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Type 'q()' to quit R. > # This file is part of the standard setup for testthat. > # It is recommended that you do not modify it. > # > # Where should you do additional test configuration? > # Learn more about the roles of various files in: > # * https://r-pkgs.org/testing-design.html#sec-tests-files-overview > # * https://testthat.r-lib.org/articles/special-files.html > > library(testthat) > library(eratosthenes) > > test_check("eratosthenes") Assigning initial random values (this may take a moment)... Beginning main Gibbs sampler. Will terminate either when MCSE criterion or maximum number of MC samples reached. Samples: 1000 Mean MCSE: 2.149 Samples: 2000 Mean MCSE: 1.519 Samples: 3000 Mean MCSE: 1.304 Samples: 4000 Mean MCSE: 1.128 Samples: 5000 Mean MCSE: 0.91 Samples: 6000 Mean MCSE: 0.838 Samples: 7000 Mean MCSE: 0.815 Samples: 8000 Mean MCSE: 0.777 Samples: 9000 Mean MCSE: 0.727 Samples: 10000 Mean MCSE: 0.67 Samples: 11000 Mean MCSE: 0.633 Samples: 12000 Mean MCSE: 0.621 Samples: 13000 Mean MCSE: 0.602 Samples: 14000 Mean MCSE: 0.617 Samples: 15000 Mean MCSE: 0.551 Samples: 16000 Mean MCSE: 0.541 Samples: 17000 Mean MCSE: 0.506 Samples: 18000 Mean MCSE: 0.545 Samples: 19000 Mean MCSE: 0.517 Samples: 20000 Mean MCSE: 0.482 MCSE criterion passed. Finishing. Assigning initial random values (this may take a moment)... Beginning main Gibbs sampler. Will terminate either when MCSE criterion or maximum number of MC samples reached. Samples: 1000 Mean MCSE: 80.482 Samples: 2000 Mean MCSE: 62.581 Samples: 3000 Mean MCSE: 52.03 Samples: 4000 Mean MCSE: 44.852 Samples: 5000 Mean MCSE: 40.955 Samples: 6000 Mean MCSE: 37.234 Samples: 7000 Mean MCSE: 34.397 Samples: 8000 Mean MCSE: 33.907 Samples: 9000 Mean MCSE: 31.605 Samples: 10000 Mean MCSE: 30.815 Samples: 11000 Mean MCSE: 29.948 Samples: 12000 Mean MCSE: 28.438 Samples: 13000 Mean MCSE: 26.747 Samples: 14000 Mean MCSE: 25.527 Samples: 15000 Mean MCSE: 24.816 Samples: 16000 Mean MCSE: 25.959 Samples: 17000 Mean MCSE: 24.401 Samples: 18000 Mean MCSE: 24.662 Samples: 19000 Mean MCSE: 22.139 Samples: 20000 Mean MCSE: 24.019 Samples: 21000 Mean MCSE: 22.603 Samples: 22000 Mean MCSE: 21.772 Samples: 23000 Mean MCSE: 21.689 Samples: 24000 Mean MCSE: 20.071 Samples: 25000 Mean MCSE: 19.981 Samples: 26000 Mean MCSE: 19.734 Samples: 27000 Mean MCSE: 19.142 Samples: 28000 Mean MCSE: 18.64 Samples: 29000 Mean MCSE: 18.331 Samples: 30000 Mean MCSE: 19.386 Samples: 31000 Mean MCSE: 18.612 Samples: 32000 Mean MCSE: 18.252 Samples: 33000 Mean MCSE: 17.901 Samples: 34000 Mean MCSE: 17.76 Samples: 35000 Mean MCSE: 16.94 Samples: 36000 Mean MCSE: 16.997 Samples: 37000 Mean MCSE: 18.175 Samples: 38000 Mean MCSE: 17.435 Samples: 39000 Mean MCSE: 17.459 Samples: 40000 Mean MCSE: 16.181 Samples: 41000 Mean MCSE: 16.469 Samples: 42000 Mean MCSE: 16.478 Samples: 43000 Mean MCSE: 16.125 Samples: 44000 Mean MCSE: 16.45 Samples: 45000 Mean MCSE: 15.837 Samples: 46000 Mean MCSE: 15.065 Samples: 47000 Mean MCSE: 15.514 Samples: 48000 Mean MCSE: 14.696 Samples: 49000 Mean MCSE: 15.237 Samples: 50000 Mean MCSE: 14.804 Samples: 51000 Mean MCSE: 14.128 Samples: 52000 Mean MCSE: 14.511 Samples: 53000 Mean MCSE: 14.563 Samples: 54000 Mean MCSE: 14.117 Samples: 55000 Mean MCSE: 14.484 Samples: 56000 Mean MCSE: 13.839 Samples: 57000 Mean MCSE: 13.959 Samples: 58000 Mean MCSE: 13.998 Samples: 59000 Mean MCSE: 12.634 Samples: 60000 Mean MCSE: 13.048 Samples: 61000 Mean MCSE: 13.202 Samples: 62000 Mean MCSE: 12.707 Samples: 63000 Mean MCSE: 12.955 Samples: 64000 Mean MCSE: 12.727 Samples: 65000 Mean MCSE: 12.485 Samples: 66000 Mean MCSE: 12.826 Samples: 67000 Mean MCSE: 12.765 Samples: 68000 Mean MCSE: 11.905 Samples: 69000 Mean MCSE: 12.404 Samples: 70000 Mean MCSE: 12.152 Samples: 71000 Mean MCSE: 12.119 Samples: 72000 Mean MCSE: 12.269 Samples: 73000 Mean MCSE: 12.452 Samples: 74000 Mean MCSE: 12.12 Samples: 75000 Mean MCSE: 11.542 Samples: 76000 Mean MCSE: 11.85 Samples: 77000 Mean MCSE: 11.571 Samples: 78000 Mean MCSE: 11.407 Samples: 79000 Mean MCSE: 11.512 Samples: 80000 Mean MCSE: 11.254 Samples: 81000 Mean MCSE: 11.12 Samples: 82000 Mean MCSE: 11.457 Samples: 83000 Mean MCSE: 11.155 Samples: 84000 Mean MCSE: 11.292 Samples: 85000 Mean MCSE: 11.322 Samples: 86000 Mean MCSE: 10.688 Samples: 87000 Mean MCSE: 10.86 Samples: 88000 Mean MCSE: 11.063 Samples: 89000 Mean MCSE: 11.074 Samples: 90000 Mean MCSE: 10.726 Samples: 91000 Mean MCSE: 10.504 Samples: 92000 Mean MCSE: 10.617 Samples: 93000 Mean MCSE: 10.276 Samples: 94000 Mean MCSE: 10.553 Samples: 95000 Mean MCSE: 11.003 Samples: 96000 Mean MCSE: 10.87 Samples: 97000 Mean MCSE: 10.59 Samples: 98000 Mean MCSE: 10.117 Samples: 99000 Mean MCSE: 10.199 Samples: 100000 Mean MCSE: 9.662 MC samples exceeded maximum stipulated without passing MCSE criterion. Finishing. Assigning initial random values (this may take a moment)... /data/localhost/ripley/R/test-clang/Rcpp/include/Rcpp/sugar/operators/Comparator_With_One_Value.h:57:45: runtime error: nan is outside the range of representable values of type 'int' #0 0x7fee3d7ce63f in Rcpp::sugar::Comparator_With_One_Value<14, Rcpp::sugar::less<14>, true, Rcpp::Vector<14, Rcpp::PreserveStorage>>::rhs_is_na(int) const /data/localhost/ripley/R/test-clang/Rcpp/include/Rcpp/sugar/operators/Comparator_With_One_Value.h:57:45 #1 0x7fee3d7ceeb7 in Rcpp::sugar::Comparator_With_One_Value<14, Rcpp::sugar::less<14>, true, Rcpp::Vector<14, Rcpp::PreserveStorage>>::operator[](long) const /data/localhost/ripley/R/test-clang/Rcpp/include/Rcpp/sugar/operators/Comparator_With_One_Value.h:46:10 #2 0x7fee3d7ceeb7 in void Rcpp::Vector<10, Rcpp::PreserveStorage>::import_expression, true, Rcpp::Vector<14, Rcpp::PreserveStorage>>>(Rcpp::sugar::Comparator_With_One_Value<14, Rcpp::sugar::less<14>, true, Rcpp::Vector<14, Rcpp::PreserveStorage>> const&, long) /data/localhost/ripley/R/test-clang/Rcpp/include/Rcpp/vector/Vector.h:1097:32 #3 0x7fee3d7b60be in void Rcpp::Vector<10, Rcpp::PreserveStorage>::import_sugar_expression, true, Rcpp::Vector<14, Rcpp::PreserveStorage>>>(Rcpp::VectorBase<10, true, Rcpp::sugar::Comparator_With_One_Value<14, Rcpp::sugar::less<14>, true, Rcpp::Vector<14, Rcpp::PreserveStorage>>> const&, Rcpp::traits::integral_constant) /data/localhost/ripley/R/test-clang/Rcpp/include/Rcpp/vector/Vector.h:1083:9 #4 0x7fee3d7b60be in Rcpp::Vector<10, Rcpp::PreserveStorage>::Vector, true, Rcpp::Vector<14, Rcpp::PreserveStorage>>>(Rcpp::VectorBase<10, true, Rcpp::sugar::Comparator_With_One_Value<14, Rcpp::sugar::less<14>, true, Rcpp::Vector<14, Rcpp::PreserveStorage>>> const&) /data/localhost/ripley/R/test-clang/Rcpp/include/Rcpp/vector/Vector.h:165:9 #5 0x7fee3d7b60be in Rcpp::SubsetProxy<14, Rcpp::PreserveStorage, 10, true, Rcpp::sugar::Comparator_With_One_Value<14, Rcpp::sugar::less<14>, true, Rcpp::Vector<14, Rcpp::PreserveStorage>>> Rcpp::Vector<14, Rcpp::PreserveStorage>::operator[]<10, true, Rcpp::sugar::Comparator_With_One_Value<14, Rcpp::sugar::less<14>, true, Rcpp::Vector<14, Rcpp::PreserveStorage>>>(Rcpp::VectorBase<10, true, Rcpp::sugar::Comparator_With_One_Value<14, Rcpp::sugar::less<14>, true, Rcpp::Vector<14, Rcpp::PreserveStorage>>> const&) /data/localhost/ripley/R/test-clang/Rcpp/include/Rcpp/vector/Vector.h:401:13 #6 0x7fee3d7b60be in gibbs_ad_initial_cpp(Rcpp::Vector<14, Rcpp::PreserveStorage>, Rcpp::Vector<13, Rcpp::PreserveStorage>, Rcpp::Matrix<13, Rcpp::PreserveStorage>, Rcpp::Vector<19, Rcpp::PreserveStorage>, Rcpp::Vector<13, Rcpp::PreserveStorage>, Rcpp::Matrix<13, Rcpp::PreserveStorage>, Rcpp::Vector<19, Rcpp::PreserveStorage>, Rcpp::Vector<13, Rcpp::PreserveStorage>, int) /data/localhost/ripley/R/packages/tests-clang-UBSAN/eratosthenes/src/eratosthenes.cpp:548:44 #7 0x7fee3d776ca5 in _eratosthenes_gibbs_ad_initial_cpp /data/localhost/ripley/R/packages/tests-clang-UBSAN/eratosthenes/src/RcppExports.cpp:102:34 #8 0x5588004b2b3b in R_doDotCall (/data/localhost/ripley/R/R-clang/bin/exec/R+0x91b3b) #9 0x5588004b342d in do_dotcall (/data/localhost/ripley/R/R-clang/bin/exec/R+0x9242d) #10 0x5588004f1f63 in bcEval_loop eval.c #11 0x5588004eb60b in bcEval eval.c #12 0x5588004eadc4 in Rf_eval (/data/localhost/ripley/R/R-clang/bin/exec/R+0xc9dc4) #13 0x558800502e98 in R_execClosure eval.c #14 0x55880050238a in applyClosure_core eval.c #15 0x5588004ebcd6 in Rf_applyClosure (/data/localhost/ripley/R/R-clang/bin/exec/R+0xcacd6) #16 0x55880054f4d1 in dispatchMethod objects.c #17 0x55880054eef8 in Rf_usemethod (/data/localhost/ripley/R/R-clang/bin/exec/R+0x12def8) #18 0x55880054fa2e in do_usemethod (/data/localhost/ripley/R/R-clang/bin/exec/R+0x12ea2e) #19 0x5588004f21c5 in bcEval_loop eval.c #20 0x5588004eb60b in bcEval eval.c #21 0x5588004eadc4 in Rf_eval (/data/localhost/ripley/R/R-clang/bin/exec/R+0xc9dc4) #22 0x558800502e98 in R_execClosure eval.c #23 0x55880050238a in applyClosure_core eval.c #24 0x5588004ebcd6 in Rf_applyClosure (/data/localhost/ripley/R/R-clang/bin/exec/R+0xcacd6) #25 0x5588004eb217 in Rf_eval (/data/localhost/ripley/R/R-clang/bin/exec/R+0xca217) #26 0x5588004af7cb in do_External (/data/localhost/ripley/R/R-clang/bin/exec/R+0x8e7cb) #27 0x5588004f1f63 in bcEval_loop eval.c #28 0x5588004eb60b in bcEval eval.c #29 0x5588004eadc4 in Rf_eval (/data/localhost/ripley/R/R-clang/bin/exec/R+0xc9dc4) #30 0x558800502e98 in R_execClosure eval.c #31 0x55880050238a in applyClosure_core eval.c #32 0x5588004ebcd6 in Rf_applyClosure (/data/localhost/ripley/R/R-clang/bin/exec/R+0xcacd6) #33 0x5588004eb217 in Rf_eval (/data/localhost/ripley/R/R-clang/bin/exec/R+0xca217) #34 0x5588005074da in do_begin (/data/localhost/ripley/R/R-clang/bin/exec/R+0xe64da) #35 0x5588004eafef in Rf_eval (/data/localhost/ripley/R/R-clang/bin/exec/R+0xc9fef) #36 0x558800509647 in do_eval (/data/localhost/ripley/R/R-clang/bin/exec/R+0xe8647) #37 0x5588004f1f63 in bcEval_loop eval.c #38 0x5588004eb60b in bcEval eval.c #39 0x5588004eadc4 in Rf_eval (/data/localhost/ripley/R/R-clang/bin/exec/R+0xc9dc4) #40 0x558800502e98 in R_execClosure eval.c #41 0x55880050238a in applyClosure_core eval.c #42 0x5588004ebcd6 in Rf_applyClosure (/data/localhost/ripley/R/R-clang/bin/exec/R+0xcacd6) #43 0x5588004eb217 in Rf_eval (/data/localhost/ripley/R/R-clang/bin/exec/R+0xca217) #44 0x558800509a27 in do_eval (/data/localhost/ripley/R/R-clang/bin/exec/R+0xe8a27) #45 0x5588004f1f63 in bcEval_loop eval.c #46 0x5588004eb60b in bcEval eval.c #47 0x5588004eadc4 in Rf_eval (/data/localhost/ripley/R/R-clang/bin/exec/R+0xc9dc4) #48 0x558800502e98 in R_execClosure eval.c #49 0x55880050238a in applyClosure_core eval.c #50 0x558800505838 in R_forceAndCall (/data/localhost/ripley/R/R-clang/bin/exec/R+0xe4838) #51 0x55880043674b in do_lapply (/data/localhost/ripley/R/R-clang/bin/exec/R+0x1574b) #52 0x55880054d217 in do_internal (/data/localhost/ripley/R/R-clang/bin/exec/R+0x12c217) #53 0x5588004f21c5 in bcEval_loop eval.c #54 0x5588004eb60b in bcEval eval.c #55 0x5588004eadc4 in Rf_eval (/data/localhost/ripley/R/R-clang/bin/exec/R+0xc9dc4) #56 0x558800502e98 in R_execClosure eval.c #57 0x55880050238a in applyClosure_core eval.c #58 0x5588004ebcd6 in Rf_applyClosure (/data/localhost/ripley/R/R-clang/bin/exec/R+0xcacd6) #59 0x5588004eb217 in Rf_eval (/data/localhost/ripley/R/R-clang/bin/exec/R+0xca217) #60 0x558800538377 in Rf_ReplIteration (/data/localhost/ripley/R/R-clang/bin/exec/R+0x117377) #61 0x558800539e6e in run_Rmainloop (/data/localhost/ripley/R/R-clang/bin/exec/R+0x118e6e) #62 0x558800539eda in Rf_mainloop (/data/localhost/ripley/R/R-clang/bin/exec/R+0x118eda) #63 0x558800422db7 in main (/data/localhost/ripley/R/R-clang/bin/exec/R+0x1db7) #64 0x7fee47b0b680 in __libc_start_call_main (/lib64/libc.so.6+0x3680) (BuildId: 5bd941be836f538fe5e10eff508f7f5dd94905a6) #65 0x7fee47b0b797 in __libc_start_main@GLIBC_2.2.5 (/lib64/libc.so.6+0x3797) (BuildId: 5bd941be836f538fe5e10eff508f7f5dd94905a6) #66 0x558800422cd4 in _start (/data/localhost/ripley/R/R-clang/bin/exec/R+0x1cd4) SUMMARY: UndefinedBehaviorSanitizer: undefined-behavior /data/localhost/ripley/R/test-clang/Rcpp/include/Rcpp/sugar/operators/Comparator_With_One_Value.h:57:45 Estimating production, use, and depositional dates for id(s)/type(s) specified. Assigning initial random values (this may take a moment)... Beginning main Gibbs sampler. Will terminate either when MCSE criterion or maximum number of MC samples reached. Note: MCSE stopping criterion is only applied to sequences/constraints, not finds. Samples: 1000 Mean MCSE: 2.173 Samples: 2000 Mean MCSE: 1.495 Samples: 3000 Mean MCSE: 1.348 Samples: 4000 Mean MCSE: 1.014 Samples: 5000 Mean MCSE: 0.937 Samples: 6000 Mean MCSE: 0.852 Samples: 7000 Mean MCSE: 0.719 Samples: 8000 Mean MCSE: 0.708 Samples: 9000 Mean MCSE: 0.68 Samples: 10000 Mean MCSE: 0.637 Samples: 11000 Mean MCSE: 0.609 Samples: 12000 Mean MCSE: 0.562 Samples: 13000 Mean MCSE: 0.553 Samples: 14000 Mean MCSE: 0.506 Samples: 15000 Mean MCSE: 0.519 Samples: 16000 Mean MCSE: 0.477 MCSE criterion passed. Finishing. Estimating production, use, and depositional dates for id(s)/type(s) specified. Assigning initial random values (this may take a moment)... Beginning main Gibbs sampler. Will terminate either when MCSE criterion or maximum number of MC samples reached. Note: MCSE stopping criterion is only applied to sequences/constraints, not finds. Samples: 1000 Mean MCSE: 2.207 Samples: 2000 Mean MCSE: 1.454 Samples: 3000 Mean MCSE: 1.36 Samples: 4000 Mean MCSE: 1.162 Samples: 5000 Mean MCSE: 0.992 Samples: 6000 Mean MCSE: 0.851 Samples: 7000 Mean MCSE: 0.825 Samples: 8000 Mean MCSE: 0.761 Samples: 9000 Mean MCSE: 0.728 Samples: 10000 Mean MCSE: 0.642 Samples: 11000 Mean MCSE: 0.607 Samples: 12000 Mean MCSE: 0.619 Samples: 13000 Mean MCSE: 0.586 Samples: 14000 Mean MCSE: 0.568 Samples: 15000 Mean MCSE: 0.561 Samples: 16000 Mean MCSE: 0.566 Samples: 17000 Mean MCSE: 0.545 Samples: 18000 Mean MCSE: 0.487 MCSE criterion passed. Finishing. Assigning initial random values (this may take a moment)... Beginning main Gibbs sampler. Will terminate either when MCSE criterion or maximum number of MC samples reached. Samples: 1000 Mean MCSE: 2.009 Samples: 2000 Mean MCSE: 1.315 Samples: 3000 Mean MCSE: 1.07 Samples: 4000 Mean MCSE: 0.944 Samples: 5000 Mean MCSE: 0.848 Samples: 6000 Mean MCSE: 0.795 Samples: 7000 Mean MCSE: 0.776 Samples: 8000 Mean MCSE: 0.77 Samples: 9000 Mean MCSE: 0.669 Samples: 10000 Mean MCSE: 0.689 Samples: 11000 Mean MCSE: 0.638 Samples: 12000 Mean MCSE: 0.586 Samples: 13000 Mean MCSE: 0.611 Samples: 14000 Mean MCSE: 0.578 Samples: 15000 Mean MCSE: 0.558 Samples: 16000 Mean MCSE: 0.525 Samples: 17000 Mean MCSE: 0.527 Samples: 18000 Mean MCSE: 0.503 Samples: 19000 Mean MCSE: 0.479 MCSE criterion passed. Finishing. Beginning jackknife/LOO-style routine to compute MSD. This may take a while, depending on the number of events / mcse_crit... Depositional Event / Absolute Constraint: B Assigning initial random values (this may take a moment)... Beginning main Gibbs sampler. Will terminate either when MCSE criterion or maximum number of MC samples reached. Samples: 1000 Mean MCSE: 25.066 Samples: 2000 Mean MCSE: 20.544 Samples: 3000 Mean MCSE: 14.906 Samples: 4000 Mean MCSE: 13.761 Samples: 5000 Mean MCSE: 11.957 MC samples exceeded maximum stipulated without passing MCSE criterion. Finishing. Depositional Event / Absolute Constraint: C Assigning initial random values (this may take a moment)... Beginning main Gibbs sampler. Will terminate either when MCSE criterion or maximum number of MC samples reached. Samples: 1000 Mean MCSE: 9.183 Samples: 2000 Mean MCSE: 6.137 Samples: 3000 Mean MCSE: 4.742 Samples: 4000 Mean MCSE: 4.503 Samples: 5000 Mean MCSE: 3.908 MC samples exceeded maximum stipulated without passing MCSE criterion. Finishing. Depositional Event / Absolute Constraint: D Assigning initial random values (this may take a moment)... Beginning main Gibbs sampler. Will terminate either when MCSE criterion or maximum number of MC samples reached. Samples: 1000 Mean MCSE: 8.148 Samples: 2000 Mean MCSE: 6.158 Samples: 3000 Mean MCSE: 4.969 Samples: 4000 Mean MCSE: 4.475 Samples: 5000 Mean MCSE: 3.876 MC samples exceeded maximum stipulated without passing MCSE criterion. Finishing. Depositional Event / Absolute Constraint: E Assigning initial random values (this may take a moment)... Beginning main Gibbs sampler. Will terminate either when MCSE criterion or maximum number of MC samples reached. Samples: 1000 Mean MCSE: 8.333 Samples: 2000 Mean MCSE: 6.136 Samples: 3000 Mean MCSE: 5.2 Samples: 4000 Mean MCSE: 4.202 Samples: 5000 Mean MCSE: 3.904 MC samples exceeded maximum stipulated without passing MCSE criterion. Finishing. Depositional Event / Absolute Constraint: F Assigning initial random values (this may take a moment)... Beginning main Gibbs sampler. Will terminate either when MCSE criterion or maximum number of MC samples reached. Samples: 1000 Mean MCSE: 7.055 Samples: 2000 Mean MCSE: 5.381 Samples: 3000 Mean MCSE: 4.583 Samples: 4000 Mean MCSE: 4.021 Samples: 5000 Mean MCSE: 3.75 MC samples exceeded maximum stipulated without passing MCSE criterion. Finishing. Depositional Event / Absolute Constraint: G Assigning initial random values (this may take a moment)... Beginning main Gibbs sampler. Will terminate either when MCSE criterion or maximum number of MC samples reached. Samples: 1000 Mean MCSE: 9.84 Samples: 2000 Mean MCSE: 7.16 Samples: 3000 Mean MCSE: 5.825 Samples: 4000 Mean MCSE: 5.501 Samples: 5000 Mean MCSE: 4.931 MC samples exceeded maximum stipulated without passing MCSE criterion. Finishing. Depositional Event / Absolute Constraint: H Assigning initial random values (this may take a moment)... Beginning main Gibbs sampler. Will terminate either when MCSE criterion or maximum number of MC samples reached. Samples: 1000 Mean MCSE: 8.693 Samples: 2000 Mean MCSE: 6.65 Samples: 3000 Mean MCSE: 5.476 Samples: 4000 Mean MCSE: 4.488 Samples: 5000 Mean MCSE: 3.988 MC samples exceeded maximum stipulated without passing MCSE criterion. Finishing. Depositional Event / Absolute Constraint: I Assigning initial random values (this may take a moment)... Beginning main Gibbs sampler. Will terminate either when MCSE criterion or maximum number of MC samples reached. Samples: 1000 Mean MCSE: 9.575 Samples: 2000 Mean MCSE: 6.299 Samples: 3000 Mean MCSE: 5.486 Samples: 4000 Mean MCSE: 4.54 Samples: 5000 Mean MCSE: 4.142 MC samples exceeded maximum stipulated without passing MCSE criterion. Finishing. Depositional Event / Absolute Constraint: J Assigning initial random values (this may take a moment)... Beginning main Gibbs sampler. Will terminate either when MCSE criterion or maximum number of MC samples reached. Samples: 1000 Mean MCSE: 27.762 Samples: 2000 Mean MCSE: 20.182 Samples: 3000 Mean MCSE: 16.903 Samples: 4000 Mean MCSE: 15.011 Samples: 5000 Mean MCSE: 13.46 MC samples exceeded maximum stipulated without passing MCSE criterion. Finishing. Depositional Event / Absolute Constraint: coin1 Assigning initial random values (this may take a moment)... Beginning main Gibbs sampler. Will terminate either when MCSE criterion or maximum number of MC samples reached. Samples: 1000 Mean MCSE: 32.732 Samples: 2000 Mean MCSE: 22.082 Samples: 3000 Mean MCSE: 17.882 Samples: 4000 Mean MCSE: 16.299 Samples: 5000 Mean MCSE: 14.859 MC samples exceeded maximum stipulated without passing MCSE criterion. Finishing. Depositional Event / Absolute Constraint: coin2 Assigning initial random values (this may take a moment)... Beginning main Gibbs sampler. Will terminate either when MCSE criterion or maximum number of MC samples reached. Samples: 1000 Mean MCSE: 9.852 Samples: 2000 Mean MCSE: 7.477 Samples: 3000 Mean MCSE: 5.837 Samples: 4000 Mean MCSE: 4.984 Samples: 5000 Mean MCSE: 4.659 MC samples exceeded maximum stipulated without