544 0.7641158 [469] 1.2992995 2.4327253 3.7715418 1.8857709 17.6207507 8.8103754 [475] 4.4051877 2.2025938 7.8770073 3.9385036 1.9692518 3.8225595 [481] 3.8285759 1.9142879 0.9571440 0.4785720 0.7097466 2.1603007 [487] 1.9258972 2.4064702 1.2032351 0.6016176 1.4876020 8.8171448 [493] 4.4085724 3.2799485 1.6399743 0.8199871 0.4211402 0.9112404 [499] 0.4556202 0.4434480 0.5789590 0.6330192 0.9331165 0.9017597 [505] 0.4508798 0.3506841 0.3049972 0.7539227 0.6354174 0.3916286 [511] 1.0069515 0.7228579 0.4842787 3.3955494 9.3624279 4.6812139 [517] 2.3406070 1.1703035 0.5851517 0.9195894 1.6860110 1.3637666 [523] 8.0838581 5.0260593 2.5130297 3.1082600 1.5541300 0.7770650 [529] 1.6919998 0.8459999 0.4229999 20.1270898 10.3147456 5.1573728 [535] 2.5786864 1.2893432 0.7663450 0.6567375 1.9033991 0.9516996 [541] 4.3068863 2.1534431 19.0574787 9.5287394 4.7643697 2.3821848 [547] 1.1910924 1.4691235 0.7345618 1.7437678 5.3496943 2.6748471 [553] 1.3374236 39.3973461 19.6986730 9.8493365 4.9246683 2.4623341 [559] 56.1807900 28.0903950 14.0451975 7.0225987 3.5112994 1.7556497 [565] 0.8778248 1.2491569 0.6245785 0.3122892 0.5977035 4.5739164 [571] 2.2869582 4.6680650 2.3340325 2.3794558 1.1897279 0.6219419 [577] 4.4036450 2.2018225 1.4840666 2.1028697 3.5820538 1.7910269 [583] 0.8955134 0.5345394 2.1239433 1.0619717 3.5500209 1.7750105 [589] 75.4973971 37.7486986 18.8743493 9.4371746 4.7185873 2.7772737 [595] 4.9584565 12.5018466 6.2509233 3.1254617 2.3461412 1.1730706 [601] 0.9025225 1.5924458 0.7962229 0.3981115 0.3138816 2.1055406 [607] 1.0527703 0.5263852 0.4953579 0.2476789 0.1928680 0.5655874 [613] 25.1858571 12.5929285 36.0055471 18.0027736 9.0013868 4.5006934 [619] 2.2503467 3.2164331 1.6082166 0.8041083 0.4020541 0.4747845 [625] 0.8395422 10.1386643 5.0693322 16.1229609 8.0614805 4.0307402 [631] 9.0593046 4.5296523 2.2648262 1.1324131 51.2285093 25.6142546 [637] 12.8071273 6.4035637 3.2017818 1.6008909 1.4940282 0.7470141 [643] 1.8063552 1.9174717 1.1153361 1.0579659 1.0688042 0.5344021 [649] 2.3759430 2.2367600 6.3013858 8.1148623 7.1932722 5.7503517 [655] 2.8751759 1.4375879 2.4270307 1.2135153 0.6067577 0.3033788 [661] 0.3196184 0.5931406 0.4294120 0.5290608 3.2749146 1.6374573 [667] 2.0431059 1.4993266 0.9876977 0.8844457 1.3186257 0.8606594 [673] 0.4303297 0.5255806 0.2627903 1.5710964 9.8893935 4.9446967 [679] 2.4723484 1.2361742 0.6180871 2.7429834 1.3714917 1.3152089 [685] 2.1784742 1.0892371 24.1278609 12.0639304 6.0319652 3.0159826 [691] 32.1580320 16.0790160 8.0395080 4.0197540 2.0098770 1.0049385 [697] 9.0043877 4.5021938 2.2510969 1.6375586 1.4187151 0.9877362 [703] 0.4938681 1.0420386 0.7016585 0.4591192 0.8364346 0.5179720 [709] 0.4158840 1.1323980 0.8711167 0.4355584 1.2566852 0.9168554 [715] 6.7075335 3.3537668 1.6768834 22.9640420 11.4820210 5.7410105 [721] 5.2089240 2.6044620 1.3022310 0.9450712 1.1443159 0.5721579 [727] 1.0343420 1.5001485 0.9352824 0.4676412 0.6780070 0.3390035 [733] 2.0283782 1.0141891 0.5070945 0.2535473 0.5768156 0.2884078 [739] 0.6071565 2.0121104 4.4727316 2.2363658 1.1181829 0.5590915 [745] 5.1126898 2.5563449 2.5930351 1.2965175 0.6482588 9.3366266 [751] 4.6683133 2.3341566 1.9212210 0.9606105 0.5808620 0.8003445 [757] 0.6359513 0.8961568 3.2568594 8.3548426 9.1544264 4.5772132 [763] 2.2886066 16.8149770 8.4074885 4.2037443 2.1018721 1.0509361 [769] 1.0866720 1.8832020 1.7114160 0.8557080 2.6730501 2.5007304 [775] 1.2503652 1.5741425 0.9508938 0.6666507 1.2978973 1.0958147 [781] 1.5646941 0.7823470 0.7906196 1.0505038 0.5252519 11.8178464 [787] 5.9089232 3.2673750 1.6336875 0.8168437 0.5569769 0.3220555 [793] 0.9279492 0.8784919 0.8922833 0.9376574 0.6940549 0.4071297 [799] 12.0229544 6.0114772 3.0057386 1.5028693 0.7514346 0.6652745 [805] 0.4859539 1.2352947 0.6176474 0.7695003 1.7605155 0.9264817 [811] 2.5185703 1.2592852 0.6792174 2.3140013 3.4450050 4.4547436 [817] 2.2273718 2.6094924 1.4168877 2.3276407 2.0758506 1.2286446 [823] 0.6143223 0.6126952 0.3249566 0.1624783 0.1868370 0.2383081 [829] 0.5768320 1.3988104 0.6994052 0.3497026 4.0116198 2.0058099 [835] 1.8242497 0.9121249 0.4560624 0.6776526 0.3388263 0.4040101 [841] 1.3940372 0.6970186 0.3485093 1.6585026 1.0327975 1.3140772 [847] 0.6570386 0.4790494 11.5365826 92.2022832 46.1011416 23.0505708 [853] 11.5252854 5.7626427 2.8813214 2.2588218 1.1294109 0.5647055 [859] 2.6364149 1.3182075 15.2221778 7.6110889 3.8055444 1.9027722 [865] 1.8120018 0.9060009 12.8816532 6.4408266 3.6917544 3.9679913 [871] 1.9839956 0.9919978 0.7132151 0.3566075 1.3246312 1.9479973 [877] 2.2764086 24.6388658 12.3194329 6.1597165 3.0798582 1.5907264 [883] 0.7953632 0.5490406 0.7444932 7.0739039 3.5369520 1.7684760 [889] 8.4224242 4.2112121 2.1056060 1.0528030 0.5752064 0.7748364 [895] 1.3194755 1.6594606 0.8297303 0.6593238 0.5129088 0.8683734 [901] 0.4341867 0.2699834 0.4996126 0.4267722 0.5927370 0.4676072 [907] 1.5287668 0.7643834 0.7259368 0.4427511 2.0965736 1.9869931 [913] 2.7677997 1.3838999 1.9757338 0.9878669 0.6863520 1.3871534 [919] 3.5831107 1.7915553 0.8957777 1.2022312 0.6011156 1.1661156 [925] 0.5830578 1.2482302 0.6241151 0.3120576 1.1585946 0.5792973 [931] 0.2896487 0.1469676 5.8151111 3.5045545 7.0238323 3.5119162 [937] 68.2107216 34.1053608 17.0526804 8.5263402 4.2631701 2.1315851 [943] 1.0657925 0.5702924 1.7672464 32.2947737 16.1473868 8.0736934 [949] 6.7112112 3.3556056 2.1985481 1.0992740 0.5496370 0.8041888 [955] 0.6462312 1.2755260 0.6377630 0.3188815 0.5891383 0.5381206 [961] 1.1264439 0.5632219 1.0106370 0.5053185 0.2526593 0.5272479 [967] 0.7258260 1.3624888 0.6812444 2.9396340 1.4698170 852.6909653 [973] 426.3454827 213.1727413 106.5863707 53.2931853 26.6465927 17.2963137 [979] 8.6481569 9.3868052 4.6934026 2.3467013 278.4119283 139.2059641 [985] 69.6029821 34.8014910 17.4007455 8.7003728 4.3501864 2.1750932 [991] 1.0875466 2.6344633 2.8761669 1.4380835 0.7190417 0.3595209 [997] 