* using log directory ‘/data/blackswan/ripley/R/packages/tests-devel/SMM.Rcheck’ * using R Under development (unstable) (2024-04-19 r86447) * using platform: x86_64-pc-linux-gnu * R was compiled by gcc (GCC) 13.2.1 20240316 (Red Hat 13.2.1-7) GNU Fortran (GCC) 13.2.1 20240316 (Red Hat 13.2.1-7) * running under: Fedora Linux 38 (Workstation Edition) * using session charset: UTF-8 * checking for file ‘SMM/DESCRIPTION’ ... OK * checking extension type ... Package * this is package ‘SMM’ version ‘1.0.2’ * checking package namespace information ... OK * checking package dependencies ... OK * checking if this is a source package ... OK * checking if there is a namespace ... OK * checking for executable files ... OK * checking for hidden files and directories ... OK * checking for portable file names ... OK * checking for sufficient/correct file permissions ... OK * checking whether package ‘SMM’ can be installed ... [10s/10s] OK * checking package directory ... OK * checking ‘build’ directory ... OK * checking DESCRIPTION meta-information ... OK * checking top-level files ... OK * checking for left-over files ... OK * checking index information ... OK * checking package subdirectories ... OK * checking code files for non-ASCII characters ... OK * checking R files for syntax errors ... OK * checking whether the package can be loaded ... OK * checking whether the package can be loaded with stated dependencies ... OK * checking whether the package can be unloaded cleanly ... OK * checking whether the namespace can be loaded with stated dependencies ... OK * checking whether the namespace can be unloaded cleanly ... OK * checking loading without being on the library search path ... OK * checking dependencies in R code ... OK * checking S3 generic/method consistency ... OK * checking replacement functions ... OK * checking foreign function calls ... OK * checking R code for possible problems ... [23s/23s] OK * checking Rd files ... NOTE checkRd: (-1) estimMk.Rd:44: Lost braces; missing escapes or markup? 44 | estimMk returns the transition probability matrix of size (S^{k})xS (with S = length(E)) | ^ * checking Rd metadata ... OK * checking Rd line widths ... OK * checking Rd cross-references ... OK * checking for missing documentation entries ... OK * checking for code/documentation mismatches ... OK * checking Rd \usage sections ... OK * checking Rd contents ... OK * checking for unstated dependencies in examples ... OK * checking sizes of PDF files under ‘inst/doc’ ... OK * checking installed files from ‘inst/doc’ ... OK * checking files in ‘vignettes’ ... OK * checking examples ... OK * checking examples with --run-donttest ... ERROR Running examples in ‘SMM-Ex.R’ failed The error most likely occurred in: > ### Name: estimSM > ### Title: Estimation of a semi-Markov chain > ### Aliases: estimSM > ### Keywords: Estimation Semi-Markov models > > ### ** Examples > > alphabet = c("a","c","g","t") > S = length(alphabet) > # creation of the transition matrix > Pij = matrix(c(0,0.2,0.3,0.5,0.4,0,0.2,0.4,0.1,0.2,0,0.7,0.8,0.1,0.1,0), nrow = S, + ncol = S, byrow = TRUE) > > Pij [,1] [,2] [,3] [,4] [1,] 0.0 0.2 0.3 0.5 [2,] 0.4 0.0 