passing MCSE criterion. Finishing. Depositional Event / Absolute Constraint: destr Assigning initial random values (this may take a moment)... Beginning main Gibbs sampler. Will terminate either when MCSE criterion or maximum number of MC samples reached. Samples: 1000 Mean MCSE: 22.712 Samples: 2000 Mean MCSE: 18.337 Samples: 3000 Mean MCSE: 14.249 Samples: 4000 Mean MCSE: 12.53 Samples: 5000 Mean MCSE: 10.676 MC samples exceeded maximum stipulated without passing MCSE criterion. Finishing. Estimation of MSD complete. Assigning initial random values (this may take a moment)... Beginning main Gibbs sampler. Will terminate either when MCSE criterion or maximum number of MC samples reached. Samples: 1000 Mean MCSE: 2.041 Samples: 2000 Mean MCSE: 1.565 Samples: 3000 Mean MCSE: 1.328 Samples: 4000 Mean MCSE: 1.155 Samples: 5000 Mean MCSE: 0.944 Samples: 6000 Mean MCSE: 0.886 Samples: 7000 Mean MCSE: 0.792 Samples: 8000 Mean MCSE: 0.744 Samples: 9000 Mean MCSE: 0.706 Samples: 10000 Mean MCSE: 0.651 Samples: 11000 Mean MCSE: 0.612 Samples: 12000 Mean MCSE: 0.601 Samples: 13000 Mean MCSE: 0.606 Samples: 14000 Mean MCSE: 0.567 Samples: 15000 Mean MCSE: 0.574 Samples: 16000 Mean MCSE: 0.537 Samples: 17000 Mean MCSE: 0.518 Samples: 18000 Mean MCSE: 0.51 Samples: 19000 Mean MCSE: 0.493 MCSE criterion passed. Finishing. Estimating production, use, and depositional dates for id(s)/type(s) specified. Assigning initial random values (this may take a moment)... Beginning main Gibbs sampler. Will terminate either when MCSE criterion or maximum number of MC samples reached. Note: MCSE stopping criterion is only applied to sequences/constraints, not finds. Samples: 1000 Mean MCSE: 1.671 Samples: 2000 Mean MCSE: 1.263 Samples: 3000 Mean MCSE: 1.119 Samples: 4000 Mean MCSE: 0.954 Samples: 5000 Mean MCSE: 1.022 Samples: 6000 Mean MCSE: 0.871 Samples: 7000 Mean MCSE: 0.765 Samples: 8000 Mean MCSE: 0.783 Samples: 9000 Mean MCSE: 0.724 Samples: 10000 Mean MCSE: 0.69 Samples: 11000 Mean MCSE: 0.624 Samples: 12000 Mean MCSE: 0.601 Samples: 13000 Mean MCSE: 0.563 Samples: 14000 Mean MCSE: 0.556 Samples: 15000 Mean MCSE: 0.602 Samples: 16000 Mean MCSE: 0.521 Samples: 17000 Mean MCSE: 0.555 Samples: 18000 Mean MCSE: 0.538 Samples: 19000 Mean MCSE: 0.477 MCSE criterion passed. Finishing. Beginning jackknife/LOO-style routine to compute squared displacement. This may take a while, depending on the number of events / mcse_crit... Depositional Event / Absolute Constraint: B Assigning initial random values (this may take a moment)... Beginning main Gibbs sampler. Will terminate either when MCSE criterion or maximum number of MC samples reached. Samples: 1000 Mean MCSE: 24.371 Samples: 2000 Mean MCSE: 18.715 Samples: 3000 Mean MCSE: 15.814 MC samples exceeded maximum stipulated without passing MCSE criterion. Finishing. Depositional Event / Absolute Constraint: C Assigning initial random values (this may take a moment)... Beginning main Gibbs sampler. Will terminate either when MCSE criterion or maximum number of MC samples reached. Samples: 1000 Mean MCSE: 7.786 Samples: 2000 Mean MCSE: 6.302 Samples: 3000 Mean MCSE: 5.131 MC samples exceeded maximum stipulated without passing MCSE criterion. Finishing. Depositional Event / Absolute Constraint: D Assigning initial random values (this may take a moment)... Beginning main Gibbs sampler. Will terminate either when MCSE criterion or maximum number of MC samples reached. Samples: 1000 Mean MCSE: 7.807 Samples: 2000 Mean MCSE: 5.168 Samples: 3000 Mean MCSE: 4.644 MC samples exceeded maximum stipulated without passing MCSE criterion. Finishing. Depositional Event / Absolute Constraint: F Assigning initial random values (this may take a moment)... Beginning main Gibbs sampler. Will terminate either when MCSE criterion or maximum number of MC samples reached. Samples: 1000 Mean MCSE: 8.025 Samples: 2000 Mean MCSE: 5.707 Samples: 3000 Mean MCSE: 4.797 MC samples exceeded maximum stipulated without passing MCSE criterion. Finishing. Depositional Event / Absolute Constraint: G Assigning initial random values (this may take a moment)... Beginning main Gibbs sampler. Will terminate either when MCSE criterion or maximum number of MC samples reached. Samples: 1000 Mean MCSE: 9.932 Samples: 2000 Mean MCSE: 7.907 Samples: 3000 Mean MCSE: 6.459 MC samples exceeded maximum stipulated without passing MCSE criterion. Finishing. Depositional Event / Absolute Constraint: H Assigning initial random values (this may take a moment)... Beginning main Gibbs sampler. Will terminate either when MCSE criterion or maximum number of MC samples reached. Samples: 1000 Mean MCSE: 8.938 Samples: 2000 Mean MCSE: 6.03 Samples: 3000 Mean MCSE: 5.024 MC samples exceeded maximum stipulated without passing MCSE criterion. Finishing. Depositional Event / Absolute Constraint: I Assigning initial random values (this may take a moment)... Beginning main Gibbs sampler. Will terminate either when MCSE criterion or maximum number of MC samples reached. Samples: 1000 Mean MCSE: 9.271 Samples: 2000 Mean MCSE: 6.228 Samples: 3000 Mean MCSE: 5.316 MC samples exceeded maximum stipulated without passing MCSE criterion. Finishing. Depositional Event / Absolute Constraint: J Assigning initial random values (this may take a moment)... Beginning main Gibbs sampler. Will terminate either when MCSE criterion or maximum number of MC samples reached. Samples: 1000 Mean MCSE: 20.295 Samples: 2000 Mean MCSE: 19.331 Samples: 3000 Mean MCSE: 15.902 MC samples exceeded maximum stipulated without passing MCSE criterion. Finishing. Depositional Event / Absolute Constraint: coin1 Assigning initial random values (this may take a moment)... Beginning main Gibbs sampler. Will terminate either when MCSE criterion or maximum number of MC samples reached. Samples: 1000 Mean MCSE: 26.315 Samples: 2000 Mean MCSE: 21.263 Samples: 3000 Mean MCSE: 14.61 MC samples exceeded maximum stipulated without passing MCSE criterion. Finishing. Depositional Event / Absolute Constraint: coin2 Assigning initial random values (this may take a moment)... Beginning main Gibbs sampler. Will terminate either when MCSE criterion or maximum number of MC samples reached. Samples: 1000 Mean MCSE: 10.094 Samples: 2000 Mean MCSE: 7.323 Samples: 3000 Mean MCSE: 6.59 MC samples exceeded maximum stipulated without passing MCSE criterion. Finishing. Depositional Event / Absolute Constraint: destr Assigning initial random values (this may take a moment)... Beginning main Gibbs sampler. Will terminate either when MCSE criterion or maximum number of MC samples reached. Samples: 1000 Mean MCSE: 23.508 Samples: 2000 Mean MCSE: 17.569 Samples: 3000 Mean MCSE: 14.25 MC samples exceeded maximum stipulated without passing MCSE criterion. Finishing. Estimation of squared displacement complete. Beginning jackknife/LOO-style routine to compute squared displacement. This may take a while, depending on the number of events / mcse_crit... Depositional Event / Absolute Constraint: coin1 Estimating production, use, and depositional dates for id(s)/type(s) specified. Assigning initial random values (this may take a moment)... Beginning main Gibbs sampler. Will terminate either when MCSE criterion or maximum number of MC samples reached. Note: MCSE stopping criterion is only applied to sequences/constraints, not finds. Samples: 1000 Mean MCSE: 28.376 Samples: 2000 Mean MCSE: 19.167 Samples: 3000 Mean MCSE: 17.205 MC samples exceeded maximum stipulated without passing MCSE criterion. Finishing. Depositional Event / Absolute Constraint: coin2 Estimating production, use, and depositional dates for id(s)/type(s) specified. Assigning initial random values (this may take a moment)... Beginning main Gibbs sampler. Will terminate either when MCSE criterion or maximum number of MC samples reached. Note: MCSE stopping criterion is only applied to sequences/constraints, not finds. Samples: 1000 Mean MCSE: 10.669 Samples: 2000 Mean MCSE: 7.796 Samples: 3000 Mean MCSE: 6.326 MC samples exceeded maximum stipulated without passing MCSE criterion. Finishing. Depositional Event / Absolute Constraint: destr Estimating production, use, and depositional dates for id(s)/type(s) specified. Assigning initial random values (this may take a moment)... Beginning main Gibbs sampler. Will terminate either when MCSE criterion or maximum number of MC samples reached. Note: MCSE stopping criterion is only applied to sequences/constraints, not finds. Samples: 1000 Mean MCSE: 24.199 Samples: 2000 Mean MCSE: 18.698 Samples: 3000 Mean MCSE: 15.649 MC samples exceeded maximum stipulated without passing MCSE criterion. Finishing. Depositional Event / Absolute Constraint: A Estimating production, use, and depositional dates for id(s)/type(s) specified. Assigning initial random values (this may take a moment)... Beginning main Gibbs sampler. Will terminate either when MCSE criterion or maximum number of MC samples reached. Note: MCSE stopping criterion is only applied to sequences/constraints, not finds. Samples: 1000 Mean MCSE: 4.809 Samples: 2000 Mean MCSE: 4.308 Samples: 3000 Mean MCSE: 3.321 MC samples exceeded maximum stipulated without passing MCSE criterion. Finishing. Depositional Event / Absolute Constraint: B Estimating production, use, and depositional dates for id(s)/type(s) specified. Assigning initial random values (this may take a moment)... Beginning main Gibbs sampler. Will terminate either when MCSE criterion or maximum number of MC samples reached. Note: MCSE stopping criterion is only applied to sequences/constraints, not finds. Samples: 1000 Mean MCSE: 24.996 Samples: 2000 Mean MCSE: 19.77 Samples: 3000 Mean MCSE: 15.668 MC samples exceeded maximum stipulated without passing MCSE criterion. Finishing. Depositional Event / Absolute Constraint: C Estimating production, use, and depositional dates for id(s)/type(s) specified. Assigning initial random values (this may take a