0.2339394 0.3068913 2.0069070 1.0034535 attr(,"control") model theta max 0.5 Time Series: Start = 1 End = 1000 Frequency = 1 [1] 4.11760913 1.79505353 1.79505353 0.87323968 0.82855007 0.82855007 [7] 0.45734240 0.26212389 0.19447961 0.70170518 0.70170518 0.71734801 [13] 0.71734801 0.63577623 0.63577623 1.21080224 1.21080224 0.47265524 [19] 0.16412578 0.39784639 0.87041688 0.87041688 0.53697953 1.86006253 [25] 1.86342298 2.25636228 2.25636228 0.37038541 0.37038541 0.15059873 [31] 0.68784367 2.45104607 2.45104607 8.58483884 8.58483884 5.17131417 [37] 2.71988610 2.02952047 1.78206924 1.78206924 1.17176224 1.93896250 [43] 1.93896250 0.31664260 0.35775069 1.68320912 1.68320912 0.68296388 [49] 1.98463890 1.98463890 0.35182974 0.35182974 1.52023242 1.52023242 [55] 2.20498808 2.20498808 1.82863667 1.31252787 0.87898275 1.12048015 [61] 1.12048015 0.93004365 0.93004365 0.72973417 1.86449081 1.86449081 [67] 2.25124223 2.25124223 1.04707789 3.20523004 3.20523004 2.23266358 [73] 0.38090107 2.13654598 2.13654598 4.93997072 4.93997072 0.95979714 [79] 0.95979714 2.57926785 2.57926785 1.68423115 1.68423115 2.10519807 [85] 2.28871152 2.28871152 0.53811929 0.53811929 1.05109658 1.05109658 [91] 0.22770208 0.22770208 1.12290038 1.34030227 2.08554321 2.08554321 [97] 1.02609647 3.43272264 3.43272264 0.27854896 1.13958723 1.13958723 [103] 0.83072109 1.11091816 1.11091816 2.37891976 2.37891976 0.56071645 [109] 3.28469759 3.28469759 0.74259389 0.74259389 1.04127754 1.04127754 [115] 0.35788284 0.94978791 0.94978791 0.87430324 2.88669782 2.88669782 [121] 1.89748741 1.89748741 1.23526431 3.22462640 3.22462640 0.75153992 [127] 0.04627107 0.12084820 0.60412771 0.60412771 1.03470678 1.03470678 [133] 1.84151087 1.84151087 0.64493615 0.55487212 0.80058631 0.80058631 [139] 0.70590523 0.83747839 2.14149916 2.14149916 1.30886783 1.23741837 [145] 1.12047351 1.12047351 0.63920300 0.63920300 0.59559911 0.22342168 [151] 0.64109942 0.64109942 1.12103882 1.12103882 2.34782307 2.34782307 [157] 1.02692036 3.15371688 3.15371688 0.70216063 0.60852311 2.11979902 [163] 2.56824982 2.56824982 0.28902646 0.28902646 0.27032472 0.27032472 [169] 0.42326961 1.19910239 1.19910239 0.80769268 0.07913937 1.32517732 [175] 1.32517732 5.69914169 5.69914169 2.21853762 2.21853762 0.67967202 [181] 2.32344908 2.32344908 0.83531336 1.16488800 1.16488800 1.12023206 [187] 0.90455688 0.90455688 0.57244255 1.56815252 1.56815252 1.19356636 [193] 0.68588941 0.39403352 0.39403352 0.29266741 0.25789856 0.84644020 [199] 3.20092304 3.20092304 1.67834201 0.96099482 0.02080770 0.93299602 [205] 5.72931017 5.72931017 1.16859009 3.90803759 6.46038589 6.46038589 [211] 0.85469510 1.42892028 1.42892028 0.67273679 2.63717453 2.63717453 [217] 0.94120990 0.92882094 0.92882094 0.41777952 0.33782499 0.33357542 [223] 0.58428673 0.58428673 2.64556514 2.64556514 4.05944802 4.05944802 [229] 0.21762017 0.12508518 1.58463925 1.58463925 1.77446939 1.77446939 [235] 1.84549012 2.68199636 2.68199636 1.76823587 1.76823587 0.60193881 [241] 0.26077276 0.25244630 2.21148084 2.21148084 1.29354322 2.92803433 [247] 2.92803433 0.74876735 5.21448207 5.21448207 1.98653502 1.98653502 [253] 1.55340950 1.08846525 1.08846525 0.99888899 5.45149110 5.45149110 [259] 2.33519986 1.83431672 0.87973337 0.93667954 1.00190929 1.36140653 [265] 1.36140653 1.06116109 0.94937967 0.90042163 0.28767829 1.36902357 [271] 1.36902357 1.37820885 1.80233526 1.80233526 0.64571689 1.33634265 [277] 1.33634265 0.63930653 0.46607086 1.59479433 1.59479433 0.58885776 [283] 0.35955568 0.35955568 1.38049016 1.38049016 0.54163904 1.03963209 [289] 1.47605087 1.47605087 1.39534430 0.87176648 0.87176648 0.55605518 [295] 0.59993716 2.79692706 2.79692706 0.08948000 0.49040048 1.15358894 [301] 1.15358894 0.45330600 0.45330600 2.50937744 2.50937744 1.75289000 [307] 1.75289000 0.84798079 0.23890017 0.17524907 0.74405031 3.32002379 [313] 3.40952142 3.40952142 0.48428386 0.71246475 0.71246475 0.64419523 [319] 0.64419523 0.37310568 0.83085601 0.84172487 0.84172487 0.81595064 [325] 1.67795835 2.74164693 2.74164693 2.55163667 2.55163667 0.67643158 [331] 1.56875411 2.12817177 2.12817177 1.33859559 1.33859559 1.86764800 [337] 1.86764800 1.36509972 1.36509972 0.21913371 0.23372050 0.23372050 [343] 3.41203397 4.33862675 4.33862675 2.62345097 2.62345097 1.03947369 [349] 1.05832973 1.05832973 2.45021487 2.45021487 0.87589719 0.41442774 [355] 0.45529984 0.47849590 0.98668294 0.98668294 2.05990663 2.05990663 [361] 1.66412850 1.78231933 1.78231933 0.68958432 0.96257427 0.96257427 [367] 0.36194974 1.79064870 1.79064870 1.63107391 1.63107391 0.63210668 [373] 0.63210668 0.81825153 0.81825153 1.93850376 1.93850376 1.72442729 [379] 1.72442729 1.17647178 3.00796872 3.00796872 1.14969781 0.55153231 [385] 1.21690153 1.49200859 1.49200859 0.49946101 0.90459192 0.90459192 [391] 0.63991651 0.63991651 1.37499261 2.13494875 2.13494875 0.09871340 [397] 0.66065866 1.08013242 1.08013242 0.66338798 0.66338798 1.50474779 [403] 3.23371082 3.23371082 0.70634685 0.33590349 0.90696043 0.90696043 [409] 0.36330414 0.47394298 1.24563202 1.24563202 1.52773359 1.52773359 [415] 0.26060687 0.36532743 0.44532943 0.74912466 0.74912466 2.11341419 [421] 2.11341419 2.70562491 2.70562491 2.65346443 2.65346443 1.29012329 [427] 1.29012329 2.04803978 2.04803978 0.13352358 1.12932700 1.12932700 [433] 0.97198451 1.31351684 1.48848876 1.48848876 0.99619658 0.99619658 [439] 0.97765164 0.42798778 0.30004768 2.19078604 2.19078604 1.39915607 [445] 1.27284597 0.72784433 0.72784433 0.42778287 3.34422199 3.34422199 [451] 1.37486505 2.56321866 2.56321866 0.83743913 1.07300717 1.43897419 [457] 1.43897419 0.82240295 0.96995090 0.96995090 0.64370219 1.48522211 [463] 1.48522211 0.79494842 1.79677257 1.79677257 1.13919059 0.33776528 [469] 0.38935960 0.38935960 0.33352884 0.28147734 1.67758183 2.44905430 [475] 2.44905430 1.36722469 1.36722469 3.16498058 3.16498058 0.40585234 [481] 1.47255816 3.94476650 3.94476650 