CRAN Package Check Results for Package tsallisqexp

Last updated on 2024-03-27 22:58:02 CET.

Flavor Version Tinstall Tcheck Ttotal Status Flags
r-devel-linux-x86_64-debian-clang 0.9-4 2.55 36.15 38.70 OK
r-devel-linux-x86_64-debian-gcc 0.9-4 2.14 27.47 29.61 OK
r-devel-linux-x86_64-fedora-clang 0.9-4 48.26 OK
r-devel-linux-x86_64-fedora-gcc 0.9-4 59.60 OK
r-devel-windows-x86_64 0.9-4 3.00 204.00 207.00 ERROR
r-patched-linux-x86_64 0.9-4 2.83 33.49 36.32 OK
r-release-linux-x86_64 0.9-4 2.65 33.99 36.64 OK
r-release-macos-arm64 0.9-4 25.00 OK
r-release-macos-x86_64 0.9-4 36.00 OK
r-release-windows-x86_64 0.9-4 4.00 59.00 63.00 OK
r-oldrel-macos-arm64 0.9-4 20.00 OK
r-oldrel-windows-x86_64 0.9-4 7.00 65.00 72.00 OK

Check Details

Version: 0.9-4
Check: tests
Result: ERROR Running 'test-tsal.R' [155s] Running the tests in 'tests/test-tsal.R' failed. Complete output: > library(tsallisqexp) > > # ?EPD > > ##### > # (1) density function > x <- seq(-5, 5, length=101) > > cbind(x, y <- dtsal(x, 1/2, 1/4), dtsal(x, 1/2, 1/4, log=TRUE)) x [1,] -5.0 0.00000000 -Inf [2,] -4.9 0.00000000 -Inf [3,] -4.8 0.00000000 -Inf [4,] -4.7 0.00000000 -Inf [5,] -4.6 0.00000000 -Inf [6,] -4.5 0.00000000 -Inf [7,] -4.4 0.00000000 -Inf [8,] -4.3 0.00000000 -Inf [9,] -4.2 0.00000000 -Inf [10,] -4.1 0.00000000 -Inf [11,] -4.0 0.00000000 -Inf [12,] -3.9 0.00000000 -Inf [13,] -3.8 0.00000000 -Inf [14,] -3.7 0.00000000 -Inf [15,] -3.6 0.00000000 -Inf [16,] -3.5 0.00000000 -Inf [17,] -3.4 0.00000000 -Inf [18,] -3.3 0.00000000 -Inf [19,] -3.2 0.00000000 -Inf [20,] -3.1 0.00000000 -Inf [21,] -3.0 0.00000000 -Inf [22,] -2.9 0.00000000 -Inf [23,] -2.8 0.00000000 -Inf [24,] -2.7 0.00000000 -Inf [25,] -2.6 0.00000000 -Inf [26,] -2.5 0.00000000 -Inf [27,] -2.4 0.00000000 -Inf [28,] -2.3 0.00000000 -Inf [29,] -2.2 0.00000000 -Inf [30,] -2.1 0.00000000 -Inf [31,] -2.0 0.00000000 -Inf [32,] -1.9 0.00000000 -Inf [33,] -1.8 0.00000000 -Inf [34,] -1.7 0.00000000 -Inf [35,] -1.6 0.00000000 -Inf [36,] -1.5 0.00000000 -Inf [37,] -1.4 0.00000000 -Inf [38,] -1.3 0.00000000 -Inf [39,] -1.2 0.00000000 -Inf [40,] -1.1 0.00000000 -Inf [41,] -1.0 0.00000000 -Inf [42,] -0.9 0.00000000 -Inf [43,] -0.8 0.00000000 -Inf [44,] -0.7 0.00000000 -Inf [45,] -0.6 0.00000000 -Inf [46,] -0.5 0.00000000 -Inf [47,] -0.4 0.00000000 -Inf [48,] -0.3 0.00000000 -Inf [49,] -0.2 22.36067977 3.1073040 [50,] -0.1 4.30331483 1.4593856 [51,] 0.0 2.00000000 0.6931472 [52,] 0.1 1.20736322 0.1884388 [53,] 0.2 0.82817332 -0.1885328 [54,] 0.3 0.61290897 -0.4895389 [55,] 0.4 0.47705667 -0.7401200 [56,] 0.5 0.38490018 -0.9547713 [57,] 0.6 0.31901538 -1.1425160 [58,] 0.7 0.26999430 -1.3093544 [59,] 0.8 0.23235716 -1.4594796 [60,] 0.9 0.20271844 -1.5959373 [61,] 1.0 0.17888544 -1.7210097 [62,] 1.1 0.15938203 -1.8364512 [63,] 1.2 0.14318186 -1.9436397 [64,] 1.3 0.12955150 -2.0436768 [65,] 1.4 0.11795439 -2.1374573 [66,] 1.5 0.10798985 -2.2257180 [67,] 1.6 0.09935333 -2.3090728 [68,] 1.7 0.09180960 -2.3880384 [69,] 1.8 0.08517443 -2.4630541 [70,] 1.9 0.07930167 -2.5344961 [71,] 2.0 0.07407407 -2.6026897 [72,] 2.1 0.06939660 -2.6679174 [73,] 