moment)... Beginning main Gibbs sampler. Will terminate either when MCSE criterion or maximum number of MC samples reached. Note: MCSE stopping criterion is only applied to sequences/constraints, not finds. Samples: 1000 Mean MCSE: 8.145 Samples: 2000 Mean MCSE: 5.878 Samples: 3000 Mean MCSE: 5.031 MC samples exceeded maximum stipulated without passing MCSE criterion. Finishing. Depositional Event / Absolute Constraint: D Estimating production, use, and depositional dates for id(s)/type(s) specified. Assigning initial random values (this may take a moment)... Beginning main Gibbs sampler. Will terminate either when MCSE criterion or maximum number of MC samples reached. Note: MCSE stopping criterion is only applied to sequences/constraints, not finds. Samples: 1000 Mean MCSE: 8.468 Samples: 2000 Mean MCSE: 6.304 Samples: 3000 Mean MCSE: 5.166 MC samples exceeded maximum stipulated without passing MCSE criterion. Finishing. Depositional Event / Absolute Constraint: E Estimating production, use, and depositional dates for id(s)/type(s) specified. Assigning initial random values (this may take a moment)... Beginning main Gibbs sampler. Will terminate either when MCSE criterion or maximum number of MC samples reached. Note: MCSE stopping criterion is only applied to sequences/constraints, not finds. Samples: 1000 Mean MCSE: 7.973 Samples: 2000 Mean MCSE: 5.724 Samples: 3000 Mean MCSE: 4.651 MC samples exceeded maximum stipulated without passing MCSE criterion. Finishing. Depositional Event / Absolute Constraint: F Estimating production, use, and depositional dates for id(s)/type(s) specified. Assigning initial random values (this may take a moment)... Beginning main Gibbs sampler. Will terminate either when MCSE criterion or maximum number of MC samples reached. Note: MCSE stopping criterion is only applied to sequences/constraints, not finds. Samples: 1000 Mean MCSE: 8.179 Samples: 2000 Mean MCSE: 5.733 Samples: 3000 Mean MCSE: 4.847 MC samples exceeded maximum stipulated without passing MCSE criterion. Finishing. Depositional Event / Absolute Constraint: G Estimating production, use, and depositional dates for id(s)/type(s) specified. Assigning initial random values (this may take a moment)... Beginning main Gibbs sampler. Will terminate either when MCSE criterion or maximum number of MC samples reached. Note: MCSE stopping criterion is only applied to sequences/constraints, not finds. Samples: 1000 Mean MCSE: 10.91 Samples: 2000 Mean MCSE: 8.036 Samples: 3000 Mean MCSE: 6.16 MC samples exceeded maximum stipulated without passing MCSE criterion. Finishing. Depositional Event / Absolute Constraint: H Estimating production, use, and depositional dates for id(s)/type(s) specified. Assigning initial random values (this may take a moment)... Beginning main Gibbs sampler. Will terminate either when MCSE criterion or maximum number of MC samples reached. Note: MCSE stopping criterion is only applied to sequences/constraints, not finds. Samples: 1000 Mean MCSE: 10.025 Samples: 2000 Mean MCSE: 6.648 Samples: 3000 Mean MCSE: 4.756 MC samples exceeded maximum stipulated without passing MCSE criterion. Finishing. Depositional Event / Absolute Constraint: K Estimating production, use, and depositional dates for id(s)/type(s) specified. Assigning initial random values (this may take a moment)... Beginning main Gibbs sampler. Will terminate either when MCSE criterion or maximum number of MC samples reached. Note: MCSE stopping criterion is only applied to sequences/constraints, not finds. Samples: 1000 Mean MCSE: 6.864 Samples: 2000 Mean MCSE: 5.135 Samples: 3000 Mean MCSE: 4.16 MC samples exceeded maximum stipulated without passing MCSE criterion. Finishing. Depositional Event / Absolute Constraint: I Estimating production, use, and depositional dates for id(s)/type(s) specified. Assigning initial random values (this may take a moment)... Beginning main Gibbs sampler. Will terminate either when MCSE criterion or maximum number of MC samples reached. Note: MCSE stopping criterion is only applied to sequences/constraints, not finds. Samples: 1000 Mean MCSE: 7.879 Samples: 2000 Mean MCSE: 5.251 Samples: 3000 Mean MCSE: 4.669 MC samples exceeded maximum stipulated without passing MCSE criterion. Finishing. Depositional Event / Absolute Constraint: L Estimating production, use, and depositional dates for id(s)/type(s) specified. Assigning initial random values (this may take a moment)... Beginning main Gibbs sampler. Will terminate either when MCSE criterion or maximum number of MC samples reached. Note: MCSE stopping criterion is only applied to sequences/constraints, not finds. Samples: 1000 Mean MCSE: 7.241 Samples: 2000 Mean MCSE: 5.243 Samples: 3000 Mean MCSE: 4.359 MC samples exceeded maximum stipulated without passing MCSE criterion. Finishing. Depositional Event / Absolute Constraint: J Estimating production, use, and depositional dates for id(s)/type(s) specified. Assigning initial random values (this may take a moment)... Beginning main Gibbs sampler. Will terminate either when MCSE criterion or maximum number of MC samples reached. Note: MCSE stopping criterion is only applied to sequences/constraints, not finds. Samples: 1000 Mean MCSE: 24.384 Samples: 2000 Mean MCSE: 17.504 Samples: 3000 Mean MCSE: 15.213 MC samples exceeded maximum stipulated without passing MCSE criterion. Finishing. Depositional Event / Absolute Constraint: M Estimating production, use, and depositional dates for id(s)/type(s) specified. Assigning initial random values (this may take a moment)... Beginning main Gibbs sampler. Will terminate either when MCSE criterion or maximum number of MC samples reached. Note: MCSE stopping criterion is only applied to sequences/constraints, not finds. Samples: 1000 Mean MCSE: 7.132 Samples: 2000 Mean MCSE: 5.36 Samples: 3000 Mean MCSE: 4.218 MC samples exceeded maximum stipulated without passing MCSE criterion. Finishing. Estimation of squared displacement complete. Assigning initial random values (this may take a moment)... Beginning main Gibbs sampler. Will terminate either when MCSE criterion or maximum number of MC samples reached. Samples: 1000 Mean MCSE: 0.011 Samples: 2000 Mean MCSE: 0.008 Samples: 3000 Mean MCSE: 0.006 Samples: 4000 Mean MCSE: 0.006 Samples: 5000 Mean MCSE: 0.005 Samples: 6000 Mean MCSE: 0.004 Samples: 7000 Mean MCSE: 0.004 Samples: 8000 Mean MCSE: 0.003 Samples: 9000 Mean MCSE: 0.003 Samples: 10000 Mean MCSE: 0.003 Samples: 11000 Mean MCSE: 0.003 Samples: 12000 Mean MCSE: 0.003 Samples: 13000 Mean MCSE: 0.003 Samples: 14000 Mean MCSE: 0.003 Samples: 15000 Mean MCSE: 0.003 Samples: 16000 Mean MCSE: 0.003 Samples: 17000 Mean MCSE: 0.003 Samples: 18000 Mean MCSE: 0.002 Samples: 19000 Mean MCSE: 0.002 Samples: 20000 Mean MCSE: 0.002 Samples: 21000 Mean MCSE: 0.002 Samples: 22000 Mean MCSE: 0.002 Samples: 23000 Mean MCSE: 0.002 Samples: 24000 Mean MCSE: 0.002 Samples: 25000 Mean MCSE: 0.002 Samples: 26000 Mean MCSE: 0.002 Samples: 27000 Mean MCSE: 0.002 Samples: 28000 Mean MCSE: 0.002 Samples: 29000 Mean MCSE: 0.002 Samples: 30000 Mean MCSE: 0.002 Samples: 31000 Mean MCSE: 0.002 Samples: 32000 Mean MCSE: 0.002 Samples: 33000 Mean MCSE: 0.002 Samples: 34000 Mean MCSE: 0.002 Samples: 35000 Mean MCSE: 0.002 Samples: 36000 Mean MCSE: 0.002 Samples: 37000 Mean MCSE: 0.002 Samples: 38000 Mean MCSE: 0.002 Samples: 39000 Mean MCSE: 0.002 Samples: 40000 Mean MCSE: 0.002 Samples: 41000 Mean MCSE: 0.002 Samples: 42000 Mean MCSE: 0.002 Samples: 43000 Mean MCSE: 0.001 Samples: 44000 Mean MCSE: 0.001 Samples: 45000 Mean MCSE: 0.001 Samples: 46000 Mean MCSE: 0.002 Samples: 47000 Mean MCSE: 0.001 Samples: 48000 Mean MCSE: 0.001 Samples: 49000 Mean MCSE: 0.002 Samples: 50000 Mean MCSE: 0.001 Samples: 51000 Mean MCSE: 0.001 Samples: 52000 Mean MCSE: 0.001 Samples: 53000 Mean MCSE: 0.001 Samples: 54000 Mean MCSE: 0.001 Samples: 55000 Mean MCSE: 0.001 Samples: 56000 Mean MCSE: 0.001 Samples: 57000 Mean MCSE: 0.001 Samples: 58000 Mean MCSE: 0.001 Samples: 59000 Mean MCSE: 0.001 Samples: 60000 Mean MCSE: 0.001 Samples: 61000 Mean MCSE: 0.001 Samples: 62000 Mean MCSE: 0.001 Samples: 63000 Mean MCSE: 0.001 Samples: 64000 Mean MCSE: 0.001 Samples: 65000 Mean MCSE: 0.001 Samples: 66000 Mean MCSE: 0.001 Samples: 67000 Mean MCSE: 0.001 Samples: 68000 Mean MCSE: 0.001 Samples: 69000 Mean MCSE: 0.001 Samples: 70000 Mean MCSE: 0.001 Samples: 71000 Mean MCSE: 0.001 Samples: 72000 Mean MCSE: 0.001 Samples: 73000 Mean MCSE: 0.001 Samples: 74000 Mean MCSE: 0.001 Samples: 75000 Mean MCSE: 0.001 Samples: 76000 Mean MCSE: 0.001 Samples: 77000 Mean MCSE: 0.001 Samples: 78000 Mean MCSE: 0.001 Samples: 79000 Mean MCSE: 0.001 Samples: 80000 Mean MCSE: 0.001 Samples: 81000 Mean MCSE: 0.001 Samples: 82000 Mean MCSE: 0.001 Samples: 83000 Mean MCSE: 0.001 Samples: 84000 Mean MCSE: 0.001 Samples: 85000 Mean MCSE: 0.001 Samples: 86000 Mean MCSE: 0.001 Samples: 87000 Mean MCSE: 0.001 Samples: 88000 Mean MCSE: 0.001 Samples: 89000 Mean MCSE: 0.001 Samples: 90000 Mean MCSE: 0.001 Samples: 91000 Mean MCSE: 0.001 Samples: 92000 Mean MCSE: 0.001 Samples: 93000 Mean MCSE: 0.001 MCSE criterion passed. Finishing. Assigning initial random values (this may take a moment)... Beginning main Gibbs sampler. Will terminate either when MCSE criterion or maximum number of MC samples reached. 