3.23949763 3.23949763 0.60045307 [487] 0.83889948 0.92408332 0.92408332 0.86690894 2.85295352 2.85295352 [493] 1.03279711 1.72501558 1.72501558 1.80820026 1.91995829 1.91995829 [499] 0.44968109 0.25064269 0.55724189 0.55724189 1.53236794 2.63455885 [505] 2.63455885 0.42721871 1.78209787 1.78209787 1.17350423 0.66504396 [511] 0.81299868 0.86712280 3.01225302 3.01225302 0.72333417 0.72333417 [517] 0.68016275 0.67825835 2.62889654 2.62889654 1.03774279 1.66415413 [523] 1.66415413 1.37504317 0.92620494 1.11829502 1.11829502 1.06064272 [529] 1.06064272 1.10411511 1.18374461 1.49148431 3.55200072 3.55200072 [535] 1.39520531 1.11843617 2.00593139 2.00593139 0.95563846 3.28184412 [541] 3.28184412 3.23624687 3.23624687 0.51470949 0.69172071 0.69172071 [547] 0.63901119 1.05896192 1.05896192 1.04656841 1.04656841 0.42892005 [553] 0.43822410 0.69223840 0.76800126 1.11788698 1.11788698 1.02852511 [559] 0.25652225 0.54521370 0.54521370 0.10190359 0.99260340 3.24622770 [565] 3.24622770 0.44424469 0.44424469 0.38397328 0.66730199 0.66730199 [571] 0.68213148 0.68213148 1.79913869 1.79913869 0.98747596 0.98747596 [577] 0.62144623 0.62144623 0.60208874 5.48503700 5.48503700 1.14293630 [583] 1.47675481 1.47675481 0.84438163 2.02697642 2.02697642 1.45465724 [589] 1.45465724 0.73605781 3.32051481 3.32051481 1.20604548 0.74456358 [595] 1.20044314 1.20044314 0.89627160 1.53030110 1.53030110 1.77601814 [601] 1.77601814 1.17645672 2.01914787 2.01914787 0.27970810 0.78621732 [607] 0.78621732 0.61482654 2.18118594 2.18118594 0.36893266 1.01064761 [613] 3.08869560 3.08869560 0.64676933 0.54921964 0.15353736 0.61339772 [619] 0.61339772 1.02981106 1.02981106 0.62433203 0.82923649 2.54981171 [625] 2.54981171 1.85431957 1.36563318 1.36563318 0.76187002 1.32991444 [631] 2.51407965 2.51407965 1.48682365 1.48682365 2.07232185 2.07232185 [637] 0.77500542 0.65979851 1.12786779 1.12786779 0.54457302 0.54457302 [643] 0.10084682 0.20806237 0.87316780 1.41064882 1.41064882 0.29584265 [649] 1.48965675 1.48965675 0.46447671 0.46447671 1.29587553 1.29587553 [655] 0.79367312 1.28297207 1.28297207 0.77850512 0.63932522 0.87368430 [661] 0.87368430 0.52502211 1.64166807 1.64166807 0.29968221 1.54913920 [667] 1.54913920 0.95172370 0.95172370 0.67534547 0.38892297 0.43625097 [673] 0.43625097 0.93941106 2.01557589 2.01557589 1.49708109 0.50179174 [679] 0.63699628 0.98780853 0.98780853 1.00190217 1.00190217 0.79092033 [685] 1.73151542 1.73151542 1.34900357 1.34900357 0.68166599 0.33224327 [691] 0.33293603 0.33293603 0.20530455 0.47011041 0.74675905 0.74675905 [697] 0.14314602 2.23785353 2.23785353 3.29241746 3.29241746 0.27460076 [703] 1.70354718 1.70354718 0.31325645 1.48967190 2.15676527 2.15676527 [709] 1.26316773 1.26316773 0.13426111 0.78448229 0.78448229 0.15258115 [715] 1.35026062 1.35026062 1.64315831 1.77403818 5.42004816 5.42004816 [721] 1.78772512 2.09976555 2.09976555 1.06704947 0.39018246 0.99577973 [727] 0.99577973 0.90127437 0.59980849 0.60883806 0.60883806 0.57913238 [733] 0.98033392 0.98033392 1.64749983 1.64749983 1.19502490 1.19502490 [739] 0.52663843 0.53619112 0.53619112 0.86021994 0.86021994 3.62389322 [745] 3.62389322 1.31210762 1.63231591 3.69494673 3.69494673 0.45678760 [751] 0.86027933 0.86027933 1.50139198 1.50139198 3.00420768 3.00420768 [757] 3.78569515 4.24033951 4.24033951 2.05750681 1.33745256 1.50739563 [763] 1.50739563 0.85290166 1.29015984 1.89766636 1.89766636 1.19004688 [769] 0.97729705 0.97729705 3.01578610 3.01578610 0.88850628 1.34603191 [775] 3.23892505 3.23892505 4.57110061 4.57110061 2.08503605 1.21611263 [781] 1.21611263 0.32553946 0.07836508 0.35973053 2.30758281 2.30758281 [787] 0.79038830 0.37466578 5.28164576 5.28164576 0.92303433 2.23887689 [793] 2.23887689 2.10585215 0.21335451 1.00127431 1.00127431 0.59344017 [799] 0.68880193 0.68880193 0.47091859 0.46376019 0.50021977 0.94318011 [805] 0.94318011 0.41880739 1.12879306 1.12879306 0.39509991 0.13838348 [811] 0.64047544 0.64047544 1.17338659 1.17338659 0.39093437 3.04043221 [817] 3.04043221 0.60517017 0.60517017 0.81595240 0.81595240 0.03598729 [823] 1.58518232 2.30710269 2.30710269 1.25420998 0.23412617 3.83203787 [829] 3.83203787 0.07398989 1.83400763 1.83400763 0.33517445 3.65734675 [835] 3.65734675 1.96390705 1.96390705 1.12415639 1.46049801 1.46049801 [841] 0.69810720 1.20595678 1.20595678 1.01442040 1.01442040 3.39851420 [847] 3.39851420 0.54836232 0.54836232 0.42303339 0.65972587 0.65972587 [853] 0.12718866 0.20589736 1.38695564 1.38695564 1.12068327 0.81158153 [859] 1.14349836 1.14349836 0.26598376 0.26598376 0.17757466 0.17757466 [865] 1.79416301 1.79416301 1.38127458 0.88547237 0.08761732 0.96838262 [871] 0.96838262 0.86978819 1.19393924 1.19393924 0.95254374 0.95254374 [877] 1.19423749 1.41827369 1.41827369 0.13471388 0.53652667 0.53652667 [883] 0.41877990 0.28529422 2.84898031 2.84898031 1.43013116 4.34800942 [889] 4.34800942 2.55909258 2.12164815 2.12164815 1.72116044 0.66906514 [895] 1.11901244 1.11901244 3.00604355 3.00604355 1.04409924 3.18916337 [901] 3.18916337 0.44089421 1.25168614 1.25168614 1.57244580 1.57244580 [907] 1.38593045 2.32324639 2.32324639 0.74133468 0.58779232 0.88931608 [913] 1.29125555 1.29125555 1.44398074 1.44398074 0.53731641 3.60272455 [919] 3.60272455 0.57375409 0.53581048 0.72357365 0.72357365 0.42841187 [925] 1.86339975 1.86339975 0.17336363 1.02329046 1.02329046 0.71736319 [931] 0.82524027 0.82524027 2.47267531 2.47267531 1.54819157 1.39733851 [937] 1.39733851 0.03832705 0.05964955 0.25284625 1.08307388 2.48831009 [943] 2.48831009 2.12844067 1.65891495 1.65891495 0.96330957 2.20150765 [949] 2.20150765 1.18953157 1.18953157 0.21155776 0.21155776 0.82523258 [955] 2.79336706 2.79336706 1.56168405 0.97853525 0.97853525 0.64493793 [961] 0.63354135 0.63354135 1.86884213 1.86884213 0.88265313 0.45833778 [967] 0.45833778 0.25792880 3.51531631 3.51531631 1.99058416 1.01513598 [973] 0.81412815 0.37723791 