2.2 0.06519149 -2.7304264 [74,] 2.3 0.06139454 -2.7904344 [75,] 2.4 0.05795237 -2.8481338 [76,] 2.5 0.05482024 -2.9036957 [77,] 2.6 0.05196043 -2.9572729 [78,] 2.7 0.04934089 -3.0090021 [79,] 2.8 0.04693429 -3.0590067 [80,] 2.9 0.04471716 -3.1073980 [81,] 3.0 0.04266925 -3.1542769 [82,] 3.1 0.04077301 -3.1997349 [83,] 3.2 0.03901318 -3.2438557 [84,] 3.3 0.03737640 -3.2867158 [85,] 3.4 0.03585095 -3.3283851 [86,] 3.5 0.03442652 -3.3689281 [87,] 3.6 0.03309397 -3.4084041 [88,] 3.7 0.03184523 -3.4468677 [89,] 3.8 0.03067309 -3.4843697 [90,] 3.9 0.02957113 -3.5209569 [91,] 4.0 0.02853360 -3.5566728 [92,] 4.1 0.02755536 -3.5915581 [93,] 4.2 0.02663177 -3.6256505 [94,] 4.3 0.02575864 -3.6589852 [95,] 4.4 0.02493220 -3.6915952 [96,] 4.5 0.02414902 -3.7235113 [97,] 4.6 0.02340601 -3.7547624 [98,] 4.7 0.02270033 -3.7853757 [99,] 4.8 0.02202941 -3.8153767 [100,] 4.9 0.02139091 -3.8447894 [101,] 5.0 0.02078266 -3.8736365 > # plot(x, y, type="l") > cbind(x, y <- dtsal.tail(x, 1/2, 1/4, xmin=3), dtsal.tail(x, 1/2, 1/4, log=TRUE, xmin=3)) x [1,] -5.0 0.00000000 -Inf [2,] -4.9 0.00000000 -Inf [3,] -4.8 0.00000000 -Inf [4,] -4.7 0.00000000 -Inf [5,] -4.6 0.00000000 -Inf [6,] -4.5 0.00000000 -Inf [7,] -4.4 0.00000000 -Inf [8,] -4.3 0.00000000 -Inf [9,] -4.2 0.00000000 -Inf [10,] -4.1 0.00000000 -Inf [11,] -4.0 0.00000000 -Inf [12,] -3.9 0.00000000 -Inf [13,] -3.8 0.00000000 -Inf [14,] -3.7 0.00000000 -Inf [15,] -3.6 0.00000000 -Inf [16,] -3.5 0.00000000 -Inf [17,] -3.4 0.00000000 -Inf [18,] -3.3 0.00000000 -Inf [19,] -3.2 0.00000000 -Inf [20,] -3.1 0.00000000 -Inf [21,] -3.0 0.00000000 -Inf [22,] -2.9 0.00000000 -Inf [23,] -2.8 0.00000000 -Inf [24,] -2.7 0.00000000 -Inf [25,] -2.6 0.00000000 -Inf [26,] -2.5 0.00000000 -Inf [27,] -2.4 0.00000000 -Inf [28,] -2.3 0.00000000 -Inf [29,] -2.2 0.00000000 -Inf [30,] -2.1 0.00000000 -Inf [31,] -2.0 0.00000000 -Inf [32,] -1.9 0.00000000 -Inf [33,] -1.8 0.00000000 -Inf [34,] -1.7 0.00000000 -Inf [35,] -1.6 0.00000000 -Inf [36,] -1.5 0.00000000 -Inf [37,] -1.4 0.00000000 -Inf [38,] -1.3 0.00000000 -Inf [39,] -1.2 0.00000000 -Inf [40,] -1.1 0.00000000 -Inf [41,] -1.0 0.00000000 -Inf [42,] -0.9 0.00000000 -Inf [43,] -0.8 0.00000000 -Inf [44,] -0.7 0.00000000 -Inf [45,] -0.6 0.00000000 -Inf [46,] -0.5 0.00000000 -Inf [47,] -0.4 0.00000000 -Inf [48,] -0.3 0.00000000 -Inf [49,] -0.2 0.00000000 -Inf [50,] -0.1 0.00000000 -Inf [51,] 0.0 0.00000000 -Inf [52,] 0.1 0.00000000 -Inf [53,] 0.2 0.00000000 -Inf [54,] 0.3 0.00000000 -Inf [55,] 0.4 0.00000000 -Inf [56,] 0.5 0.00000000 -Inf [57,] 0.6 0.00000000 -Inf [58,] 0.7 0.00000000 -Inf [59,] 0.8 0.00000000 -Inf [60,] 0.9 0.00000000 -Inf [61,] 1.0 0.00000000 -Inf [62,] 1.1 0.00000000 -Inf [63,] 1.2 0.00000000 -Inf [64,] 1.3 0.00000000 -Inf [65,] 1.4 0.00000000 -Inf [66,] 1.5 0.00000000 -Inf [67,] 1.6 0.00000000 -Inf [68,] 1.7 0.00000000 -Inf [69,] 1.8 0.00000000 -Inf [70,] 1.9 0.00000000 -Inf [71,] 2.0 0.00000000 -Inf [72,] 2.1 0.00000000 -Inf [73,] 2.2 0.00000000 -Inf [74,] 2.3 0.00000000 -Inf [75,] 2.4 0.00000000 -Inf [76,] 2.5 0.00000000 -Inf [77,] 2.6 0.00000000 -Inf [78,] 2.7 0.00000000 -Inf [79,] 2.8 0.00000000 -Inf [80,] 2.9 0.00000000 -Inf [81,] 3.0 0.15384615 -1.871802 [82,] 3.1 0.14700919 -1.917260 [83,] 3.2 0.14066403 -1.961381 [84,] 3.3 0.13476253 -2.004241 [85,] 3.4 0.12926245 -2.045910 [86,] 3.5 0.12412658 -2.086453 [87,] 3.6 0.11932202 -2.125929 [88,] 3.7 0.11481960 -2.164393 [89,] 3.8 0.11059338 -2.201895 [90,] 3.9 0.10662021 -2.238482 [91,] 4.0 0.10287937 -2.274198 [92,] 4.1 0.09935227 -2.309083 [93,] 4.2 0.09602220 -2.343176 [94,] 4.3 0.09287409 -2.376511 [95,] 4.4 0.08989432 -2.409121 [96,] 4.5 0.08707055 -2.441037 [97,] 4.6 0.08439157 -2.472288 [98,] 4.7 0.08184721 -2.502901 [99,] 4.8 0.07942818 -2.532902 [100,] 4.9 0.07712601 -2.562315 [101,] 5.0 0.07493293 -2.591162 > > ##### > # (2) distribution function > > ptsal(x, 1/2, 1/4) [1] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 [8] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 [15] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 [22] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 [29] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 [36] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 [43] 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 [50] 0.0000000 0.0000000 0.1548457 0.2546440 0.3258001 0.3798263 0.4226497 [57] 0.4576739 0.4870108 0.5120500 0.5337476 0.5527864 0.5696685 0.5847726 [64] 0.5983903 0.6107505 0.6220355 0.6323927 0.6419426 0.6507849 0.6590028 [71] 0.6666667 0.6738360 0.6805617 0.6868879 0.6928524 0.6984887 0.7038256 [78] 0.7088887 0.7137008 0.7182819 0.7226499 0.7268208 0.7308090 0.7346276 [85] 0.7382880 0.7418011 0.7451764 0.7484227 0.7515480 0.7545597 0.7574644 [92] 0.7602683 0.7629773 0.7655964 0.7681306 0.7705843 0.7729617 0.7752667 [99] 0.7775029 0.7796737 0.7817821 > ptsal(x, 1/2, 1/4, lower=FALSE) [1] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [8] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [15] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [22] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [29] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [36] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [43] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [50] 1.0000000 1.0000000 0.8451543 0.7453560 0.6741999 0.6201737 0.5773503 [57] 0.5423261 0.5129892 0.4879500 0.4662524 0.4472136 0.4303315 0.4152274 [64] 0.4016097 0.3892495 0.3779645 0.3676073 0.3580574 0.3492151 0.3409972 [71] 0.3333333 0.3261640 0.3194383 0.3131121 0.3071476 0.3015113 0.2961744 [78] 0.2911113 0.2862992 0.2817181 0.2773501 0.2731792 0.2691910 0.2653724 [85] 0.2617120 0.2581989 0.2548236 0.2515773 0.2484520 0.2454403 0.2425356 [92] 0.2397317 0.2370227 0.2344036 0.2318694 0.2294157 0.2270383 0.2247333 [99] 0.2224971 0.2203263 0.2182179 > ptsal(x, 1/2, 1/4, log=TRUE) [1] NaN NaN NaN NaN NaN NaN [7] NaN NaN NaN NaN NaN NaN [13] NaN NaN NaN NaN NaN NaN [19] NaN NaN NaN NaN NaN NaN [25] NaN NaN NaN NaN NaN NaN [31] NaN NaN NaN NaN NaN NaN [37] NaN NaN NaN NaN NaN NaN [43] NaN NaN NaN NaN NaN NaN [49] NaN NaN -Inf -1.8653258 -1.3678888 -1.1214712 [55] -0.9680412 -0.8612115 -0.7815985 -0.7194689 -0.6693331 -0.6278322 [61] -0.5927836 -0.5627006 -0.5365322 -0.5135120 -0.4930667 -0.4747581 [67] -0.4582447 -0.4432564 -0.4295762 -0.4170274 -0.4054651 -0.3947686 [73] -0.3848368 -0.3755842 -0.3669382 -0.3588363 -0.3512247 -0.3440567 [79] -0.3372914 -0.3308931 -0.3248304 -0.3190753 -0.3136031 -0.3083916 [85] -0.3034212 -0.2986741 -0.2941343 -0.2897874 -0.2856202 -0.2816209 [91] -0.2777788 -0.2740838 -0.2705270 -0.2671002 -0.2637956 -0.2606063 [97] -0.2575258 -0.2545482 -0.2516679 -0.2488798 -0.2461792 Warning message: In log(1 - (z^(-shape))) : NaNs produced > > ptsal(x, q=1/2, kappa=4) [1] 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 [7] 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 [13] 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 [19] 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 [25] 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 [31] 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 [37] 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 [43] 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 [49] 0.00000000 0.00000000 0.00000000 0.02484375 0.04937500 0.07359375 [55] 0.09750000 0.12109375 0.14437500 0.16734375 0.19000000 0.21234375 [61] 0.23437500 0.25609375 0.27750000 0.29859375 0.31937500 0.33984375 [67] 0.36000000 0.37984375 0.39937500 0.41859375 0.43750000 0.45609375 [73] 0.47437500 0.49234375 0.51000000 0.52734375 0.54437500 0.56109375 [79] 0.57750000 0.59359375 0.60937500 0.62484375 0.64000000 0.65484375 [85] 0.66937500 0.68359375 0.69750000 0.71109375 0.72437500 0.73734375 [91] 0.75000000 0.76234375 0.77437500 0.78609375 0.79750000 0.80859375 [97] 0.81937500 0.82984375 0.84000000 0.84984375 0.85937500 > > ptsal.tail(x, 1/2, 1/4, xmin=3) [1] 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 [7] 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 [13] 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 [19] 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 [25] 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 [31] 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 [37] 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 [43] 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 [49] 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 [55] 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 [61] 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 [67] 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 [73] 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 [79] 0.00000000 0.00000000 0.00000000 0.01503845 0.02941822 0.04318604 [85] 0.05638410 0.06905066 0.08122046 0.09292514 0.10419358 0.11505224 [91] 0.12552537 0.13563524 0.14540239 0.15484575 0.16398282 0.17282981 [97] 0.18140175 0.18971261 0.19777537 0.20560214 0.21320421 > ptsal.tail(x, 1/2, 1/4, xmin=3, log=TRUE) [1] -Inf -Inf -Inf -Inf -Inf -Inf -Inf [8] -Inf -Inf -Inf -Inf -Inf -Inf -Inf [15] -Inf -Inf -Inf -Inf -Inf -Inf -Inf [22] -Inf -Inf -Inf -Inf -Inf -Inf -Inf [29] -Inf -Inf -Inf -Inf -Inf -Inf -Inf [36] -Inf -Inf -Inf -Inf -Inf -Inf -Inf [43] -Inf -Inf -Inf -Inf -Inf -Inf -Inf [50] -Inf -Inf -Inf -Inf -Inf -Inf -Inf [57] -Inf -Inf -Inf -Inf -Inf -Inf -Inf [64] -Inf -Inf -Inf -Inf -Inf -Inf -Inf [71] -Inf -Inf -Inf -Inf -Inf -Inf -Inf [78] -Inf -Inf -Inf -Inf -4.197145 -3.526141 -3.142238 [85] -2.875568 -2.672915 -2.510588 -2.375961 -2.261505 -2.162369 -2.075247 [92] -1.997786 -1.928250 -1.865326 -1.807994 -1.755448 -1.707041 -1.662245 [99] -1.620623 -1.581812 -1.545505 Warning message: In log(1 - (C * z^(-shape))) : NaNs produced > ptsal.tail(x, 1/2, 1/4, xmin=3, lower=FALSE) [1] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [8] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [15] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [22] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [29] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [36] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [43] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [50] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [57] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [64] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [71] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 [78] 1.0000000 1.0000000 1.0000000 1.0000000 0.9849615 0.9705818 0.9568140 [85] 0.9436159 0.9309493 0.9187795 0.9070749 0.8958064 0.8849478 0.8744746 [92] 0.8643648 0.8545976 0.8451543 0.8360172 0.8271702 0.8185982 0.8102874 [99] 0.8022246 0.7943979 0.7867958 > ptsal.tail(x, 1/2, 1/4, xmin=3, lower=FALSE, log=TRUE) [1] 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 [7] 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 [13] 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 [19] 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 [25] 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 [31] 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 [37] 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 [43] 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 [49] 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 [55] 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 [61] 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 [67] 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 [73] 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 [79] 0.00000000 0.00000000 0.00000000 -0.01515267 -0.02985962 -0.04414630 [85] -0.05803609 -0.07155042 -0.08470908 -0.09753029 -0.11003094 -0.12222667 [91] -0.13413199 -0.14576042 -0.15712455 -0.16823612 -0.17910611 -0.18974481 [97] -0.20016185 -0.21036629 -0.22036662 -0.23017086 -0.23978654 > > > > ##### > # (3) quantile function > > qtsal(0:10/10, 3, 2) [1] 0.00000000 0.07148834 0.15443469 0.25249576 0.37126220 0.51984210 [7] 0.71441762 0.98760316 1.41995189 2.30886938 Inf > qtsal(log(0:10/10), 3, 2, log=TRUE) [1] 0.00000000 0.07148834 0.15443469 0.25249576 0.37126220 0.51984210 [7] 0.71441762 0.98760316 1.41995189 2.30886938 Inf > > qtsal.tail(0:10/10, 3, 2, xmin=3) [1] 3.000000 3.178721 3.386087 3.631239 3.928156 4.299605 4.786044 5.469008 [9] 6.549880 8.772173 Inf > qtsal.tail(log(0:10/10), 3, 2, xmin=3, log=TRUE) [1] 3.000000 3.178721 3.386087 3.631239 3.928156 4.299605 4.786044 5.469008 [9] 6.549880 8.772173 