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Samples: 337000 Mean MCSE: 0.001 Samples: 338000 Mean MCSE: 0.001 Samples: 339000 Mean MCSE: 0.001 Samples: 340000 Mean MCSE: 0.001 Samples: 341000 Mean MCSE: 0.001 Samples: 342000 Mean MCSE: 0.001 Samples: 343000 Mean MCSE: 0.001 Samples: 344000 Mean MCSE: 0.001 Samples: 345000 Mean MCSE: 0.001 Samples: 346000 Mean MCSE: 0.001 Samples: 347000 Mean MCSE: 0.001 Samples: 348000 Mean MCSE: 0.001 Samples: 349000 Mean MCSE: 0.001 Samples: 350000 Mean MCSE: 0.001 Samples: 351000 Mean MCSE: 0.001 Samples: 352000 Mean MCSE: 0.001 Samples: 353000 Mean MCSE: 0.001 Samples: 354000 Mean MCSE: 0.001 Samples: 355000 Mean MCSE: 0.001 Samples: 356000 Mean MCSE: 0.001 Samples: 357000 Mean MCSE: 0.001 Samples: 358000 Mean MCSE: 0.001 Samples: 359000 Mean MCSE: 0.001 Samples: 360000 Mean MCSE: 0.001 Samples: 361000 Mean MCSE: 0.001 Samples: 362000 Mean MCSE: 0.001 Samples: 363000 Mean MCSE: 0.001 Samples: 364000 Mean MCSE: 0.001 Samples: 365000 Mean MCSE: 0.001 Samples: 366000 Mean MCSE: 0.001 Samples: 367000 Mean MCSE: 0.001 Samples: 368000 Mean MCSE: 0.001 Samples: 369000 Mean MCSE: 0.001 Samples: 370000 Mean MCSE: 0.001 Samples: 371000 Mean MCSE: 0.001 Samples: 372000 Mean MCSE: 0.001 Samples: 373000 Mean MCSE: 0.001 Samples: 374000 Mean MCSE: 0.001 Samples: 375000 Mean MCSE: 0.001 Samples: 376000 Mean MCSE: 0.001 Samples: 377000 Mean MCSE: 0.001 Samples: 378000 Mean MCSE: 0.001 Samples: 379000 Mean MCSE: 0.001 Samples: 380000 Mean MCSE: 0.001 Samples: 381000 Mean MCSE: 0.001 Samples: 382000 Mean MCSE: 0.001 Samples: 383000 Mean MCSE: 0.001 Samples: 384000 Mean MCSE: 0.001 Samples: 385000 Mean MCSE: 0.001 Samples: 386000 Mean MCSE: 0.001 Samples: 387000 Mean MCSE: 0.001 Samples: 388000 Mean MCSE: 0.001 Samples: 389000 Mean MCSE: 0.001 Samples: 390000 Mean MCSE: 0.001 Samples: 391000 Mean MCSE: 0 MCSE criterion passed. Finishing. Assigning initial random values (this may take a moment)... Beginning main Gibbs sampler. Will terminate either when MCSE criterion or maximum number of MC samples reached. Samples: 1000 Mean MCSE: 0.009 Samples: 2000 Mean MCSE: 0.007 Samples: 3000 Mean MCSE: 0.007 Samples: 4000 Mean MCSE: 0.005 Samples: 5000 Mean MCSE: 0.004 Samples: 6000 Mean MCSE: 0.005 Samples: 7000 Mean MCSE: 0.005 Samples: 8000 Mean MCSE: 0.004 Samples: 9000 Mean MCSE: 0.004 Samples: 10000 Mean MCSE: 0.004 Samples: 11000 Mean MCSE: 0.004 Samples: 12000 Mean MCSE: 0.003 Samples: 13000 Mean MCSE: 0.003 Samples: 14000 Mean MCSE: 0.003 Samples: 15000 Mean MCSE: 0.003 Samples: 16000 Mean MCSE: 0.003 Samples: 17000 Mean MCSE: 0.003 Samples: 18000 Mean MCSE: 0.003 Samples: 19000 Mean MCSE: 0.003 Samples: 20000 Mean MCSE: 0.002 Samples: 21000 Mean MCSE: 0.002 Samples: 22000 Mean MCSE: 0.002 Samples: 23000 Mean MCSE: 0.002 Samples: 24000 Mean MCSE: 0.002 Samples: 25000 Mean MCSE: 0.002 Samples: 26000 Mean MCSE: 0.002 Samples: 27000 Mean MCSE: 0.002 Samples: 28000 Mean MCSE: 0.002 Samples: 29000 Mean MCSE: 0.002 Samples: 30000 Mean MCSE: 0.002 Samples: 31000 Mean MCSE: 0.002 Samples: 32000 Mean MCSE: 0.002 Samples: 33000 Mean MCSE: 0.002 Samples: 34000 Mean MCSE: 0.002 Samples: 35000 Mean MCSE: 0.002 Samples: 36000 Mean MCSE: 0.002 Samples: 37000 Mean MCSE: 0.002 Samples: 38000 Mean MCSE: 0.002 Samples: 39000 Mean MCSE: 0.002 Samples: 40000 Mean MCSE: 0.002 Samples: 41000 Mean MCSE: 0.002 Samples: 42000 Mean MCSE: 0.002 Samples: 43000 Mean MCSE: 0.002 Samples: 44000 Mean MCSE: 0.002 Samples: 45000 Mean MCSE: 0.002 Samples: 46000 Mean MCSE: 0.002 Samples: 47000 Mean MCSE: 0.002 Samples: 48000 Mean MCSE: 0.001 Samples: 49000 Mean MCSE: 0.002 Samples: 50000 Mean MCSE: 0.002 Samples: 51000 Mean MCSE: 0.001 Samples: 52000 Mean MCSE: 0.002 Samples: 53000 Mean MCSE: 0.001 Samples: 54000 Mean MCSE: 0.001 Samples: 55000 Mean MCSE: 0.001 Samples: 56000 Mean MCSE: 0.001 Samples: 57000 Mean MCSE: 0.001 Samples: 58000 Mean MCSE: 0.001 Samples: 59000 Mean MCSE: 0.001 Samples: 60000 Mean MCSE: 0.001 Samples: 61000 Mean MCSE: 0.001 Samples: 62000 Mean MCSE: 0.001 Samples: 63000 Mean MCSE: 0.001 Samples: 64000 Mean MCSE: 0.001 Samples: 65000 Mean MCSE: 0.001 Samples: 66000 Mean MCSE: 0.001 Samples: 67000 Mean MCSE: 0.001 Samples: 68000 Mean MCSE: 0.001 Samples: 69000 Mean MCSE: 0.001 Samples: 70000 Mean MCSE: 0.001 Samples: 71000 Mean MCSE: 0.001 Samples: 72000 Mean MCSE: 0.001 Samples: 73000 Mean MCSE: 0.001 Samples: 74000 Mean MCSE: 0.001 Samples: 75000 Mean MCSE: 0.001 Samples: 76000 Mean MCSE: 0.001 Samples: 77000 Mean MCSE: 0.001 Samples: 78000 Mean MCSE: 0.001 Samples: 79000 Mean MCSE: 0.001 Samples: 80000 Mean MCSE: 0.001 Samples: 81000 Mean MCSE: 0.001 Samples: 82000 Mean MCSE: 0.001 Samples: 83000 Mean MCSE: 0.001 Samples: 84000 Mean MCSE: 0.001 Samples: 85000 Mean MCSE: 0.001 Samples: 86000 Mean MCSE: 0.001 Samples: 87000 Mean MCSE: 0.001 Samples: 88000 Mean MCSE: 0.001 Samples: 89000 Mean MCSE: 0.001 Samples: 90000 Mean MCSE: 0.001 Samples: 91000 Mean MCSE: 0.001 Samples: 92000 Mean MCSE: 0.001 Samples: 93000 Mean MCSE: 0.001 Samples: 94000 Mean MCSE: 0.001 Samples: 95000 Mean MCSE: 0.001 Samples: 96000 Mean MCSE: 0.001 Samples: 97000 Mean MCSE: 0.001 Samples: 98000 Mean MCSE: 0.001 Samples: 99000 Mean MCSE: 0.001 Samples: 100000 Mean MCSE: 0.001 Samples: 101000 Mean MCSE: 0.001 Samples: 102000 Mean MCSE: 0.001 Samples: 103000 Mean MCSE: 0.001 Samples: 104000 Mean MCSE: 0.001 Samples: 105000 Mean MCSE: 0.001 Samples: 106000 Mean MCSE: 0.001 Samples: 107000 Mean MCSE: 0.001 Samples: 108000 Mean MCSE: 0.001 MCSE criterion passed. Finishing. Beginning jackknife/LOO-style routine to compute MSD. This may take a while, depending on the number of events / mcse_crit... Depositional Event / Absolute Constraint: A Assigning initial random values (this may take a moment)... Beginning main Gibbs sampler. Will terminate either when MCSE criterion or maximum number of MC samples reached. Samples: 1000 Mean MCSE: 0.008 Samples: 2000 Mean MCSE: 0.006 Samples: 3000 Mean MCSE: 0.005 Samples: 4000 Mean MCSE: 0.004 Samples: 5000 Mean MCSE: 0.004 Samples: 6000 Mean MCSE: 0.004 Samples: 7000 Mean MCSE: 0.003 Samples: 8000 Mean MCSE: 0.003 Samples: 9000 Mean MCSE: 0.003 Samples: 10000 Mean MCSE: 0.003 Samples: 11000 Mean MCSE: 0.003 Samples: 12000 Mean MCSE: 0.003 Samples: 13000 Mean MCSE: 0.003 Samples: 14000 Mean MCSE: 0.003 Samples: 15000 Mean MCSE: 0.002 Samples: 16000 Mean MCSE: 0.002 Samples: 17000 Mean MCSE: 0.002 Samples: 18000 Mean MCSE: 0.002 Samples: 19000 Mean MCSE: 0.002 Samples: 20000 Mean MCSE: 0.002 Samples: 21000 Mean MCSE: 0.002 Samples: 22000 Mean MCSE: 0.002 Samples: 23000 Mean MCSE: 0.002 Samples: 24000 Mean MCSE: 0.002 Samples: 25000 Mean MCSE: 0.002 Samples: 26000 Mean MCSE: 0.002 Samples: 27000 Mean MCSE: 0.002 Samples: 28000 Mean MCSE: 0.002 Samples: 29000 Mean MCSE: 0.002 Samples: 30000 Mean MCSE: 0.002 Samples: 31000 Mean MCSE: 0.002 Samples: 32000 Mean MCSE: 0.002 Samples: 33000 Mean MCSE: 0.002 Samples: 34000 Mean MCSE: 0.002 Samples: 35000 Mean MCSE: 0.002 Samples: 36000 Mean MCSE: 0.001 Samples: 37000 Mean MCSE: 0.002 Samples: 38000 Mean MCSE: 0.001 Samples: 39000 Mean MCSE: 0.001 Samples: 40000 Mean MCSE: 0.002 Samples: 41000 Mean MCSE: 0.002 Samples: 42000 Mean MCSE: 0.002 Samples: 43000 Mean MCSE: 0.001 Samples: 44000 Mean MCSE: 0.001 Samples: 45000 Mean MCSE: 0.001 Samples: 46000 Mean MCSE: 0.001 Samples: 47000 Mean MCSE: 0.001 Samples: 48000 Mean MCSE: 0.001 Samples: 49000 Mean MCSE: 0.001 Samples: 50000 Mean MCSE: 0.001 Samples: 51000 Mean MCSE: 0.001 Samples: 52000 Mean MCSE: 0.001 Samples: 53000 Mean MCSE: 0.001 Samples: 54000 Mean MCSE: 0.001 Samples: 55000 Mean MCSE: 0.001 Samples: 56000 Mean MCSE: 0.001 Samples: 57000 Mean MCSE: 0.001 Samples: 58000 Mean MCSE: 0.001 Samples: 59000 Mean MCSE: 0.001 Samples: 60000 Mean MCSE: 0.001 Samples: 61000 Mean MCSE: 0.001 Samples: 62000 Mean MCSE: 0.001 Samples: 63000 Mean MCSE: 0.001 Samples: 64000 Mean MCSE: 0.001 Samples: 65000 Mean MCSE: 0.001 Samples: 66000 Mean MCSE: 0.001 Samples: 67000 Mean MCSE: 0.001 Samples: 68000 Mean MCSE: 0.001 Samples: 69000 Mean MCSE: 0.001 Samples: 70000 Mean MCSE: 0.001 Samples: 71000 Mean MCSE: 0.001 Samples: 72000 Mean MCSE: 0.001 Samples: 73000 Mean MCSE: 0.001 Samples: 74000 Mean MCSE: 0.001 Samples: 75000 Mean MCSE: 0.001 Samples: 76000 Mean MCSE: 0.001 Samples: 77000 Mean MCSE: 0.001 Samples: 78000 Mean MCSE: 0.001 Samples: 79000 Mean MCSE: 0.001 Samples: 80000 Mean MCSE: 0.001 Samples: 81000 Mean MCSE: 0.001 Samples: 82000 Mean MCSE: 0.001 Samples: 83000 Mean MCSE: 0.001 Samples: 84000 Mean MCSE: 0.001 Samples: 85000 Mean MCSE: 0.001 Samples: 86000 Mean MCSE: 0.001 Samples: 87000 Mean MCSE: 0.001 Samples: 88000 Mean MCSE: 0.001 Samples: 89000 Mean MCSE: 0.001 Samples: 90000 Mean MCSE: 0.001 Samples: 91000 Mean MCSE: 0.001 Samples: 92000 Mean MCSE: 0.001 Samples: 93000 Mean MCSE: 0.001 Samples: 94000 Mean MCSE: 0.001 Samples: 95000 Mean MCSE: 0.001 Samples: 96000 Mean MCSE: 0.001 Samples: 97000 Mean MCSE: 0.001 Samples: 98000 Mean MCSE: 0.001 MCSE criterion passed. Finishing. Depositional Event / Absolute Constraint: B Assigning initial random values (this may take a moment)... Beginning main Gibbs sampler. Will terminate either when MCSE criterion or maximum number of MC samples reached. Samples: 1000 Mean MCSE: 0.007 Samples: 2000 Mean MCSE: 0.006 Samples: 3000 Mean MCSE: 0.005 Samples: 4000 Mean MCSE: 0.004 Samples: 5000 Mean MCSE: 0.004 Samples: 6000 Mean MCSE: 0.004 Samples: 7000 Mean MCSE: 0.003 Samples: 8000 Mean MCSE: 0.003 Samples: 9000 Mean MCSE: 0.003 Samples: 10000 Mean MCSE: 0.003 Samples: 11000 Mean MCSE: 0.003 Samples: 12000 Mean MCSE: 0.002 Samples: 13000 Mean MCSE: 0.003 Samples: 14000 Mean MCSE: 0.002 Samples: 15000 Mean MCSE: 0.002 Samples: 16000 Mean MCSE: 0.002 Samples: 17000 Mean MCSE: 0.002 Samples: 18000 Mean MCSE: 0.002 Samples: 19000 Mean MCSE: 0.002 Samples: 20000 Mean MCSE: 0.002 Samples: 21000 Mean MCSE: 0.002 Samples: 22000 Mean MCSE: 0.002 Samples: 23000 Mean MCSE: 0.002 Samples: 24000 Mean MCSE: 0.002 Samples: 25000 Mean MCSE: 0.002 Samples: 26000 Mean MCSE: 0.002 Samples: 27000 Mean MCSE: 0.002 Samples: 28000 Mean MCSE: 0.002 Samples: 29000 Mean MCSE: 0.002 Samples: 30000 Mean MCSE: 0.002 Samples: 31000 Mean MCSE: 0.002 Samples: 32000 Mean MCSE: 0.002 Samples: 33000 Mean MCSE: 0.002 Samples: 34000 Mean MCSE: 0.002 Samples: 35000 Mean MCSE: 0.002 Samples: 36000 Mean MCSE: 0.002 Samples: 37000 Mean MCSE: 0.001 Samples: 38000 Mean MCSE: 0.002 Samples: 39000 Mean MCSE: 0.002 Samples: 40000 Mean MCSE: 0.002 Samples: 