0.95798092 1.95701497 1.95701497 0.99241752 [979] 0.32290883 1.32756523 1.32756523 0.73915372 0.80490412 0.80490412 [985] 0.64007101 0.85280853 0.85280853 0.61983532 0.61983532 0.23843704 [991] 0.22587889 0.27458317 0.27458317 1.81328070 1.81328070 1.25059334 [997] 1.30197866 1.30197866 2.49603839 2.49603839 attr(,"control") model theta pair 0.5 done successfully. Executing test function test.emd ... done successfully. Executing test function test.laws ... [1] 2.674357 1.547742 1.158124 6.174013 4.950532 4.144262 3.583898 [8] 4.663700 4.764892 11.803328 11.064854 10.699831 9.897977 9.245721 [15] 8.901271 8.349668 7.880462 7.451348 7.088134 6.747916 6.599749 [22] 8.178503 7.903010 8.045013 7.726959 7.609565 7.338938 7.489077 [29] 7.310228 7.409908 7.219404 7.749952 7.518816 7.310775 7.105245 [36] 7.346005 7.180217 6.997162 6.835342 6.668668 6.511176 7.257413 [43] 7.108297 6.947328 6.895490 6.752813 6.722398 6.588282 6.515465 [50] 6.388182 6.263652 6.159437 6.224852 6.240475 6.135145 6.106719 [57] 6.067347 5.979519 5.879079 5.788326 5.696907 5.685401 5.610255 [64] 5.605829 5.563503 5.502886 5.446484 5.389372 5.313551 5.241493 [71] 5.178312 5.109557 5.041253 5.219792 5.230851 5.163120 7.652298 [78] 7.595116 7.506996 7.419675 7.332441 7.331545 7.252397 7.167383 [85] 7.085824 7.105515 7.035086 6.961689 6.889425 6.870494 6.807534 [92] 6.738318 6.669287 6.636381 6.567255 6.500423 6.438741 6.379113 [99] 6.331065 6.269787 6.226290 6.179438 6.170415 6.112891 6.072305 [106] 6.017389 6.141988 6.086627 6.036384 7.174683 7.116545 7.053593 [113] 7.026333 6.968396 7.096282 7.039705 6.984697 6.927130 6.902112 [120] 6.849659 6.961638 6.909354 6.854545 6.830313 6.780337 6.726544 [127] 6.680400 6.633993 6.604415 7.335967 7.442258 7.388790 7.346271 [134] 7.399263 7.395021 7.361144 7.343595 7.291793 7.245592 7.193861 [141] 7.209153 7.159227 7.122780 7.088372 7.041511 6.995914 6.956030 [148] 6.917398 6.878133 6.885457 6.863507 6.822818 6.832131 6.792785 [155] 6.754597 6.711857 6.672745 6.652882 6.612987 6.575525 6.538921 [162] 6.498892 6.486453 6.458180 6.420945 6.405758 6.369597 6.374518 [169] 6.345328 6.310531 6.303917 6.269781 6.306529 6.300205 6.272910 [176] 6.239749 6.208852 6.181434 6.151693 6.122843 6.095245 6.062424 [183] 6.056875 6.026538 5.998614 5.969588 5.938416 5.910198 5.892959 [190] 5.889817 5.859601 5.831234 5.801025 5.893989 5.866346 5.843580 [197] 5.855803 5.827734 5.801388 5.851591 5.835433 5.822977 5.796258 [204] 5.771886 5.762123 5.742046 5.716150 5.691170 5.688928 5.662191 [211] 5.644203 5.618099 5.607898 5.586321 5.561847 5.556209 5.950041 [218] 5.924306 5.911449 5.890992 5.864343 5.850772 5.825938 5.803397 [225] 5.786297 5.763758 5.738753 5.728036 5.870407 5.845233 5.821297 [232] 5.799677 5.783210 5.788599 5.764283 5.749621 5.729723 5.740772 [239] 5.747213 5.730519 5.707311 5.820029 5.840374 5.874706 5.903407 [246] 5.922331 5.898631 5.876243 5.853876 5.854443 5.831369 5.812319 [253] 5.822045 5.800184 5.791332 5.771122 5.754128 5.736262 5.716353 [260] 5.701530 5.770651 5.748870 5.731958 5.711690 5.690588 5.669394 [267] 5.648881 5.671731 5.658650 5.638068 5.622005 5.603274 5.584017 [274] 5.569696 5.551486 5.542422 5.524252 5.511769 5.492511 5.482814 [281] 5.465070 5.447432 5.434474 5.425027 5.406218 5.390038 5.373409 [288] 5.361128 5.348589 5.330393 5.314199 5.296294 5.278338 5.265659 [295] 5.250202 5.233472 5.254492 5.267360 5.250162 5.675949 5.663334 [302] 5.655917 5.637485 5.629842 5.621839 5.606616 5.591874 5.575210 [309] 5.565524 5.549419 5.611636 5.596412 5.581183 5.566669 5.594795 [316] 5.579337 5.583992 5.573734 5.566091 5.548922 5.534480 5.517374 [323] 5.576331 5.565018 5.578935 5.640304 5.625432 5.610824 5.598721 [330] 5.591016 5.578412 5.571635 5.561772 5.548030 5.532929 5.522093 [337] 5.508697 5.494227 5.478991 5.464054 5.450106 5.442511 5.434432 [344] 5.423579 5.412196 5.400016 5.429246 5.415090 5.400748 5.386063 [351] 5.370972 5.358019 5.345931 5.331987 5.321568 5.307435 5.294038 [358] 5.279927 5.281748 5.267843 5.295707 5.282007 5.272602 5.265011 [365] 5.251896 5.240871 5.227247 5.214233 5.203252 5.199940 5.187276 [372] 5.173576 5.161252 5.147887 5.165120 5.170163 5.157998 5.145661 [379] 5.134799 5.122891 5.126429 5.114390 5.449535 5.462639 5.453600 [386] 5.440991 5.430365 5.427309 5.420561 5.407135 5.393310 5.415587 [393] 5.409285 5.885920 5.878794 5.864169 5.850453 6.018827 6.004551 [400] 5.990857 5.977028 5.963597 5.956413 5.941899 5.927816 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-1.576097e-01 -1.605554e-01 -1.632983e-01 [411] -1.653159e-01 -1.670847e-01 -1.699932e-01 -1.729438e-01 -1.755074e-01 [416] -1.771743e-01 -1.795522e-01 -1.823214e-01 -1.851932e-01 -1.880568e-01 [421] -1.908042e-01 -1.931423e-01 -1.956711e-01 -1.985532e-01 -1.936539e-01 [426] -1.962255e-01 -1.990430e-01 -2.016054e-01 -2.020937e-01 -2.043541e-01 [431] -1.921773e-01 -1.950114e-01 -1.978562e-01 -2.001832e-01 -2.017410e-01 [436] -2.039230e-01 -2.063895e-01 -2.090119e-01 -2.110555e-01 -2.138107e-01 [441] -2.149135e-01 -2.174019e-01 -2.148280e-01 -2.175989e-01 -2.171430e-01 [446] -2.199091e-01 -2.006207e-01 -2.029206e-01 -2.056939e-01 -2.083501e-01 [451] -2.110412e-01 -2.138191e-01 -2.164846e-01 -2.191382e-01 -2.219073e-01 [456] -2.246115e-01 -2.264558e-01 -2.291996e-01 -2.319158e-01 -2.341587e-01 [461] -2.368305e-01 -2.392975e-01 -2.414135e-01 -2.430501e-01 -2.450512e-01 [466] -2.475394e-01 -2.498109e-01 -2.487967e-01 -2.514714e-01 -2.534021e-01 [471] -2.544996e-01 -2.571304e-01 -2.587974e-01 -2.613951e-01 -2.638615e-01 [476] -2.664281e-01 -2.686332e-01 -2.707478e-01 -2.706757e-01 -2.731176e-01 [481] -2.752070e-01 -2.773080e-01 -2.798898e-01 -2.823177e-01 -2.848731e-01 [486] -2.873201e-01 -2.899209e-01 4.013139e-01 3.981495e-01 3.951190e-01 [491] 3.917997e-01 3.982649e-01 3.982029e-01 3.952051e-01 3.928473e-01 [496] 3.895327e-01 3.862531e-01 3.831979e-01 3.799110e-01 3.769533e-01 [501] 3.741360e-01 3.723199e-01 3.697886e-01 3.690664e-01 3.677945e-01 [506] 3.647258e-01 3.615483e-01 3.583754e-01 3.557602e-01 3.528130e-01 [511] 3.517798e-01 3.492384e-01 3.462334e-01 3.431233e-01 3.401374e-01 [516] 3.403848e-01 3.374194e-01 3.355056e-01 3.324532e-01 3.301552e-01 [521] 3.270062e-01 3.245085e-01 3.266663e-01 3.296342e-01 3.270554e-01 [526] 3.275960e-01 3.246604e-01 3.274452e-01 3.245542e-01 3.226708e-01 [531] 3.215643e-01 3.191637e-01 3.161714e-01 3.134504e-01 3.104666e-01 [536] 3.076747e-01 3.046159e-01 3.015617e-01 2.986794e-01 2.957954e-01 [541] 2.937374e-01 2.922853e-01 3.602447e-01 3.579094e-01 3.549477e-01 [546] 3.519083e-01 3.488684e-01 3.458658e-01 3.438883e-01 3.408590e-01 [551] 3.380157e-01 3.352996e-01 3.330041e-01 3.304019e-01 3.285066e-01 [556] 3.259302e-01 3.229238e-01 3.210219e-01 3.195216e-01 3.174574e-01 [561] 3.144596e-01 3.115972e-01 3.087090e-01 3.064053e-01 3.243534e-01 [566] 3.215243e-01 3.186320e-01 3.176560e-01 3.168088e-01 3.196879e-01 [571] 3.541786e-01 3.515281e-01 3.485204e-01 3.754316e-01 3.727917e-01 [576] 3.698611e-01 3.678166e-01 3.654703e-01 3.625517e-01 3.597647e-01 [581] 3.569005e-01 3.539631e-01 3.521792e-01 3.503124e-01 3.473822e-01 [586] 3.445358e-01 3.421373e-01 3.397838e-01 3.370617e-01 3.358950e-01 [591] 3.413782e-01 3.391417e-01 3.385377e-01 3.358052e-01 3.368430e-01 [596] 3.339946e-01 3.311150e-01 3.291480e-01 3.264927e-01 3.235977e-01 [601] 3.208436e-01 3.195578e-01 3.171312e-01 3.145195e-01 3.146675e-01 [606] 3.154245e-01 3.125635e-01 3.140859e-01 3.115142e-01 3.129534e-01 [611] 3.101838e-01 3.080146e-01 3.051887e-01 3.023719e-01 2.999111e-01 [616] 2.974461e-01 2.948390e-01 3.196544e-01 3.172182e-01 3.170995e-01 [621] 3.149077e-01 3.121111e-01 3.109526e-01 3.083985e-01 3.055807e-01 [626] 3.028348e-01 3.001559e-01 2.978531e-01 2.959221e-01 2.948153e-01 [631] 2.958691e-01 3.002906e-01 2.975526e-01 2.955936e-01 2.932144e-01 [636] 2.907444e-01 2.880973e-01 2.854871e-01 2.831752e-01 2.816777e-01 [641] 2.791885e-01 2.767116e-01 2.753643e-01 2.726445e-01 2.717088e-01 [646] 2.690936e-01 2.672012e-01 2.658648e-01 2.880013e-01 2.852630e-01 [651] 2.848873e-01 2.822878e-01 2.798387e-01 2.772320e-01 2.746264e-01 [656] 2.790562e-01 2.765529e-01 2.741141e-01 2.729948e-01 2.704268e-01 [661] 2.679239e-01 2.653322e-01 2.637797e-01 2.611348e-01 2.638438e-01 [666] 2.613318e-01 2.586863e-01 2.602781e-01 2.580777e-01 2.559778e-01 [671] 2.542213e-01 2.520688e-01 2.494657e-01 2.470329e-01 2.455212e-01 [676] 2.438712e-01 2.415734e-01 2.397199e-01 2.375577e-01 2.350130e-01 [681] 2.329233e-01 2.309979e-01 2.283830e-01 2.259194e-01 2.234509e-01 [686] 2.211617e-01 2.185863e-01 2.162739e-01 2.288701e-01 2.263434e-01 [691] 2.238291e-01 2.215654e-01 2.190244e-01 2.164517e-01 2.141667e-01 [696] 2.119434e-01 2.094688e-01 2.087640e-01 2.063551e-01 2.039961e-01 [701] 2.049286e-01 2.026804e-01 2.003624e-01 1.981816e-01 1.973402e-01 [706] 1.948409e-01 1.924811e-01 1.900245e-01 1.876747e-01 1.852986e-01 [711] 1.828537e-01 1.820840e-01 1.828631e-01 1.804927e-01 1.781485e-01 [716] 1.800563e-01 1.776564e-01 1.753114e-01 1.747073e-01 1.787585e-01 [721] 1.763903e-01 1.751831e-01 1.736114e-01 1.711795e-01 1.744834e-01 [726] 1.720386e-01 1.732815e-01 1.729203e-01 1.710183e-01 1.685628e-01 [731] 1.661487e-01 1.636896e-01 1.767893e-01 1.744843e-01 1.720935e-01 [736] 1.703240e-01 1.699443e-01 1.679747e-01 1.656416e-01 1.636206e-01 [741] 1.623726e-01 1.599282e-01 1.605694e-01 1.581938e-01 1.567119e-01 [746] 1.544666e-01 1.522233e-01 1.505256e-01 1.485078e-01 1.461942e-01 [751] 1.438883e-01 1.420932e-01 1.412097e-01 1.391378e-01 1.367862e-01 [756] 1.347363e-01 1.326180e-01 1.305065e-01 1.285796e-01 1.262211e-01 [761] 1.242594e-01 1.219070e-01 1.195263e-01 1.173845e-01 1.152651e-01 [766] 1.141312e-01 1.121458e-01 1.099652e-01 1.079367e-01 1.056079e-01 [771] 1.037859e-01 1.017317e-01 1.009280e-01 9.859320e-02 9.701382e-02 [776] 9.499961e-02 9.542997e-02 9.313023e-02 9.081916e-02 8.851783e-02 [781] 8.818258e-02 8.602450e-02 8.372459e-02 8.176959e-02 8.073835e-02 [786] 8.198158e-02 8.135821e-02 7.907748e-02 7.812993e-02 7.813558e-02 [791] 7.666284e-02 7.556696e-02 7.358302e-02 7.186816e-02 6.983434e-02 [796] 6.755692e-02 6.733911e-02 6.531185e-02 6.642299e-02 6.486528e-02 [801] 6.263864e-02 6.626790e-02 6.420072e-02 6.311283e-02 6.121872e-02 [806] 5.898349e-02 5.708992e-02 5.502387e-02 5.285229e-02 5.072483e-02 [811] 4.879793e-02 4.696242e-02 4.471620e-02 4.301413e-02 4.118138e-02 [816] 3.900191e-02 3.704818e-02 3.491188e-02 3.284349e-02 3.088954e-02 [821] 2.886552e-02 3.567545e-02 3.350143e-02 4.501877e-02 4.331975e-02 [826] 4.116240e-02 3.929855e-02 3.736855e-02 4.135657e-02 3.953528e-02 [831] 3.748802e-02 3.541390e-02 3.433064e-02 3.219007e-02 3.374123e-02 [836] 3.195484e-02 2.987360e-02 2.774468e-02 2.636011e-02 2.853628e-02 [841] 2.725706e-02 2.530763e-02 2.348817e-02 1.569104e-01 1.555482e-01 [846] 1.535935e-01 1.514061e-01 1.491853e-01 1.470548e-01 1.457789e-01 [851] 1.437159e-01 1.414635e-01 1.395988e-01 1.374042e-01 1.365932e-01 [856] 1.344372e-01 1.328231e-01 1.308923e-01 1.299123e-01 1.277051e-01 [861] 1.258670e-01 1.239480e-01 1.222717e-01 1.200499e-01 1.192659e-01 [866] 1.180125e-01 1.160502e-01 1.147747e-01 1.140210e-01 1.121342e-01 [871] 1.107997e-01 1.088249e-01 1.068878e-01 1.049551e-01 1.027711e-01 [876] 1.006347e-01 1.018949e-01 9.991312e-02 9.776894e-02 9.626558e-02 [881] 9.788990e-02 9.608077e-02 9.395538e-02 9.232843e-02 9.265819e-02 [886] 9.055406e-02 8.839369e-02 8.626036e-02 8.747013e-02 8.532428e-02 [891] 8.340673e-02 8.132588e-02 7.935293e-02 7.733870e-02 7.621051e-02 [896] 7.418388e-02 7.232072e-02 7.032752e-02 6.822115e-02 6.700270e-02 [901] 6.962216e-02 6.833181e-02 6.633246e-02 6.455445e-02 6.260117e-02 [906] 6.056987e-02 5.932185e-02 5.734451e-02 6.602772e-02 6.426909e-02 [911] 6.275776e-02 6.157030e-02 5.966287e-02 5.834725e-02 5.635684e-02 [916] 