0.2 0.4 [3,] 0.1 0.2 0.0 0.7 [4,] 0.8 0.1 0.1 0.0 > # [,1] [,2] [,3] [,4] > #[1,] 0.0 0.2 0.3 0.5 > #[2,] 0.4 0.0 0.2 0.4 > #[3,] 0.1 0.2 0.0 0.7 > #[4,] 0.8 0.1 0.1 0.0 > > ################################ > ## Parametric estimation of a trajectory (of length equal to 5000) > ## where the sojourn times depend neither on the present state nor on the next state ("f") > ################################ > ## Simulation of a sequence of length 5000 > seq5000 = simulSM(E = alphabet, NbSeq = 1, lengthSeq = 5000, TypeSojournTime = "f", + init = c(1/4,1/4,1/4,1/4), Ptrans = Pij, distr = "pois", param = 2) > > > ## Estimation of the simulated sequence > estSeq5000 = estimSM(seq = seq5000, E = alphabet, TypeSojournTime = "f", + distr = "pois", cens.end = 0, cens.beg = 0) > > # initial distribution estimated > estSeq5000$init [1] 0.3475122 0.1460837 0.1605641 0.3458400 > # [1] 0.3592058 0.1456077 0.1600481 0.3351384 > > # transition matrix estimated > estSeq5000$Ptrans [,1] [,2] [,3] [,4] [1,] 0.00000000 0.1982759 0.28965517 0.5120690 [2,] 0.43442623 0.0000000 0.19672131 0.3688525 [3,] 0.08178439 0.2081784 0.00000000 0.7100372 [4,] 0.78336222 0.1265165 0.09012132 0.0000000 > # [,1] [,2] [,3] [,4] > #[1,] 0.0000000 0.21775544 0.30150754 0.4807370 > #[2,] 0.4297521 0.00000000 0.18181818 0.3884298 > #[3,] 0.1052632 0.23308271 0.00000000 0.6616541 > #[4,] 0.8348294 0.08976661 0.07540395 0.0000000 > > # estimated parameter > estSeq5000$param [1] 1.992216 0.000000 > # [1] 2.007822 0.000000 > > # estimated semi-Markov kernel > estSeq5000$q , , 1 [,1] [,2] [,3] [,4] [1,] 0.00000000 0.02704342 0.03950691 0.06984257 [2,] 0.05925265 0.00000000 0.02683139 0.05030886 [3,] 0.01115481 0.02839406 0.00000000 0.09684403 [4,] 0.10684505 0.01725595 0.01229191 0.00000000 , , 2 [,1] [,2] [,3] [,4] [1,] 0.00000000 0.05387632 0.07870628 0.1391415 [2,] 0.11804406 0.00000000 0.05345391 0.1002261 [3,] 0.02222279 0.05656709 0.00000000 0.1929342 [4,] 0.21285837 0.03437757 0.02448813 0.0000000 , , 3 [,1] [,2] [,3] [,4] [1,] 0.00000000 0.05366662 0.07839994 0.13859989 [2,] 0.11758461 0.00000000 0.05324586 0.09983599 [3,] 0.02213629 0.05634692 0.00000000 0.19218325 [4,] 0.21202987 0.03424376 0.02439282 0.00000000 , , 4 [,1] [,2] [,3] [,4] [1,] 0.00000000 0.03563850 0.05206319 0.09204029 [2,] 0.07808463 0.00000000 0.03535908 0.06629827 [3,] 0.01470009 0.03741840 0.00000000 0.12762348 [4,] 0.14080307 0.02274032 0.01619858 0.00000000 , , 5 [,1] [,2] [,3] [,4] [1,] 0.000000000 0.01774989 0.025930276 0.04584102 [2,] 0.038890353 0.00000000 0.017610726 0.03302011 [3,] 0.007321436 0.01863638 0.000000000 0.06356337 [4,] 0.070127518 0.01132590 0.008067768 0.00000000 , , 6 [,1] [,2] [,3] [,4] [1,] 0.000000000 0.007072322 0.010331740 0.01826504 [2,] 0.015495593 0.000000000 0.007016873 0.01315664 [3,] 0.002917176 0.007425538 0.000000000 0.02532639 [4,] 0.027941827 0.004512729 0.003214546 0.00000000 , , 7 [,1] [,2] [,3] [,4] [1,] 0.0000000000 0.002348265 0.003430509 0.006064650 [2,] 0.0051450937 0.000000000 0.002329854 0.004368476 [3,] 0.0009686071 0.002465545 0.000000000 0.008409271 [4,] 0.0092776904 0.001498388 0.001067345 0.000000000 , , 8 [,1] [,2] [,3] [,4] [1,] 