41000 Mean MCSE: 0.001 Samples: 42000 Mean MCSE: 0.002 Samples: 43000 Mean MCSE: 0.001 Samples: 44000 Mean MCSE: 0.001 Samples: 45000 Mean MCSE: 0.001 Samples: 46000 Mean MCSE: 0.001 Samples: 47000 Mean MCSE: 0.001 Samples: 48000 Mean MCSE: 0.001 Samples: 49000 Mean MCSE: 0.001 Samples: 50000 Mean MCSE: 0.001 Samples: 51000 Mean MCSE: 0.001 Samples: 52000 Mean MCSE: 0.001 Samples: 53000 Mean MCSE: 0.001 Samples: 54000 Mean MCSE: 0.001 Samples: 55000 Mean MCSE: 0.001 Samples: 56000 Mean MCSE: 0.001 Samples: 57000 Mean MCSE: 0.001 Samples: 58000 Mean MCSE: 0.001 Samples: 59000 Mean MCSE: 0.001 Samples: 60000 Mean MCSE: 0.001 Samples: 61000 Mean MCSE: 0.001 Samples: 62000 Mean MCSE: 0.001 Samples: 63000 Mean MCSE: 0.001 Samples: 64000 Mean MCSE: 0.001 Samples: 65000 Mean MCSE: 0.001 Samples: 66000 Mean MCSE: 0.001 Samples: 67000 Mean MCSE: 0.001 Samples: 68000 Mean MCSE: 0.001 Samples: 69000 Mean MCSE: 0.001 Samples: 70000 Mean MCSE: 0.001 Samples: 71000 Mean MCSE: 0.001 Samples: 72000 Mean MCSE: 0.001 Samples: 73000 Mean MCSE: 0.001 Samples: 74000 Mean MCSE: 0.001 Samples: 75000 Mean MCSE: 0.001 Samples: 76000 Mean MCSE: 0.001 Samples: 77000 Mean MCSE: 0.001 Samples: 78000 Mean MCSE: 0.001 Samples: 79000 Mean MCSE: 0.001 Samples: 80000 Mean MCSE: 0.001 Samples: 81000 Mean MCSE: 0.001 Samples: 82000 Mean MCSE: 0.001 Samples: 83000 Mean MCSE: 0.001 Samples: 84000 Mean MCSE: 0.001 Samples: 85000 Mean MCSE: 0.001 Samples: 86000 Mean MCSE: 0.001 Samples: 87000 Mean MCSE: 0.001 Samples: 88000 Mean MCSE: 0.001 Samples: 89000 Mean MCSE: 0.001 Samples: 90000 Mean MCSE: 0.001 Samples: 91000 Mean MCSE: 0.001 Samples: 92000 Mean MCSE: 0.001 MCSE criterion passed. Finishing. Depositional Event / Absolute Constraint: C Assigning initial random values (this may take a moment)... Beginning main Gibbs sampler. Will terminate either when MCSE criterion or maximum number of MC samples reached. Samples: 1000 Mean MCSE: 0.009 Samples: 2000 Mean MCSE: 0.007 Samples: 3000 Mean MCSE: 0.006 Samples: 4000 Mean MCSE: 0.005 Samples: 5000 Mean MCSE: 0.004 Samples: 6000 Mean MCSE: 0.004 Samples: 7000 Mean MCSE: 0.004 Samples: 8000 Mean MCSE: 0.004 Samples: 9000 Mean MCSE: 0.003 Samples: 10000 Mean MCSE: 0.003 Samples: 11000 Mean MCSE: 0.003 Samples: 12000 Mean MCSE: 0.003 Samples: 13000 Mean MCSE: 0.003 Samples: 14000 Mean MCSE: 0.003 Samples: 15000 Mean MCSE: 0.003 Samples: 16000 Mean MCSE: 0.003 Samples: 17000 Mean MCSE: 0.002 Samples: 18000 Mean MCSE: 0.002 Samples: 19000 Mean MCSE: 0.002 Samples: 20000 Mean MCSE: 0.002 Samples: 21000 Mean MCSE: 0.002 Samples: 22000 Mean MCSE: 0.002 Samples: 23000 Mean MCSE: 0.002 Samples: 24000 Mean MCSE: 0.002 Samples: 25000 Mean MCSE: 0.002 Samples: 26000 Mean MCSE: 0.002 Samples: 27000 Mean MCSE: 0.002 Samples: 28000 Mean MCSE: 0.002 Samples: 29000 Mean MCSE: 0.002 Samples: 30000 Mean MCSE: 0.002 Samples: 31000 Mean MCSE: 0.002 Samples: 32000 Mean MCSE: 0.002 Samples: 33000 Mean MCSE: 0.002 Samples: 34000 Mean MCSE: 0.002 Samples: 35000 Mean MCSE: 0.002 Samples: 36000 Mean MCSE: 0.002 Samples: 37000 Mean MCSE: 0.002 Samples: 38000 Mean MCSE: 0.002 Samples: 39000 Mean MCSE: 0.002 Samples: 40000 Mean MCSE: 0.001 Samples: 41000 Mean MCSE: 0.001 Samples: 42000 Mean MCSE: 0.001 Samples: 43000 Mean MCSE: 0.002 Samples: 44000 Mean MCSE: 0.001 Samples: 45000 Mean MCSE: 0.001 Samples: 46000 Mean MCSE: 0.001 Samples: 47000 Mean MCSE: 0.001 Samples: 48000 Mean MCSE: 0.001 Samples: 49000 Mean MCSE: 0.001 Samples: 50000 Mean MCSE: 0.001 Samples: 51000 Mean MCSE: 0.001 Samples: 52000 Mean MCSE: 0.001 Samples: 53000 Mean MCSE: 0.001 Samples: 54000 Mean MCSE: 0.001 Samples: 55000 Mean MCSE: 0.001 Samples: 56000 Mean MCSE: 0.001 Samples: 57000 Mean MCSE: 0.001 Samples: 58000 Mean MCSE: 0.001 Samples: 59000 Mean MCSE: 0.001 Samples: 60000 Mean MCSE: 0.001 Samples: 61000 Mean MCSE: 0.001 Samples: 62000 Mean MCSE: 0.001 Samples: 63000 Mean MCSE: 0.001 Samples: 64000 Mean MCSE: 0.001 Samples: 65000 Mean MCSE: 0.001 Samples: 66000 Mean MCSE: 0.001 Samples: 67000 Mean MCSE: 0.001 Samples: 68000 Mean MCSE: 0.001 Samples: 69000 Mean MCSE: 0.001 Samples: 70000 Mean MCSE: 0.001 Samples: 71000 Mean MCSE: 0.001 Samples: 72000 Mean MCSE: 0.001 Samples: 73000 Mean MCSE: 0.001 Samples: 74000 Mean MCSE: 0.001 Samples: 75000 Mean MCSE: 0.001 Samples: 76000 Mean MCSE: 0.001 Samples: 77000 Mean MCSE: 0.001 Samples: 78000 Mean MCSE: 0.001 Samples: 79000 Mean MCSE: 0.001 Samples: 80000 Mean MCSE: 0.001 Samples: 81000 Mean MCSE: 0.001 Samples: 82000 Mean MCSE: 0.001 Samples: 83000 Mean MCSE: 0.001 Samples: 84000 Mean MCSE: 0.001 Samples: 85000 Mean MCSE: 0.001 MCSE criterion passed. Finishing. Depositional Event / Absolute Constraint: tpq_default Assigning initial random values (this may take a moment)... Beginning main Gibbs sampler. Will terminate either when MCSE criterion or maximum number of MC samples reached. Samples: 1000 Mean MCSE: 0.01 Samples: 2000 Mean MCSE: 0.007 Samples: 3000 Mean MCSE: 0.006 Samples: 4000 Mean MCSE: 0.005 Samples: 5000 Mean MCSE: 0.004 Samples: 6000 Mean MCSE: 0.004 Samples: 7000 Mean MCSE: 0.004 Samples: 8000 Mean MCSE: 0.003 Samples: 9000 Mean MCSE: 0.003 Samples: 10000 Mean MCSE: 0.003 Samples: 11000 Mean MCSE: 0.003 Samples: 12000 Mean MCSE: 0.003 Samples: 13000 Mean MCSE: 0.003 Samples: 14000 Mean MCSE: 0.003 Samples: 15000 Mean MCSE: 0.003 Samples: 16000 Mean MCSE: 0.002 Samples: 17000 Mean MCSE: 0.003 Samples: 18000 Mean MCSE: 0.003 Samples: 19000 Mean MCSE: 0.002 Samples: 20000 Mean MCSE: 0.002 Samples: 21000 Mean MCSE: 0.002 Samples: 22000 Mean MCSE: 0.002 Samples: 23000 Mean MCSE: 0.002 Samples: 24000 Mean MCSE: 0.002 Samples: 25000 Mean MCSE: 0.002 Samples: 26000 Mean MCSE: 0.002 Samples: 27000 Mean MCSE: 0.002 Samples: 28000 Mean MCSE: 0.002 Samples: 29000 Mean MCSE: 0.002 Samples: 30000 Mean MCSE: 0.002 Samples: 31000 Mean MCSE: 0.002 Samples: 32000 Mean MCSE: 0.002 Samples: 33000 Mean MCSE: 0.002 Samples: 34000 Mean MCSE: 0.002 Samples: 35000 Mean MCSE: 0.002 Samples: 36000 Mean MCSE: 0.002 Samples: 37000 Mean MCSE: 0.002 Samples: 38000 Mean MCSE: 0.002 Samples: 39000 Mean MCSE: 0.002 Samples: 40000 Mean MCSE: 0.002 Samples: 41000 Mean MCSE: 0.002 Samples: 42000 Mean MCSE: 0.002 Samples: 43000 Mean MCSE: 0.002 Samples: 44000 Mean MCSE: 0.002 Samples: 45000 Mean MCSE: 0.002 Samples: 46000 Mean MCSE: 0.002 Samples: 47000 Mean MCSE: 0.002 Samples: 48000 Mean MCSE: 0.001 Samples: 49000 Mean MCSE: 0.001 Samples: 50000 Mean MCSE: 0.002 Samples: 51000 Mean MCSE: 0.001 Samples: 52000 Mean MCSE: 0.001 Samples: 53000 Mean MCSE: 0.001 Samples: 54000 Mean MCSE: 0.001 Samples: 55000 Mean MCSE: 0.001 Samples: 56000 Mean MCSE: 0.001 Samples: 57000 Mean MCSE: 0.001 Samples: 58000 Mean MCSE: 0.001 Samples: 59000 Mean MCSE: 0.001 Samples: 60000 Mean MCSE: 0.001 Samples: 61000 Mean MCSE: 0.001 Samples: 62000 Mean MCSE: 0.001 Samples: 63000 Mean MCSE: 0.001 Samples: 64000 Mean MCSE: 0.001 Samples: 65000 Mean MCSE: 0.001 Samples: 66000 Mean MCSE: 0.001 Samples: 67000 Mean MCSE: 0.001 Samples: 68000 Mean MCSE: 0.001 Samples: 69000 Mean MCSE: 0.001 Samples: 70000 Mean MCSE: 0.001 Samples: 71000 Mean MCSE: 0.001 Samples: 72000 Mean MCSE: 0.001 Samples: 73000 Mean MCSE: 0.001 Samples: 74000 Mean MCSE: 0.001 Samples: 75000 Mean MCSE: 0.001 Samples: 76000 Mean MCSE: 0.001 Samples: 77000 Mean MCSE: 0.001 Samples: 78000 Mean MCSE: 0.001 Samples: 79000 Mean MCSE: 0.001 Samples: 80000 Mean MCSE: 0.001 Samples: 81000 Mean MCSE: 0.001 Samples: 82000 Mean MCSE: 0.001 Samples: 83000 Mean MCSE: 0.001 Samples: 84000 Mean MCSE: 0.001 Samples: 85000 Mean MCSE: 0.001 Samples: 86000 Mean MCSE: 0.001 Samples: 87000 Mean MCSE: 0.001 Samples: 88000 Mean MCSE: 0.001 Samples: 89000 Mean MCSE: 0.001 Samples: 90000 Mean MCSE: 0.001 Samples: 91000 Mean MCSE: 0.001 Samples: 92000 Mean MCSE: 0.001 Samples: 93000 Mean MCSE: 0.001 Samples: 94000 Mean MCSE: 0.001 Samples: 95000 Mean MCSE: 0.001 Samples: 96000 Mean MCSE: 0.001 Samples: 97000 Mean MCSE: 0.001 Samples: 98000 Mean MCSE: 0.001 Samples: 99000 Mean MCSE: 0.001 Samples: 100000 Mean MCSE: 0.001 Samples: 101000 Mean MCSE: 0.001 Samples: 102000 Mean MCSE: 0.001 Samples: 103000 Mean MCSE: 0.001 Samples: 104000 Mean MCSE: 0.001 MCSE criterion passed. Finishing. Depositional Event / Absolute Constraint: taq_default Assigning initial random values (this may take a moment)... Beginning main Gibbs sampler. Will terminate either when MCSE criterion or maximum number of MC samples reached. Samples: 1000 Mean MCSE: 0.01 Samples: 2000 Mean MCSE: 0.006 Samples: 3000 Mean MCSE: 0.005 Samples: 4000 Mean MCSE: 0.004 Samples: 5000 Mean MCSE: 0.004 Samples: 6000 Mean MCSE: 0.004 Samples: 7000 Mean MCSE: 0.004 Samples: 8000 Mean MCSE: 0.003 Samples: 9000 Mean MCSE: 0.003 Samples: 10000 Mean MCSE: 0.003 Samples: 11000 Mean MCSE: 0.003 Samples: 12000 Mean MCSE: 0.003 Samples: 13000 Mean MCSE: 0.003 Samples: 14000 Mean MCSE: 0.003 Samples: 15000 Mean MCSE: 0.002 Samples: 16000 Mean MCSE: 0.002 Samples: 17000 Mean MCSE: 0.002 Samples: 18000 Mean MCSE: 0.002 Samples: 19000 Mean MCSE: 0.002 Samples: 20000 Mean MCSE: 0.002 Samples: 21000 Mean MCSE: 0.002 Samples: 22000 Mean MCSE: 0.002 Samples: 23000 Mean MCSE: 0.002 Samples: 24000 Mean MCSE: 0.002 Samples: 25000 Mean MCSE: 0.002 Samples: 26000 Mean MCSE: 0.002 Samples: 27000 Mean MCSE: 0.002 Samples: 28000 Mean MCSE: 0.002 Samples: 29000 Mean MCSE: 0.002 Samples: 30000 Mean MCSE: 0.002 Samples: 31000 Mean MCSE: 0.002 Samples: 32000 Mean MCSE: 0.002 Samples: 33000 Mean MCSE: 0.002 Samples: 34000 Mean MCSE: 0.002 Samples: 35000 Mean MCSE: 0.002 Samples: 36000 Mean MCSE: 0.002 Samples: 37000 Mean MCSE: 0.002 Samples: 38000 Mean MCSE: 0.002 Samples: 39000 Mean MCSE: 0.002 Samples: 40000 Mean MCSE: 0.002 Samples: 41000 Mean MCSE: 0.002 Samples: 42000 Mean MCSE: 0.002 Samples: 43000 Mean MCSE: 0.002 Samples: 44000 Mean MCSE: 0.002 Samples: 45000 Mean MCSE: 0.001 Samples: 46000 Mean MCSE: 0.002 Samples: 47000 