Inf > > > ##### > # (4) random generation function > > rtsal(10, 3, 2) [1] 0.50672430 0.17306287 0.68673217 0.05193965 0.45937181 0.19526519 [7] 0.78724509 0.11115452 0.53350668 0.82837696 > rtsal.tail(10, 3, 2, xmin=3) [1] 7.976940 3.784501 8.181562 4.555309 11.355400 6.107752 7.317455 [8] 3.917363 4.117301 3.036674 > > ##### > # (5) fit function > > set.seed(1234) > x <- rtsal(10, 3, 2) > > tsal.fit(x, method="mle.equation") $type [1] "tsal" $q [1] 0.6145008 $kappa [1] 0.9282705 $shape [1] -2.594039 $scale [1] -2.40797 $loglik [1] -5.400687 $n [1] 10 $xmin [1] 0 $method [1] "mle.equation" > tsal.fit(x, method="mle.direct") $type [1] "tsal" $q [1] 0.6145008 $kappa [1] 0.9282705 $shape [1] -2.594039 $scale [1] -2.40797 $loglik [1] -5.400687 $n [1] 10 $xmin [1] 0 $method [1] "mle.direct" > tsal.fit(x, method="leastsquares") $type [1] "tsal" $q [1] 1 $kappa [1] 0.6644425 $shape [1] 11748774 $scale [1] 7806385 $loglik [1] -5.911932 $n [1] 10 $xmin [1] 0 $method [1] "leastsquares" > > > > ##### > # (6) boot functions > > # ?tsal.boot > > tsal.bootstrap.errors(dist=NULL, reps=100, confidence=0.95, n=10) $originals shape scale q kappa 1 1 2 1 $bias shape scale q kappa -0.6808935 0.9614131 -0.3320685 1.0020521 $se shape scale q kappa 11.792101 14.756864 1.345451 3.448985 $confidence.interval.lower shape scale q kappa -10.2051863 -15.6167973 -3.3790572 0.3052557 $confidence.interval.upper shape scale q kappa 8.156044 8.542180 3.246330 13.988616 $sample.size [1] 10 $bootrap.replicates [1] 100 $confidence [1] 0.95 $method [1] "mle.equation" $xmin [1] 0 Warning message: In log(shape/scale) : NaNs produced > > tsal.bootstrap.errors(dist=tsal.fit(x, method="mle.equation"), reps=100) $originals shape scale q kappa -2.5940391 -2.4079699 0.6145008 0.9282705 $bias shape scale q kappa 2.640742 1.321381 -Inf Inf $se shape scale q kappa 9.171589 4.546734 NaN NaN $confidence.interval.lower shape scale q kappa -9.5448472 -6.1911554 -Inf 0.4337565 $confidence.interval.upper shape scale q kappa 8.454331 3.906426 1.139538 Inf $sample.size [1] 10 $bootrap.replicates [1] 100 $confidence [1] 0.95 $method [1] "mle.equation" $xmin [1] 0 There were 15 warnings (use warnings() to see them) > > tsal.total.magnitude(dist=NULL, n=10) $magnitude.est [1] Inf $count.est [1] 10 > > tsal.total.magnitude(dist=tsal.fit(x, method="mle.equation")) $magnitude.est [1] 6.699899 $count.est [1] 10 > > > ##### > # (7) test functions > > # ?tsal.test > > test.tsal.quantile.transform(from=0, to=1e6, shape=1, scale=1, + n=1e5, lwd=0.01, xmin=0) > > test.tsal.LR.distribution(n=10, reps=100, shape=2, scale=3/2, + xmin=0,method="mle.equation") Exact one-sample Kolmogorov-Smirnov test data: LR2 D = 0.15949, p-value = 0.01147 alternative hypothesis: two-sided Warning message: In log(shape/scale) : NaNs produced > test.tsal.LR.distribution(n=1000, reps=100, shape=2, scale=3/2, + xmin=0,method="mle.equation") Asymptotic one-sample Kolmogorov-Smirnov test data: LR2 D = 0.094052, p-value = 0.3393 alternative hypothesis: two-sided > > proc.time() user system elapsed 2.75 0.10 2.84 Flavor: r-devel-windows-x86_64