0.0000000000 0.0006683214 0.0009763304 0.001726013 [2,] 0.0014643051 0.0000000000 0.0006630816 0.001243278 [3,] 0.0002756677 0.0007016997 0.0000000000 0.002393297 [4,] 0.0026404513 0.0004264446 0.0003037687 0.000000000 , , 9 [,1] [,2] [,3] [,4] [1,] 0.0000000000 0.0001664300 0.0002431326 0.0004298237 [2,] 0.0003646514 0.0000000000 0.0001651252 0.0003096097 [3,] 0.0000686487 0.0001747421 0.0000000000 0.0005959955 [4,] 0.0006575435 0.0001061962 0.0000756466 0.0000000000 , , 10 [,1] [,2] [,3] [,4] [1,] 0.000000e+00 3.684050e-05 5.381917e-05 9.514460e-05 [2,] 8.071825e-05 0.000000e+00 3.655166e-05 6.853436e-05 [3,] 1.519589e-05 3.868044e-05 0.000000e+00 1.319279e-04 [4,] 1.455521e-04 2.350730e-05 1.674493e-05 0.000000e+00 > # , , 1 > # [,1] [,2] [,3] [,4] > #[1,] 0.00000000 0.02924038 0.04048668 0.06455377 > #[2,] 0.05770747 0.00000000 0.02441470 0.05215867 > #[3,] 0.01413482 0.03129854 0.00000000 0.08884747 > #[4,] 0.11210159 0.01205393 0.01012531 0.00000000 > #, , 2 > # [,1] [,2] [,3] [,4] > #[1,] 0.00000000 0.05870948 0.08129005 0.1296125 > #[2,] 0.11586631 0.00000000 0.04902036 0.1047253 > #[3,] 0.02838021 0.06284189 0.00000000 0.1783899 > #[4,] 0.22508003 0.02420215 0.02032981 0.0000000 > #, , 3 > # [,1] [,2] [,3] [,4] > #[1,] 0.0000000 0.05893909 0.08160797 0.1301194 > #[2,] 0.1163195 0.00000000 0.04921208 0.1051349 > #[3,] 0.0284912 0.06308767 0.00000000 0.1790876 > #[4,] 0.2259603 0.02429681 0.02040932 0.0000000 > #, , 4 > # [,1] [,2] [,3] [,4] > #[1,] 0.00000000 0.03944640 0.05461809 0.08708551 > #[2,] 0.07784959 0.00000000 0.03293636 0.07036405 > #[3,] 0.01906842 0.04222293 0.00000000 0.11985865 > #[4,] 0.15122935 0.01626122 0.01365943 0.00000000 > #, , 5 > # [,1] [,2] [,3] [,4] > #[1,] 0.000000000 0.019800336 0.027415850 0.04371305 > #[2,] 0.039077026 0.000000000 0.016532588 0.03531962 > #[3,] 0.009571498 0.021194032 0.000000000 0.06016370 > #[4,] 0.075910402 0.008162409 0.006856423 0.00000000 > #, , 6 > # [,1] [,2] [,3] [,4] > #[1,] 0.000000000 0.007951110 0.011009229 0.0175536 > #[2,] 0.015691942 0.000000000 0.006638898 0.0141831 > #[3,] 0.003843573 0.008510768 0.000000000 0.0241596 > #[4,] 0.030482913 0.003277733 0.002753295 0.0000000 > #, , 7 > # [,1] [,2] [,3] [,4] > #[1,] 0.000000000 0.002660735 0.0036840950 0.005874085 > #[2,] 0.005251104 0.000000000 0.0022216210 0.004746190 > #[3,] 0.001286202 0.002848018 0.0000000000 0.008084696 > #[4,] 0.010200710 0.001096851 0.0009213545 0.000000000 > #, , 8 > # [,1] [,2] [,3] [,4] > #[1,] 0.0000000000 0.0007631832 0.0010567152 0.001684874 > #[2,] 0.0015061831 0.0000000000 0.0006372313 0.001361358 > #[3,] 0.0003689234 0.0008169018 0.0000000000 0.002318947 > #[4,] 0.0029258870 0.0003146115 0.0002642737 0.000000000 > #, , 9 > # [,1] [,2] [,3] [,4] > #[1,] 0.000000e+00 1.915420e-04 2.652120e-04 0.0004228658 > #[2,] 3.780184e-04 0.000000e+00 1.599309e-04 0.0003416705 > #[3,] 9.259156e-05 2.050242e-04 0.000000e+00 0.0005820041 > #[4,] 7.343325e-04 7.896048e-05 6.632681e-05 0.0000000000 > > ## No test: > ################################ > ## Parametric estimation of a trajectory (of length equal to 5000), > ## where sojourn times do not depend neither on the present state nor