Mean MCSE: 0.002 Samples: 48000 Mean MCSE: 0.001 Samples: 49000 Mean MCSE: 0.001 Samples: 50000 Mean MCSE: 0.001 Samples: 51000 Mean MCSE: 0.001 Samples: 52000 Mean MCSE: 0.001 Samples: 53000 Mean MCSE: 0.001 Samples: 54000 Mean MCSE: 0.001 Samples: 55000 Mean MCSE: 0.001 Samples: 56000 Mean MCSE: 0.001 Samples: 57000 Mean MCSE: 0.001 Samples: 58000 Mean MCSE: 0.001 Samples: 59000 Mean MCSE: 0.001 Samples: 60000 Mean MCSE: 0.001 Samples: 61000 Mean MCSE: 0.001 Samples: 62000 Mean MCSE: 0.001 Samples: 63000 Mean MCSE: 0.001 Samples: 64000 Mean MCSE: 0.001 Samples: 65000 Mean MCSE: 0.001 Samples: 66000 Mean MCSE: 0.001 Samples: 67000 Mean MCSE: 0.001 Samples: 68000 Mean MCSE: 0.001 Samples: 69000 Mean MCSE: 0.001 Samples: 70000 Mean MCSE: 0.001 Samples: 71000 Mean MCSE: 0.001 Samples: 72000 Mean MCSE: 0.001 Samples: 73000 Mean MCSE: 0.001 Samples: 74000 Mean MCSE: 0.001 Samples: 75000 Mean MCSE: 0.001 Samples: 76000 Mean MCSE: 0.001 Samples: 77000 Mean MCSE: 0.001 Samples: 78000 Mean MCSE: 0.001 Samples: 79000 Mean MCSE: 0.001 Samples: 80000 Mean MCSE: 0.001 Samples: 81000 Mean MCSE: 0.001 Samples: 82000 Mean MCSE: 0.001 Samples: 83000 Mean MCSE: 0.001 Samples: 84000 Mean MCSE: 0.001 Samples: 85000 Mean MCSE: 0.001 Samples: 86000 Mean MCSE: 0.001 Samples: 87000 Mean MCSE: 0.001 Samples: 88000 Mean MCSE: 0.001 Samples: 89000 Mean MCSE: 0.001 Samples: 90000 Mean MCSE: 0.001 Samples: 91000 Mean MCSE: 0.001 Samples: 92000 Mean MCSE: 0.001 Samples: 93000 Mean MCSE: 0.001 Samples: 94000 Mean MCSE: 0.001 Samples: 95000 Mean MCSE: 0.001 Samples: 96000 Mean MCSE: 0.001 Samples: 97000 Mean MCSE: 0.001 Samples: 98000 Mean MCSE: 0.001 Samples: 99000 Mean MCSE: 0.001 Samples: 100000 Mean MCSE: 0.001 Samples: 101000 Mean MCSE: 0.001 Samples: 102000 Mean MCSE: 0.001 Samples: 103000 Mean MCSE: 0.001 MCSE criterion passed. Finishing. Estimation of MSD complete. Assigning initial random values (this may take a moment)... Beginning main Gibbs sampler. Will terminate either when MCSE criterion or maximum number of MC samples reached. Samples: 1000 Mean MCSE: 0.008 Samples: 2000 Mean MCSE: 0.007 Samples: 3000 Mean MCSE: 0.006 Samples: 4000 Mean MCSE: 0.005 Samples: 5000 Mean MCSE: 0.005 Samples: 6000 Mean MCSE: 0.004 Samples: 7000 Mean MCSE: 0.004 Samples: 8000 Mean MCSE: 0.004 Samples: 9000 Mean MCSE: 0.004 Samples: 10000 Mean MCSE: 0.003 Samples: 11000 Mean MCSE: 0.003 Samples: 12000 Mean MCSE: 0.003 Samples: 13000 Mean MCSE: 0.003 Samples: 14000 Mean MCSE: 0.003 Samples: 15000 Mean MCSE: 0.003 Samples: 16000 Mean MCSE: 0.003 Samples: 17000 Mean MCSE: 0.003 Samples: 18000 Mean MCSE: 0.003 Samples: 19000 Mean MCSE: 0.002 Samples: 20000 Mean MCSE: 0.002 Samples: 21000 Mean MCSE: 0.002 Samples: 22000 Mean MCSE: 0.002 Samples: 23000 Mean MCSE: 0.002 Samples: 24000 Mean MCSE: 0.002 Samples: 25000 Mean MCSE: 0.002 Samples: 26000 Mean MCSE: 0.002 Samples: 27000 Mean MCSE: 0.002 Samples: 28000 Mean MCSE: 0.002 Samples: 29000 Mean MCSE: 0.002 Samples: 30000 Mean MCSE: 0.002 Samples: 31000 Mean MCSE: 0.002 Samples: 32000 Mean MCSE: 0.002 Samples: 33000 Mean MCSE: 0.002 Samples: 34000 Mean MCSE: 0.002 Samples: 35000 Mean MCSE: 0.002 Samples: 36000 Mean MCSE: 0.002 Samples: 37000 Mean MCSE: 0.002 Samples: 38000 Mean MCSE: 0.002 Samples: 39000 Mean MCSE: 0.002 Samples: 40000 Mean MCSE: 0.002 Samples: 41000 Mean MCSE: 0.002 Samples: 42000 Mean MCSE: 0.002 Samples: 43000 Mean MCSE: 0.002 Samples: 44000 Mean MCSE: 0.002 Samples: 45000 Mean MCSE: 0.002 Samples: 46000 Mean MCSE: 0.001 Samples: 47000 Mean MCSE: 0.001 Samples: 48000 Mean MCSE: 0.001 Samples: 49000 Mean MCSE: 0.001 Samples: 50000 Mean MCSE: 0.001 Samples: 51000 Mean MCSE: 0.001 Samples: 52000 Mean MCSE: 0.001 Samples: 53000 Mean MCSE: 0.001 Samples: 54000 Mean MCSE: 0.001 Samples: 55000 Mean MCSE: 0.001 Samples: 56000 Mean MCSE: 0.001 Samples: 57000 Mean MCSE: 0.001 Samples: 58000 Mean MCSE: 0.001 Samples: 59000 Mean MCSE: 0.001 Samples: 60000 Mean MCSE: 0.001 Samples: 61000 Mean MCSE: 0.001 Samples: 62000 Mean MCSE: 0.001 Samples: 63000 Mean MCSE: 0.001 Samples: 64000 Mean MCSE: 0.001 Samples: 65000 Mean MCSE: 0.001 Samples: 66000 Mean MCSE: 0.001 Samples: 67000 Mean MCSE: 0.001 Samples: 68000 Mean MCSE: 0.001 Samples: 69000 Mean MCSE: 0.001 Samples: 70000 Mean MCSE: 0.001 Samples: 71000 Mean MCSE: 0.001 Samples: 72000 Mean MCSE: 0.001 Samples: 73000 Mean MCSE: 0.001 Samples: 74000 Mean MCSE: 0.001 Samples: 75000 Mean MCSE: 0.001 Samples: 76000 Mean MCSE: 0.001 Samples: 77000 Mean MCSE: 0.001 Samples: 78000 Mean MCSE: 0.001 Samples: 79000 Mean MCSE: 0.001 Samples: 80000 Mean MCSE: 0.001 Samples: 81000 Mean MCSE: 0.001 Samples: 82000 Mean MCSE: 0.001 Samples: 83000 Mean MCSE: 0.001 Samples: 84000 Mean MCSE: 0.001 Samples: 85000 Mean MCSE: 0.001 Samples: 86000 Mean MCSE: 0.001 Samples: 87000 Mean MCSE: 0.001 Samples: 88000 Mean MCSE: 0.001 Samples: 89000 Mean MCSE: 0.001 Samples: 90000 Mean MCSE: 0.001 Samples: 91000 Mean MCSE: 0.001 Samples: 92000 Mean MCSE: 0.001 Samples: 93000 Mean MCSE: 0.001 Samples: 94000 Mean MCSE: 0.001 Samples: 95000 Mean MCSE: 0.001 Samples: 96000 Mean MCSE: 0.001 Samples: 97000 Mean MCSE: 0.001 Samples: 98000 Mean MCSE: 0.001 Samples: 99000 Mean MCSE: 0.001 Samples: 100000 Mean MCSE: 0.001 Samples: 101000 Mean MCSE: 0.001 Samples: 102000 Mean MCSE: 0.001 Samples: 103000 Mean MCSE: 0.001 Samples: 104000 Mean MCSE: 0.001 Samples: 105000 Mean MCSE: 0.001 Samples: 106000 Mean MCSE: 0.001 Samples: 107000 Mean MCSE: 0.001 MCSE criterion passed. Finishing. Beginning jackknife/LOO-style routine to compute squared displacement. This may take a while, depending on the number of events / mcse_crit... Depositional Event / Absolute Constraint: A Assigning initial random values (this may take a moment)... Beginning main Gibbs sampler. Will terminate either when MCSE criterion or maximum number of MC samples reached. Samples: 1000 Mean MCSE: 0.008 Samples: 2000 Mean MCSE: 0.006 Samples: 3000 Mean MCSE: 0.005 Samples: 4000 Mean MCSE: 0.004 Samples: 5000 Mean MCSE: 0.004 Samples: 6000 Mean MCSE: 0.004 Samples: 7000 Mean MCSE: 0.004 Samples: 8000 Mean MCSE: 0.003 Samples: 9000 Mean MCSE: 0.003 Samples: 10000 Mean MCSE: 0.003 Samples: 11000 Mean MCSE: 0.003 Samples: 12000 Mean MCSE: 0.003 Samples: 13000 Mean MCSE: 0.003 Samples: 14000 Mean MCSE: 0.003 Samples: 15000 Mean MCSE: 0.002 Samples: 16000 Mean MCSE: 0.002 Samples: 17000 Mean MCSE: 0.002 Samples: 18000 Mean MCSE: 0.002 Samples: 19000 Mean MCSE: 0.002 Samples: 20000 Mean MCSE: 0.002 Samples: 21000 Mean MCSE: 0.002 Samples: 22000 Mean MCSE: 0.002 Samples: 23000 Mean MCSE: 0.002 Samples: 24000 Mean MCSE: 0.002 Samples: 25000 Mean MCSE: 0.002 Samples: 26000 Mean MCSE: 0.002 Samples: 27000 Mean MCSE: 0.002 Samples: 28000 Mean MCSE: 0.002 Samples: 29000 Mean MCSE: 0.002 Samples: 30000 Mean MCSE: 0.002 Samples: 31000 Mean MCSE: 0.002 Samples: 32000 Mean MCSE: 0.002 Samples: 33000 Mean MCSE: 0.002 Samples: 34000 Mean MCSE: 0.002 Samples: 35000 Mean MCSE: 0.002 Samples: 36000 Mean MCSE: 0.002 Samples: 37000 Mean MCSE: 0.002 Samples: 38000 Mean MCSE: 0.002 Samples: 39000 Mean MCSE: 0.002 Samples: 40000 Mean MCSE: 0.001 Samples: 41000 Mean MCSE: 0.001 Samples: 42000 Mean MCSE: 0.002 Samples: 43000 Mean MCSE: 0.001 Samples: 44000 Mean MCSE: 0.001 Samples: 45000 Mean MCSE: 0.001 Samples: 46000 Mean MCSE: 0.001 Samples: 47000 Mean MCSE: 0.001 Samples: 48000 Mean MCSE: 0.001 Samples: 49000 Mean MCSE: 0.001 Samples: 50000 Mean MCSE: 0.001 Samples: 51000 Mean MCSE: 0.001 Samples: 52000 Mean MCSE: 0.001 Samples: 53000 Mean MCSE: 0.001 Samples: 54000 Mean MCSE: 0.001 Samples: 55000 Mean MCSE: 0.001 Samples: 56000 Mean MCSE: 0.001 Samples: 57000 Mean MCSE: 0.001 Samples: 58000 Mean MCSE: 0.001 Samples: 59000 Mean MCSE: 0.001 Samples: 60000 Mean MCSE: 0.001 Samples: 61000 Mean MCSE: 0.001 Samples: 62000 Mean MCSE: 0.001 Samples: 63000 Mean MCSE: 0.001 Samples: 64000 Mean MCSE: 0.001 Samples: 65000 Mean MCSE: 0.001 Samples: 66000 Mean MCSE: 0.001 Samples: 67000 Mean MCSE: 0.001 Samples: 68000 Mean MCSE: 0.001 Samples: 69000 Mean MCSE: 0.001 Samples: 70000 Mean MCSE: 0.001 Samples: 71000 Mean MCSE: 0.001 Samples: 72000 Mean MCSE: 0.001 Samples: 73000 Mean MCSE: 0.001 Samples: 74000 Mean MCSE: 0.001 Samples: 75000 Mean MCSE: 0.001 Samples: 76000 Mean MCSE: 0.001 Samples: 77000 Mean MCSE: 0.001 Samples: 78000 Mean MCSE: 0.001 Samples: 79000 Mean MCSE: 0.001 Samples: 80000 Mean MCSE: 0.001 Samples: 81000 Mean MCSE: 0.001 Samples: 82000 Mean MCSE: 0.001 Samples: 83000 Mean MCSE: 0.001 Samples: 84000 Mean MCSE: 0.001 Samples: 85000 Mean MCSE: 0.001 Samples: 86000 Mean MCSE: 0.001 Samples: 87000 Mean MCSE: 0.001 Samples: 88000 Mean MCSE: 0.001 Samples: 89000 Mean MCSE: 0.001 Samples: 90000 Mean MCSE: 0.001 Samples: 91000 Mean MCSE: 0.001 Samples: 92000 Mean MCSE: 0.001 MCSE criterion passed. Finishing. Depositional Event / Absolute Constraint: C Assigning initial random values (this may take a moment)... Beginning main Gibbs sampler. Will terminate either when MCSE criterion or maximum number of MC samples reached. Samples: 1000 Mean MCSE: 0.01 Samples: 2000 Mean MCSE: 0.007 Samples: 3000 Mean MCSE: 0.006 Samples: 4000 Mean MCSE: 0.005 Samples: 5000 Mean MCSE: 0.005 Samples: 6000 Mean MCSE: 0.004 Samples: 7000 Mean MCSE: 0.004 Samples: 8000 Mean MCSE: 0.003 Samples: 9000 Mean MCSE: 0.003 Samples: 10000 Mean MCSE: 0.003 Samples: 11000 Mean MCSE: 0.003 Samples: 12000 Mean MCSE: 0.003 Samples: 13000 Mean MCSE: 0.003 Samples: 14000 Mean MCSE: 0.003 Samples: 15000 Mean MCSE: 0.002 Samples: 16000 Mean MCSE: 0.002 Samples: 17000 Mean MCSE: 0.002 Samples: 18000 Mean MCSE: 0.002 Samples: 19000 Mean MCSE: 0.002 Samples: 20000 Mean MCSE: 0.002 Samples: 21000 Mean MCSE: 0.002 Samples: 22000 Mean MCSE: 0.002 Samples: 23000 Mean MCSE: 0.002 Samples: 24000 Mean MCSE: 0.002 Samples: 25000 Mean MCSE: 0.002 Samples: 26000 Mean MCSE: 0.002 Samples: 27000 Mean MCSE: 0.002 Samples: 28000 Mean MCSE: 0.002 