5.429496e-02 5.310859e-02 5.111228e-02 4.972149e-02 5.157443e-02 [921] 4.998945e-02 4.856646e-02 4.737582e-02 4.542114e-02 4.356671e-02 [926] 4.149446e-02 3.971494e-02 3.769265e-02 3.764413e-02 3.582449e-02 [931] 3.412614e-02 3.362279e-02 3.159255e-02 2.981622e-02 2.826377e-02 [936] 2.627339e-02 2.479260e-02 2.480981e-02 2.326668e-02 2.250050e-02 [941] 2.088095e-02 1.903752e-02 1.740047e-02 1.552590e-02 1.355473e-02 [946] 1.173862e-02 1.040079e-02 8.611378e-03 8.611361e-03 6.862974e-03 [951] 5.047315e-03 3.066568e-03 2.478649e-03 4.562662e-04 -1.218328e-03 [956] 1.881210e-02 1.681337e-02 1.480352e-02 1.278780e-02 1.076778e-02 [961] 9.817995e-03 7.805109e-03 6.067114e-03 1.371941e-02 1.180487e-02 [966] 9.793083e-03 7.767115e-03 6.101487e-03 4.109072e-03 3.433438e-03 [971] 1.628374e-03 -3.667717e-04 -2.170086e-03 -4.012779e-03 -5.369586e-03 [976] -7.240081e-03 -8.866905e-03 -9.817440e-03 -1.151958e-02 -1.318303e-02 [981] -1.217074e-02 -1.396585e-02 -1.584950e-02 -1.773120e-02 2.015927e-02 [986] 1.828113e-02 1.651656e-02 1.572744e-02 1.400859e-02 1.208458e-02 [991] 1.065101e-02 8.933936e-03 6.990213e-03 5.362854e-03 5.833233e-03 [996] 3.877439e-03 1.970423e-03 2.704699e-16 done successfully. Executing test function test.me ... done successfully. Executing test function test.msratio ... X1 X2 X3 X4 1 1.0000000 1.0000000 1.0000000 1.0000000 2 0.8639547 0.9758038 0.9961106 0.9993855 3 0.7697381 0.9570587 0.9932969 0.9989833 4 0.8593146 0.9836772 0.9979892 0.9997476 5 0.8573494 0.9836704 0.9979892 0.9997476 6 0.8534564 0.9836430 0.9979890 0.9997476 done successfully. Executing test function test.qqpareto ... done successfully. Executing test function test.records ... number record trial expected se 1 1 2.674357 1 1.000000 0.0000000 2 2 21.221681 4 2.083333 0.8122329 3 3 75.149256 10 2.928968 1.1743937 4 4 196.829765 77 4.927501 1.8153428 5 5 1586.461984 490 6.772641 2.2648942 subsample records 1 1 9 subsample records 1 1 7 2 2 7 3 3 9 4 4 2 5 5 7 6 6 3 7 7 2 8 8 3 9 9 5 10 10 1 11 11 8 12 12 3 13 13 8 14 14 7 15 15 4 16 16 2 17 17 5 18 18 6 19 19 6 20 20 4 21 21 4 22 22 8 23 23 6 24 24 5 25 25 5 26 26 4 27 27 5 28 28 4 29 29 3 30 30 3 31 31 8 32 32 4 33 33 8 34 34 5 35 35 7 36 36 6 37 37 6 38 38 5 39 39 5 40 40 5 41 41 5 42 42 7 43 43 4 44 44 7 45 45 5 46 46 6 47 47 7 48 48 8 49 49 7 50 50 4 done successfully. Executing test function test.xacf ... lag Heights Distances 1 0 1.00000000 1.000000000 2 1 -0.06878713 -0.086279002 3 2 0.03020309 -0.124990956 4 3 -0.03524356 -0.144240616 5 4 0.07750502 -0.008437123 6 5 0.03571879 0.208771345 7 6 -0.07663678 -0.091339275 8 7 -0.03539556 0.054017312 9 8 -0.07805110 0.064638481 10 9 0.02552284 0.097867764 11 10 -0.08483328 -0.252872941 12 11 -0.05437105 0.041187343 13 12 -0.07110239 0.189578055 14 13 -0.02211933 -0.041135317 15 14 -0.07828075 -0.037980541 16 15 -0.07617815 -0.156381329 done successfully. Executing test function test.gev ... Distribution Check for: gev Call: distCheck(fun = "gev", n = 2000, xi = 0, mu = 0, beta = 1) 1. Normalization Check: NORM 1 with absolute error < 5.7e-05 2. [p-pfun(qfun(p))]^2 Check: [,1] [,2] [,3] [,4] [,5] [,6] [,7] p 0.001 0.01 0.1 0.5 0.9 0.99 0.999 P 0.001 0.01 0.1 0.5 0.9 0.99 0.999 RMSE 5.893951e-18 3. r(2000) Check: MEAN VAR SAMPLE 0.442 1.16 X 0.5772157 with absolute error < 1.3e-05 X^2 1.978112 with absolute error < 0.00011 MEAN VAR EXACT 0.577 1.64 Distribution Check for: gev Call: distCheck(fun = "gev", n = 5000, xi = 0.3, mu = 0, beta = 2) 1. Normalization Check: NORM 1 with absolute error < 3.5e-05 2. [p-pfun(qfun(p))]^2 Check: [,1] [,2] [,3] [,4] [,5] [,6] [,7] p 0.001 0.01 0.1 0.5 0.9 0.99 0.999 P 0.001 0.01 0.1 0.5 0.9 0.99 0.999 RMSE 5.119805e-18 3. r(5000) Check: MEAN VAR SAMPLE 1.34 7.12 X 1.987036 with absolute error < 0.00011 X^2 27.64662 with absolute error < 5e-04 MEAN VAR EXACT 1.99 23.7 done successfully. Executing test function test.gevMoments ... $param xi1 xi2 xi3 xi4 xi5 xi6 xi7 xi8 xi9 xi10 xi11 xi12 xi13 -4.50 -4.25 -4.00 -3.75 -3.50 -3.25 -3.00 -2.75 -2.50 -2.25 -2.00 -1.75 -1.50 xi14 xi15 xi16 xi17 xi18 xi19 xi20 xi21 xi22 xi23 xi24 xi25 mu -1.25 -1.00 -0.75 -0.50 -0.25 0.00 0.25 0.50 0.75 1.00 1.25 1.50 0.00 beta 1.00 $mean [1] -11.4095062 -8.0497910 -5.7500000 -4.1563217 -3.0376367 -2.2415647 [7] -1.6666667 -1.2447231 -0.9293404 -0.6885587 -0.5000000 -0.3476340 [13] -0.2195603 -0.1064025 0.0000000 0.1079166 0.2275461 0.3743901 [19] 0.5772157 0.9016668 1.5449077 3.5008132 NaN NaN [25] NaN $var [1] 1.778470e+04 6.535784e+03 2.484000e+03 9.784395e+02 4.003839e+02 [6] 1.706615e+02 7.600000e+01 3.548065e+01 1.743285e+01 9.055618e+00 [11] 5.000000e+00 2.953439e+00 1.881269e+00 1.305379e+00 1.000000e+00 [16] 8.616257e-01 8.584073e-01 1.034584e+00 1.644934e+00 4.332924e+00 [21] NaN NaN NaN NaN NaN done successfully. Executing test function test.gevSlider ... done successfully. Executing test function test.hillPlot ... done successfully. Executing test function test.shaparmPlot ... done successfully. Executing test function test.gevFit ... Title: GEV Parameter Estimation Call: gevFit(x = x.ts, block = 1, type = "pwm") Estimation Type: gev pwm Estimated Parameters: xi mu beta -0.26386583 0.02432345 1.00141610 Description Thu Dec 21 20:46:46 2023 Title: GEV Parameter Estimation Call: gevFit(x = x.ts, block = 1, type = "mle") Estimation Type: gev mle Estimated Parameters: xi mu beta -0.25437393 0.01928077 0.99998030 Description Thu Dec 21 20:46:46 2023 Title: GEV Parameter Estimation Call: gevFit(x = as.vector(x.ts), block = 1, type = "pwm") Estimation Type: gev pwm Estimated Parameters: xi mu beta -0.26386583 0.02432345 1.00141610 Description Thu Dec 21 20:46:46 2023 Title: GEV Parameter Estimation Call: gevFit(x = as.vector(x.ts), block = 1, type = "mle") Estimation Type: gev mle Estimated Parameters: xi mu beta -0.25437393 0.01928077 0.99998030 Description Thu Dec 21 20:46:46 2023 Title: GEV Parameter Estimation Call: gevFit(x = as.timeSeries(x.ts), block = 1, type = "pwm") Estimation Type: gev pwm Estimated Parameters: xi mu beta -0.26386583 0.02432345 1.00141610 Description Thu Dec 21 20:46:46 2023 Title: GEV Parameter Estimation Call: gevFit(x = as.timeSeries(x.ts), block = 1, type = "mle") Estimation Type: gev mle Estimated Parameters: xi mu beta -0.25437393 0.01928077 0.99998030 Description Thu Dec 21 20:46:47 2023 done successfully. Executing test function test.gevFitByBlocks ... done successfully. Executing test function test.gevSim ... GEV [1,] 0.23876776 [2,] 0.80443343 [3,] 0.72305025 [4,] -1.35519094 [5,] 0.52844365 [6,] -0.92034932 [7,] -0.50093016 [8,] -1.93289519 [9,] -0.40928250 [10,] 1.58781946 [11,] -0.73372020 [12,] 0.62840100 [13,] 1.06442713 [14,] 0.54301146 [15,] -0.35310093 [16,] -0.96778131 [17,] 1.63527659 [18,] -0.95519832 [19,] 0.09926936 [20,] -1.02736295 [21,] -0.26326804 [22,] -1.28287852 [23,] 0.67134817 [24,] -1.30160898 [25,] 1.37269281 [26,] 2.53812625 [27,] 1.84802723 [28,] 1.69839507 [29,] 0.54500201 [30,] -0.39052012 [31,] -0.87401362 [32,] 2.63642695 [33,] 0.66467236 [34,] -0.58299901 [35,] 2.40892663 [36,] -0.53396489 [37,] 0.30157276 [38,] -0.23283875 [39,] -0.15262358 [40,] -0.23279428 [41,] 0.82927276 [42,] 0.43819920 [43,] 2.25762732 [44,] 3.02681939 [45,] 1.24738154 [46,] 1.00414062 [47,] 0.67509293 [48,] -1.01598015 [49,] -1.13481517 [50,] 2.70460618 GMT x 2007-01-01 0.23876776 2007-01-02 0.80443343 2007-01-03 0.72305025 2007-01-04 -1.35519094 2007-01-05 0.52844365 2007-01-06 -0.92034932 2007-01-07 -0.50093016 2007-01-08 -1.93289519 2007-01-09 -0.40928250 2007-01-10 1.58781946 2007-01-11 -0.73372020 2007-01-12 0.62840100 2007-01-13 1.06442713 2007-01-14 0.54301146 2007-01-15 -0.35310093 2007-01-16 -0.96778131 2007-01-17 1.63527659 2007-01-18 -0.95519832 2007-01-19 0.09926936 2007-01-20 -1.02736295 2007-01-21 -0.26326804 2007-01-22 -1.28287852 2007-01-23 0.67134817 2007-01-24 -1.30160898 2007-01-25 1.37269281 2007-01-26 2.53812625 2007-01-27 1.84802723 2007-01-28 1.69839507 2007-01-29 0.54500201 2007-01-30 -0.39052012 2007-01-31 -0.87401362 2007-02-01 2.63642695 2007-02-02 0.66467236 2007-02-03 -0.58299901 2007-02-04 2.40892663 2007-02-05 -0.53396489 2007-02-06 0.30157276 2007-02-07 -0.23283875 2007-02-08 -0.15262358 2007-02-09 -0.23279428 2007-02-10 0.82927276 2007-02-11 0.43819920 2007-02-12 2.25762732 2007-02-13 3.02681939 2007-02-14 1.24738154 2007-02-15 1.00414062 2007-02-16 0.67509293 2007-02-17 -1.01598015 2007-02-18 -1.13481517 2007-02-19 2.70460618 done successfully. Executing test function test.gumbelFit ... Title: Gumbel Parameter Estimation Call: gumbelFit(x = x.ts, block = 1, type = "pwm") Estimation Type: gum pwm Estimated Parameters: mu beta 0.05894647 0.92636766 Description Thu Dec 21 20:46:54 2023 Title: Gumbel Parameter Estimation Call: gumbelFit(x = x.ts, block = 1, type = "mle") Estimation Type: gum mle Estimated Parameters: mu beta 0.01736875 1.00323052 Description Thu Dec 21 20:46:54 2023 Title: Gumbel Parameter Estimation Call: gumbelFit(x = as.vector(x.ts), block = 1, type = "pwm") Estimation Type: gum pwm Estimated Parameters: mu beta 0.05894647 0.92636766 Description Thu Dec 21 20:46:54 2023 Title: Gumbel Parameter Estimation Call: gumbelFit(x = as.vector(x.ts), block = 1, type = "mle") Estimation Type: gum mle Estimated Parameters: mu beta 0.01736875 1.00323052 Description Thu Dec 21 20:46:54 2023 Title: Gumbel Parameter Estimation Call: gumbelFit(x = as.timeSeries(x.ts), block = 1, type = "pwm") Estimation Type: gum pwm Estimated Parameters: mu beta 0.05894647 0.92636766 Description Thu Dec 21 20:46:54 2023 Title: Gumbel Parameter Estimation Call: gumbelFit(x = as.timeSeries(x.ts), block = 1, type = "mle") Estimation Type: gum mle Estimated Parameters: mu beta 0.01736875 1.00323052 Description Thu Dec 21 20:46:54 2023 done successfully. Executing test function test.gumbelSim ... GUMBEL [1,] 0.2461909 [2,] 0.8981198 [3,] 0.7975253 [4,] -1.1670880 [5,] 0.5667654 [6,] -0.8283407 [7,] -0.4719589 [8,] -1.5768718 [9,] -0.3896705 [10,] 2.0230529 [11,] -0.6736682 [12,] 0.6836290 [13,] 1.2375670 [14,] 0.5835861 [15,] -0.3383763 [16,] -0.8667159 [17,] 2.1025332 [18,] -0.8565713 [19,] 0.1005219 [20,] -0.9144049 [21,] -0.2549666 [22,] -1.1127070 [23,] 0.7349080 [24,] -1.1268640 [25,] 1.6813397 [26,] 4.0263014 [27,] 2.4796375 [28,] 2.2107507 [29,] 0.5858899 [30,] -0.3726134 [31,] -0.7904936 [32,] 4.3047435 [33,] 0.7268938 [34,] -0.5442369 [35,] 3.6875420 [36,] -0.5012098 [37,] 0.3135468 [38,] -0.2263140 [39,] -0.1497838 [40,] -0.2262720 [41,] 0.9293336 [42,] 0.4641124 [43,] 3.3241863 [44,] 5.6539198 [45,] 1.4949669 [46,] 1.1562529 [47,] 0.7394105 [48,] -0.9053379 [49,] -0.9989980 [50,] 4.5099184 GUMBEL [1,] 0.2461909 [2,] 0.8981198 [3,] 0.7975253 [4,] -1.1670880 [5,] 0.5667654 [6,] -0.8283407 [7,] -0.4719589 [8,] -1.5768718 [9,] -0.3896705 [10,] 2.0230529 [11,] -0.6736682 [12,] 0.6836290 [13,] 1.2375670 [14,] 0.5835861 [15,] -0.3383763 [16,] -0.8667159 [17,] 2.1025332 [18,] -0.8565713 [19,] 0.1005219 [20,] -0.9144049 [21,] -0.2549666 [22,] -1.1127070 [23,] 0.7349080 [24,] -1.1268640 [25,] 1.6813397 [26,] 4.0263014 [27,] 2.4796375 [28,] 2.2107507 [29,] 0.5858899 [30,] -0.3726134 [31,] -0.7904936 [32,] 4.3047435 [33,] 0.7268938 [34,] -0.5442369 [35,] 3.6875420 [36,] -0.5012098 [37,] 0.3135468 [38,] -0.2263140 [39,] -0.1497838 [40,] -0.2262720 [41,] 0.9293336 [42,] 0.4641124 [43,] 3.3241863 [44,] 5.6539198 [45,] 1.4949669 [46,] 1.1562529 [47,] 0.7394105 [48,] -0.9053379 [49,] -0.9989980 [50,] 4.5099184 GMT x 2007-01-01 0.2461909 2007-01-02 0.8981198 2007-01-03 0.7975253 2007-01-04 -1.1670880 2007-01-05 0.5667654 2007-01-06 -0.8283407 2007-01-07 -0.4719589 2007-01-08 -1.5768718 2007-01-09 -0.3896705 2007-01-10 2.0230529 2007-01-11 -0.6736682 2007-01-12 0.6836290 2007-01-13 1.2375670 2007-01-14 0.5835861 2007-01-15 -0.3383763 2007-01-16 -0.8667159 2007-01-17 2.1025332 2007-01-18 -0.8565713 2007-01-19 0.1005219 2007-01-20 -0.9144049 2007-01-21 -0.2549666 2007-01-22 -1.1127070 2007-01-23 0.7349080 2007-01-24 -1.1268640 2007-01-25 1.6813397 2007-01-26 4.0263014 2007-01-27 2.4796375 2007-01-28 2.2107507 2007-01-29 0.5858899 2007-01-30 -0.3726134 2007-01-31 -0.7904936 2007-02-01 4.3047435 2007-02-02 0.7268938 2007-02-03 -0.5442369 2007-02-04 3.6875420 2007-02-05 -0.5012098 2007-02-06 0.3135468 2007-02-07 -0.2263140 2007-02-08 -0.1497838 2007-02-09 -0.2262720 2007-02-10 0.9293336 2007-02-11 0.4641124 2007-02-12 3.3241863 2007-02-13 5.6539198 2007-02-14 1.4949669 2007-02-15 1.1562529 2007-02-16 0.7394105 2007-02-17 -0.9053379 2007-02-18 -0.9989980 2007-02-19 4.5099184 done successfully. Executing test function test.numericVectorBlocks ... done successfully. Executing test function test.plot ... Title: GEV Parameter Estimation Call: gevFit(x = x, block = "month") Estimation Type: gev mle Estimated Parameters: xi mu beta 0.6232298 8.3754440 5.9706039 Description Thu Dec 21 20:46:55 2023 done successfully. Executing test function test.summary ... Title: GEV Parameter Estimation Call: gevFit(x = x, block = "month") Estimation Type: gev mle Estimated Parameters: xi mu beta 0.6232298 8.3754440 5.9706039 Description Thu Dec 21 20:46:56 2023 Title: GEV Parameter Estimation Call: gevFit(x = x, block = "month") Estimation Type: gev mle Estimated Parameters: xi mu beta 0.6232298 8.3754440 5.9706039 Standard Deviations: xi mu beta 0.1030585 0.6115338 0.6327422 Log-Likelihood Value: 490.2329 Type of Convergence: 0 Description Thu Dec 21 20:46:56 2023 done successfully. Executing test function test.timeSeriesBlocks ... done successfully. Executing test function test.returnLevel ... done successfully. Executing test function test.gpd ... Distribution Check for: gpd Call: distCheck(fun = "gpd", n = 500, xi = 1, mu = 0, beta = 1) 1. Normalization Check: NORM 1 with absolute error < 1.1e-14 2. [p-pfun(qfun(p))]^2 Check: [,1] [,2] [,3] [,4] [,5] [,6] [,7] p 0.001 0.01 0.1 0.5 0.9 0.99 0.999 P 0.001 0.01 0.1 0.5 0.9 0.99 0.999 RMSE 4.759663e-17 3. r(500) Check: MEAN VAR SAMPLE 2.76 26 X 371.702 with absolute error < 0.045 X^2 failed with message 'the integral is probably divergent' MEAN VAR EXACT 372 -138000 done successfully. Executing test function test.gpdMoments ... done successfully. Executing test function test.gpdSlider ... done successfully. Executing test function test.fGPDFIT ... done successfully. Executing test function test.gpdFit ... Title: GPD Parameter Estimation Call: gpdFit(x = tS, u = min(series(tS)), type = "pwm") Estimation Method: gpd pwm Estimated Parameters: xi beta -0.1917369 0.9517318 Description Thu Dec 21 20:47:01 2023 by user: CRAN Title: GPD Parameter Estimation Call: gpdFit(x = ts, u = min(ts), type = "pwm") Estimation Method: gpd pwm Estimated Parameters: xi beta -0.1917369 0.9517318 Description Thu Dec 21 20:47:01 2023 by user: CRAN Title: GPD Parameter Estimation Call: gpdFit(x = x, u = min(x), type = "pwm") Estimation Method: gpd pwm Estimated Parameters: xi beta -0.1917369 0.9517318 Description Thu Dec 21 20:47:01 2023 by user: CRAN Title: GPD Parameter Estimation Call: gpdFit(x = tS, u = min(series(tS)), type = "mle") Estimation Method: gpd mle Estimated Parameters: xi beta -0.2298608 0.9845101 Description Thu Dec 21 20:47:01 2023 by user: CRAN Title: GPD Parameter Estimation Call: gpdFit(x = ts, u = min(ts), type = "mle") Estimation Method: gpd mle Estimated Parameters: xi beta -0.2298608 0.9845101 Description Thu Dec 21 20:47:01 2023 by user: CRAN Title: GPD Parameter Estimation Call: gpdFit(x = x, u = min(x), type = "mle") Estimation Method: gpd mle Estimated Parameters: xi beta -0.2298608 0.9845101 Description Thu Dec 21 20:47:01 2023 by user: CRAN Title: GPD Parameter Estimation Call: gpdFit(x = tS, u = min(series(tS)), type = "mle", information = "observed") Estimation Method: gpd mle Estimated Parameters: xi beta -0.2298608 0.9845101 Description Thu Dec 21 20:47:01 2023 by user: CRAN Title: GPD Parameter Estimation Call: gpdFit(x = tS, u = min(series(tS)), type = "mle", information = "expected") Estimation Method: gpd mle Estimated Parameters: xi beta -0.2298608 0.9845101 Description Thu Dec 21 20:47:01 2023 by user: CRAN done successfully. Executing test function test.gpdSim ... done successfully. Executing test function test.plot ... Title: GPD Parameter Estimation Call: gpdFit(x = ts, u = min(ts), type = "mle") Estimation Method: gpd mle Estimated Parameters: xi beta -0.2298608 0.9845101 Description Thu Dec 21 20:47:01 2023 by user: CRAN done successfully. Executing test function test.summary ... Title: GPD Parameter Estimation Call: gpdFit(x = ts, u = min(ts), type = "mle") Estimation Type: gpd mle Estimated Parameters: xi beta -0.2298608 0.9845101 Standard Deviations: xi beta 0.01224535 0.01813906 Log-Likelihood Value: 3771.734 Type of Convergence: 0 Description Thu Dec 21 20:47:01 2023 by user: CRAN done successfully. Executing test function test.gpdQPlot ... done successfully. Executing test function test.gpdQuantPlot ... done successfully. Executing test function test.gpdSfallPlot ... done successfully. Executing test function test.gpdShapePlot ... done successfully. Executing test function test.gpdTailPlot ... done successfully. Executing test function test.tailPlot ... done successfully. Executing test function test.tailRisk ... done successfully. Executing test function test.tailSlider ... done successfully. RUNIT TEST PROTOCOL -- Thu Dec 21 20:47:02 2023 *********************************************** Number of test functions: 47 Number of errors: 0 Number of failures: 0 1 Test Suite : fExtremes unit testing - 47 test functions, 0 errors, 0 failures There were 50 or more warnings (use warnings() to see the first 50) > > proc.time() user system elapsed 25.04 0.70 25.73