Dear All, I am running R on a debian testing machine and lately I have experienced several segmentation faults (often when running Amelia on some large data set). However, please have a look at the script pasted at the end of the email. If I uncomment the line about the RJSDMX library (which does precisely nothing in this script), the script causes a segmentation fault killing my R session. Anybody else experiences this? Here is my session_info() sessionInfo() R version 3.2.3 (2015-12-10) Platform: x86_64-pc-linux-gnu (64-bit) Running under: Debian GNU/Linux stretch/sid locale: [1] LC_CTYPE=en_GB.utf8 LC_NUMERIC=C [3] LC_TIME=en_GB.utf8 LC_COLLATE=en_GB.utf8 [5] LC_MONETARY=en_GB.utf8 LC_MESSAGES=en_GB.utf8 [7] LC_PAPER=en_GB.utf8 LC_NAME=en_GB.utf8 [9] LC_ADDRESS=en_GB.utf8 LC_TELEPHONE=en_GB.utf8 [11] LC_MEASUREMENT=en_GB.utf8 LC_IDENTIFICATION=en_GB.utf8 attached base packages: [1] stats graphics grDevices utils datasets methods base other attached packages: [1] RJSDMX_1.5 zoo_1.7-12 rJava_0.9-8 tempdisagg_0.24.0 loaded via a namespace (and not attached): [1] grid_3.2.3 lattice_0.20-33 Regards Lorenzo ################################################# rm(list=ls()) library(tempdisagg) ## library(RJSDMX) ts2 <- structure(c(339130, 356462, 363234, 378179, 367864, 378337, 392157, 402153, 376361, 392204, 403483, 414034, 391967, 406067, 419464, 434913, 410102, 424795, 437073, 448827, 415569, 430561, 444719, 455764, 419892, 444190, 454648, 466312, 439922, 448963, 465153, 475621, 445502, 457198, 473573, 485764, 463895, 470274, 484390, 490678, 478003, 483570, 499141, 509216, 481395, 492345, 511184, 513420, 483757, 490884, 514966, 515457, 497614, 510139, 523467, 526406, 499784, 519033, 532009, 531260, 521539, 532590, 553118, 557725, 548321, 556832, 578087, 578120, 566116, 580571, 587993, 569985, 534326, 539641, 564824, 568445, 558614, 570192, 594584, 598305, 593769, 598278, 620147, 615884, 611033, 609304, 630458, 624325, 614356, 627192, 649324, 645988, 642965, 645125, 669471, 665529, 664248, 669670, 694719), na.action = structure(1:64, class "omit"), .Tsp = c(1991, 2015.5, 4), class = "ts") tsq <- td(ts2 ~ 1, to = "monthly", method = "denton-cholette") tsm <- predict(tsq)