Samples: 29000 Mean MCSE: 0.002 Samples: 30000 Mean MCSE: 0.002 Samples: 31000 Mean MCSE: 0.002 Samples: 32000 Mean MCSE: 0.002 Samples: 33000 Mean MCSE: 0.002 Samples: 34000 Mean MCSE: 0.002 Samples: 35000 Mean MCSE: 0.002 Samples: 36000 Mean MCSE: 0.002 Samples: 37000 Mean MCSE: 0.002 Samples: 38000 Mean MCSE: 0.002 Samples: 39000 Mean MCSE: 0.002 Samples: 40000 Mean MCSE: 0.002 Samples: 41000 Mean MCSE: 0.002 Samples: 42000 Mean MCSE: 0.001 Samples: 43000 Mean MCSE: 0.002 Samples: 44000 Mean MCSE: 0.001 Samples: 45000 Mean MCSE: 0.001 Samples: 46000 Mean MCSE: 0.001 Samples: 47000 Mean MCSE: 0.001 Samples: 48000 Mean MCSE: 0.001 Samples: 49000 Mean MCSE: 0.001 Samples: 50000 Mean MCSE: 0.001 Samples: 51000 Mean MCSE: 0.001 Samples: 52000 Mean MCSE: 0.001 Samples: 53000 Mean MCSE: 0.001 Samples: 54000 Mean MCSE: 0.001 Samples: 55000 Mean MCSE: 0.001 Samples: 56000 Mean MCSE: 0.001 Samples: 57000 Mean MCSE: 0.001 Samples: 58000 Mean MCSE: 0.001 Samples: 59000 Mean MCSE: 0.001 Samples: 60000 Mean MCSE: 0.001 Samples: 61000 Mean MCSE: 0.001 Samples: 62000 Mean MCSE: 0.001 Samples: 63000 Mean MCSE: 0.001 Samples: 64000 Mean MCSE: 0.001 Samples: 65000 Mean MCSE: 0.001 Samples: 66000 Mean MCSE: 0.001 Samples: 67000 Mean MCSE: 0.001 Samples: 68000 Mean MCSE: 0.001 Samples: 69000 Mean MCSE: 0.001 Samples: 70000 Mean MCSE: 0.001 Samples: 71000 Mean MCSE: 0.001 Samples: 72000 Mean MCSE: 0.001 Samples: 73000 Mean MCSE: 0.001 Samples: 74000 Mean MCSE: 0.001 Samples: 75000 Mean MCSE: 0.001 Samples: 76000 Mean MCSE: 0.001 Samples: 77000 Mean MCSE: 0.001 Samples: 78000 Mean MCSE: 0.001 Samples: 79000 Mean MCSE: 0.001 Samples: 80000 Mean MCSE: 0.001 Samples: 81000 Mean MCSE: 0.001 Samples: 82000 Mean MCSE: 0.001 Samples: 83000 Mean MCSE: 0.001 Samples: 84000 Mean MCSE: 0.001 Samples: 85000 Mean MCSE: 0.001 Samples: 86000 Mean MCSE: 0.001 Samples: 87000 Mean MCSE: 0.001 Samples: 88000 Mean MCSE: 0.001 Samples: 89000 Mean MCSE: 0.001 Samples: 90000 Mean MCSE: 0.001 Samples: 91000 Mean MCSE: 0.001 Samples: 92000 Mean MCSE: 0.001 Samples: 93000 Mean MCSE: 0.001 Samples: 94000 Mean MCSE: 0.001 Samples: 95000 Mean MCSE: 0.001 Samples: 96000 Mean MCSE: 0.001 Samples: 97000 Mean MCSE: 0.001 Samples: 98000 Mean MCSE: 0.001 MCSE criterion passed. Finishing. Depositional Event / Absolute Constraint: tpq_default Assigning initial random values (this may take a moment)... Beginning main Gibbs sampler. Will terminate either when MCSE criterion or maximum number of MC samples reached. Samples: 1000 Mean MCSE: 0.01 Samples: 2000 Mean MCSE: 0.007 Samples: 3000 Mean MCSE: 0.006 Samples: 4000 Mean MCSE: 0.005 Samples: 5000 Mean MCSE: 0.005 Samples: 6000 Mean MCSE: 0.004 Samples: 7000 Mean MCSE: 0.004 Samples: 8000 Mean MCSE: 0.004 Samples: 9000 Mean MCSE: 0.004 Samples: 10000 Mean MCSE: 0.003 Samples: 11000 Mean MCSE: 0.003 Samples: 12000 Mean MCSE: 0.003 Samples: 13000 Mean MCSE: 0.003 Samples: 14000 Mean MCSE: 0.003 Samples: 15000 Mean MCSE: 0.003 Samples: 16000 Mean MCSE: 0.003 Samples: 17000 Mean MCSE: 0.002 Samples: 18000 Mean MCSE: 0.002 Samples: 19000 Mean MCSE: 0.002 Samples: 20000 Mean MCSE: 0.003 Samples: 21000 Mean MCSE: 0.002 Samples: 22000 Mean MCSE: 0.002 Samples: 23000 Mean MCSE: 0.002 Samples: 24000 Mean MCSE: 0.002 Samples: 25000 Mean MCSE: 0.002 Samples: 26000 Mean MCSE: 0.002 Samples: 27000 Mean MCSE: 0.002 Samples: 28000 Mean MCSE: 0.002 Samples: 29000 Mean MCSE: 0.002 Samples: 30000 Mean MCSE: 0.002 Samples: 31000 Mean MCSE: 0.002 Samples: 32000 Mean MCSE: 0.002 Samples: 33000 Mean MCSE: 0.002 Samples: 34000 Mean MCSE: 0.002 Samples: 35000 Mean MCSE: 0.002 Samples: 36000 Mean MCSE: 0.002 Samples: 37000 Mean MCSE: 0.002 Samples: 38000 Mean MCSE: 0.002 Samples: 39000 Mean MCSE: 0.002 Samples: 40000 Mean MCSE: 0.002 Samples: 41000 Mean MCSE: 0.002 Samples: 42000 Mean MCSE: 0.002 Samples: 43000 Mean MCSE: 0.002 Samples: 44000 Mean MCSE: 0.002 Samples: 45000 Mean MCSE: 0.002 Samples: 46000 Mean MCSE: 0.002 Samples: 47000 Mean MCSE: 0.002 Samples: 48000 Mean MCSE: 0.002 Samples: 49000 Mean MCSE: 0.001 Samples: 50000 Mean MCSE: 0.001 Samples: 51000 Mean MCSE: 0.001 Samples: 52000 Mean MCSE: 0.001 Samples: 53000 Mean MCSE: 0.001 Samples: 54000 Mean MCSE: 0.001 Samples: 55000 Mean MCSE: 0.001 Samples: 56000 Mean MCSE: 0.001 Samples: 57000 Mean MCSE: 0.001 Samples: 58000 Mean MCSE: 0.001 Samples: 59000 Mean MCSE: 0.001 Samples: 60000 Mean MCSE: 0.001 Samples: 61000 Mean MCSE: 0.001 Samples: 62000 Mean MCSE: 0.001 Samples: 63000 Mean MCSE: 0.001 Samples: 64000 Mean MCSE: 0.001 Samples: 65000 Mean MCSE: 0.001 Samples: 66000 Mean MCSE: 0.001 Samples: 67000 Mean MCSE: 0.001 Samples: 68000 Mean MCSE: 0.001 Samples: 69000 Mean MCSE: 0.001 Samples: 70000 Mean MCSE: 0.001 Samples: 71000 Mean MCSE: 0.001 Samples: 72000 Mean MCSE: 0.001 Samples: 73000 Mean MCSE: 0.001 Samples: 74000 Mean MCSE: 0.001 Samples: 75000 Mean MCSE: 0.001 Samples: 76000 Mean MCSE: 0.001 Samples: 77000 Mean MCSE: 0.001 Samples: 78000 Mean MCSE: 0.001 Samples: 79000 Mean MCSE: 0.001 Samples: 80000 Mean MCSE: 0.001 Samples: 81000 Mean MCSE: 0.001 Samples: 82000 Mean MCSE: 0.001 Samples: 83000 Mean MCSE: 0.001 Samples: 84000 Mean MCSE: 0.001 Samples: 85000 Mean MCSE: 0.001 Samples: 86000 Mean MCSE: 0.001 Samples: 87000 Mean MCSE: 0.001 Samples: 88000 Mean MCSE: 0.001 Samples: 89000 Mean MCSE: 0.001 Samples: 90000 Mean MCSE: 0.001 Samples: 91000 Mean MCSE: 0.001 Samples: 92000 Mean MCSE: 0.001 Samples: 93000 Mean MCSE: 0.001 Samples: 94000 Mean MCSE: 0.001 Samples: 95000 Mean MCSE: 0.001 Samples: 96000 Mean MCSE: 0.001 Samples: 97000 Mean MCSE: 0.001 Samples: 98000 Mean MCSE: 0.001 Samples: 99000 Mean MCSE: 0.001 Samples: 100000 Mean MCSE: 0.001 Samples: 101000 Mean MCSE: 0.001 MCSE criterion passed. Finishing. Depositional Event / Absolute Constraint: taq_default Assigning initial random values (this may take a moment)... Beginning main Gibbs sampler. Will terminate either when MCSE criterion or maximum number of MC samples reached. Samples: 1000 Mean MCSE: 0.01 Samples: 2000 Mean MCSE: 0.006 Samples: 3000 Mean MCSE: 0.005 Samples: 4000 Mean MCSE: 0.005 Samples: 5000 Mean MCSE: 0.005 Samples: 6000 Mean MCSE: 0.004 Samples: 7000 Mean MCSE: 0.004 Samples: 8000 Mean MCSE: 0.004 Samples: 9000 Mean MCSE: 0.004 Samples: 10000 Mean MCSE: 0.003 Samples: 11000 Mean MCSE: 0.003 Samples: 12000 Mean MCSE: 0.003 Samples: 13000 Mean MCSE: 0.003 Samples: 14000 Mean MCSE: 0.003 Samples: 15000 Mean MCSE: 0.003 Samples: 16000 Mean MCSE: 0.002 Samples: 17000 Mean MCSE: 0.003 Samples: 18000 Mean MCSE: 0.002 Samples: 19000 Mean MCSE: 0.002 Samples: 20000 Mean MCSE: 0.002 Samples: 21000 Mean MCSE: 0.002 Samples: 22000 Mean MCSE: 0.002 Samples: 23000 Mean MCSE: 0.002 Samples: 24000 Mean MCSE: 0.002 Samples: 25000 Mean MCSE: 0.002 Samples: 26000 Mean MCSE: 0.002 Samples: 27000 Mean MCSE: 0.002 Samples: 28000 Mean MCSE: 0.002 Samples: 29000 Mean MCSE: 0.002 Samples: 30000 Mean MCSE: 0.002 Samples: 31000 Mean MCSE: 0.002 Samples: 32000 Mean MCSE: 0.002 Samples: 33000 Mean MCSE: 0.002 Samples: 34000 Mean MCSE: 0.002 Samples: 35000 Mean MCSE: 0.002 Samples: 36000 Mean MCSE: 0.002 Samples: 37000 Mean MCSE: 0.002 Samples: 38000 Mean MCSE: 0.002 Samples: 39000 Mean MCSE: 0.002 Samples: 40000 Mean MCSE: 0.002 Samples: 41000 Mean MCSE: 0.002 Samples: 42000 Mean MCSE: 0.002 Samples: 43000 Mean MCSE: 0.002 Samples: 44000 Mean MCSE: 0.002 Samples: 45000 Mean MCSE: 0.002 Samples: 46000 Mean MCSE: 0.001 Samples: 47000 Mean MCSE: 0.001 Samples: 48000 Mean MCSE: 0.001 Samples: 49000 Mean MCSE: 0.001 Samples: 50000 Mean MCSE: 0.001 Samples: 51000 Mean MCSE: 0.001 Samples: 52000 Mean MCSE: 0.001 Samples: 53000 Mean MCSE: 0.001 Samples: 54000 Mean MCSE: 0.001 Samples: 55000 Mean MCSE: 0.001 Samples: 56000 Mean MCSE: 0.001 Samples: 57000 Mean MCSE: 0.001 Samples: 58000 Mean MCSE: 0.001 Samples: 59000 Mean MCSE: 0.001 Samples: 60000 Mean MCSE: 0.001 Samples: 61000 Mean MCSE: 0.001 Samples: 62000 Mean MCSE: 0.001 Samples: 63000 Mean MCSE: 0.001 Samples: 64000 Mean MCSE: 0.001 Samples: 65000 Mean MCSE: 0.001 Samples: 66000 Mean MCSE: 0.001 Samples: 67000 Mean MCSE: 0.001 Samples: 68000 Mean MCSE: 0.001 Samples: 69000 Mean MCSE: 0.001 Samples: 70000 Mean MCSE: 0.001 Samples: 71000 Mean MCSE: 0.001 Samples: 72000 Mean MCSE: 0.001 Samples: 73000 Mean MCSE: 0.001 Samples: 74000 Mean MCSE: 0.001 Samples: 75000 Mean MCSE: 0.001 Samples: 76000 Mean MCSE: 0.001 Samples: 77000 Mean MCSE: 0.001 Samples: 78000 Mean MCSE: 0.001 Samples: 79000 Mean MCSE: 0.001 Samples: 80000 Mean MCSE: 0.001 Samples: 81000 Mean MCSE: 0.001 Samples: 82000 Mean MCSE: 0.001 Samples: 83000 Mean MCSE: 0.001 Samples: 84000 Mean MCSE: 0.001 Samples: 85000 Mean MCSE: 0.001 Samples: 86000 Mean MCSE: 0.001 Samples: 87000 Mean MCSE: 0.001 Samples: 88000 Mean MCSE: 0.001 Samples: 89000 Mean MCSE: 0.001 Samples: 90000 Mean MCSE: 0.001 Samples: 91000 Mean MCSE: 0.001 Samples: 92000 Mean MCSE: 0.001 Samples: 93000 Mean MCSE: 0.001 Samples: 94000 Mean MCSE: 0.001 Samples: 95000 Mean MCSE: 0.001 Samples: 96000 Mean MCSE: 0.001 Samples: 97000 Mean MCSE: 0.001 Samples: 98000 Mean MCSE: 0.001 Samples: 99000 Mean MCSE: 0.001 Samples: 100000 Mean MCSE: 0.001 Samples: 101000 Mean MCSE: 0.001 Samples: 102000 Mean MCSE: 0.001 Samples: 103000 Mean MCSE: 0.001 Samples: 104000 Mean MCSE: 0.001 Samples: 105000 Mean MCSE: 0.001 Samples: 106000 Mean MCSE: 0.001 Samples: 107000 Mean MCSE: 0.001 Samples: 108000 Mean MCSE: 0.001 Samples: 109000 Mean MCSE: 0.001 Samples: 110000 Mean MCSE: 0.001 Samples: 111000 Mean MCSE: 0.001 Samples: 112000 Mean MCSE: 0.001 Samples: 113000 Mean MCSE: 0.001 Samples: 114000 Mean MCSE: 0.001 Samples: 115000 Mean MCSE: 0.001 Samples: 116000 Mean MCSE: 0.001 Samples: 117000 Mean MCSE: 0.001 Samples: 118000 Mean MCSE: 0.001 MCSE criterion passed. Finishing. Estimation of squared displacement complete. [ FAIL 0 | WARN 0 | SKIP 0 | PASS 59 ] > > proc.time() user system elapsed 54.758 1.969 57.152