on the next state > ## and the sequence is censored at the beginning. > ################################ > ## Simulation of a sequence of length 5000 > #seq5000 = simulSM(E = alphabet, NbSeq = 1, lengthSeq = 5000, TypeSojournTime = "f", > # init = c(1/4,1/4,1/4,1/4), Ptrans = Pij, distr = "pois", param = 2, > # cens.beg = 1, cens.end = 0) > > > ## Estimation of the simulated sequence > #estSeq5000 = estimSM(seq = seq5000, E = alphabet, TypeSojournTime = "f", > # distr = "pois", cens.end = 0, cens.beg = 1) > > > ################################ > ## Parametric estimation of a trajectory (of length equal to 5000), > ## where sojourn times do not depend neither on the present state nor on the next state > ## and the sequence is censored at the beginning and at the end > ################################ > ## Simulation of a sequence of legnth 5000 > #seq5000 = simulSM(E = alphabet, NbSeq = 1, lengthSeq = 5000, TypeSojournTime = "f", > # init = c(1/4,1/4,1/4,1/4), Ptrans = Pij, distr = "pois", param = 2, > # cens.beg = 1, cens.end = 1) > > > ## Estimation of the simulated sequence > #estSeq5000 = estimSM(seq = seq5000, E = alphabet, TypeSojournTime = "f", > # distr = "pois", cens.end = 1, cens.beg = 1) > > ################################ > ## Parametric simulation of several trajectories (3 trajectories of length 1000, 10 000 > ## and 2000 respectively), > ## where the sojourn times depend on the present state and on the next state > ## and the sojourn time distributions are modeled by different distributions. > ################################ > lengthSeq3 = c(1000, 10000, 2000) > ## creation of the initial distribution > vect.init = c(1/4,1/4,1/4,1/4) > ## creation of the distribution matrix > distr.matrix = matrix(c("dweibull", "pois", "geom", "nbinom", "geom", "nbinom", + "pois", "dweibull", "pois", "pois", "dweibull", "geom", "pois","geom", "geom", + "nbinom"), nrow = S, ncol = S, byrow = TRUE) > ## creation of an array containing the parameters > param1.matrix = matrix(c(0.6,2,0.4,4,0.7,2,5,0.6,2,3,0.6,0.6,4,0.3,0.4,4), nrow = S, + ncol = S, byrow = TRUE) > param2.matrix = matrix(c(0.8,0,0,2,0,5,0,0.8,0,0,0.8,0,4,0,0,4), nrow = S, ncol = S, + byrow = TRUE) > param.array = array(c(param1.matrix, param2.matrix), c(S,S,2)) > ### Simulation of 3 sequences > seq3 = simulSM(E = alphabet, NbSeq = 3, lengthSeq = lengthSeq3, TypeSojournTime = "fij", + init = vect.init, Ptrans = Pij, distr = distr.matrix, param = param.array, File.out = NULL) > > ## Estimation of the simulated sequence > estSeq3 = estimSM(seq = seq3, E = alphabet, TypeSojournTime = "fij", + distr = distr.matrix, cens.end = 0, cens.beg = 0) Warning in dnbinom(0:(Kmax - 1), size = param[j], mu = param[j + S * S]) : NaNs produced Warning in dnbinom(0:(Kmax - 1), size = param[j], mu = param[j + S * S]) : NaNs produced > > ################################ > ## Non-Parametric simulation of several trajectories (3 trajectories of length 1000, 10 000 > ## and 2000 respectively), > ## where the sojourn times depend on the present state and on the next state > ################################ > lengthSeq3 = c(1000, 10000, 2000) > ## creation of the initial distribution > vect.init = c(1/4,1/4,1/4,1/4) > ## creation of an array containing the conditional distributions > Kmax = 4 > mat1 = matrix(c(0,0.5,0.4,0.6,0.3,0,0.5,0.4,0.7,0.2,0,0.3,0.4,0.6,0.2,0), nrow = S, + ncol = S, byrow = TRUE) > mat2 = matrix(c(0,0.2,0.3,0.1,0.2,0,0.2,0.3,0.1,0.4,0,0.3,0.2,0.1,0.3,0), nrow = S, + ncol = S, byrow = TRUE) > mat3 = matrix(c(0,0.1,0.3,0.1,0.3,0,0.1,0.2,0.1,0.2,0,0.3,0.3,0.3,0.4,0), nrow = S, + ncol = S, byrow = TRUE) > mat4 = matrix(c(0,0.2,0,0.2,0.2,0,0.2,0.1,0.1,0.2,0,0.1,0.1,0,0.1,0), nrow = S, + ncol = S, byrow = TRUE) > f <- array(c(mat1,mat2,mat3,mat4), c(S,S,Kmax)) > ### Simulation of 3 sequences > seq3.NP = simulSM(E = alphabet, NbSeq = 3, lengthSeq = lengthSeq3, + TypeSojournTime = "fij", init = vect.init, Ptrans = Pij, distr = "NP", laws = f, + File.out = NULL) > > ## Estimation of the simulated sequence > estSeq3.NP = estimSM(seq = seq3.NP, E = alphabet, TypeSojournTime = "fij", + distr = "NP", cens.end = 0, cens.beg = 0) > > > # initial distribution estimated > estSeq3.NP$init [1] 0.0000000 0.6666667 0.3333333 0.0000000 > # [1] 0.1856190 0.2409524 0.2975714 0.2758571 > > # transition matrix estimated > estSeq3.NP$Ptrans [,1] [,2] [,3] [,4] [1,] 0.00000000 0.1988225 0.29710145 0.5040761 [2,] 0.39261745 0.0000000 0.19463087 0.4127517 [3,] 0.09593023 0.2122093 0.00000000 0.6918605 [4,] 0.80145719 0.1070128 0.09153005 0.0000000 > # [,1] [,2] [,3] [,4] > # [1,] 0.00000000 0.20560325 0.29191143 0.5024853 > # [2,] 0.40614334 0.00000000 0.19795222 0.3959044 > # [3,] 0.08932039 0.21941748 0.00000000 0.6912621 > # [4,] 0.81206817 0.09120221 0.09672962 0.0000000 > > # parameter estimated > estSeq3.NP$laws , , 1 [,1] [,2] [,3] [,4] [1,] 0.0000000 0.5375854 0.3917683 0.6064690 [2,] 0.3133903 0.0000000 0.5229885 0.3848238 [3,] 0.6868687 0.1917808 0.0000000 0.2885154 [4,] 0.4000000 0.5872340 0.2537313 0.0000000 , , 2 [,1] [,2] [,3] [,4] [1,] 0.0000000 0.2186788 0.3399390 0.08805031 [2,] 0.1880342 0.0000000 0.2126437 0.31165312 [3,] 0.1010101 0.3835616 0.0000000 0.26750700 [4,] 0.1960227 0.1276596 0.2835821 0.00000000 , , 3 [,1] [,2] [,3] [,4] [1,] 0.0000000 0.09111617 0.26829268 0.1069182 [2,] 0.2649573 0.00000000 0.07471264 0.1707317 [3,] 0.1212121 0.20547945 0.00000000 0.3151261 [4,] 0.3017045 0.28510638 0.38308458 0.0000000 , , 4 [,1] [,2] [,3] [,4] [1,] 0.00000000 0.1526196 0.00000000 0.1985624 [2,] 0.23361823 0.0000000 0.18965517 0.1327913 [3,] 0.09090909 0.2191781 0.00000000 0.1288515 [4,] 0.10227273 0.0000000 0.07960199 0.0000000 > #, , 1 > # [,1] [,2] [,3] [,4] > # [1,] 0.0000000 0.4769231 0.4009288 0.6016187 > # [2,] 0.3053221 0.0000000 0.4540230 0.3764368 > # [3,] 0.7717391 0.2389381 0.0000000 0.2794944 > # [4,] 0.4140669 0.5959596 0.2000000 0.0000000 > # , , 2 > # [,1] [,2] [,3] [,4] > # [1,] 0.00000000 0.2175824 0.2801858 0.1052158 > # [2,] 0.16526611 0.0000000 0.2356322 0.2959770 > # [3,] 0.07608696 0.3716814 0.0000000 0.3089888 > # [4,] 0.18774816 0.1161616 0.3142857 0.0000000 > # , , 3 > # [,1] [,2] [,3] [,4] > # [1,] 0.00000000 0.09450549 0.3188854 0.08363309 > # [2,] 0.31092437 0.00000000 0.1034483 0.19540230 > # [3,] 0.06521739 0.20353982 0.0000000 0.32022472 > # [4,] 0.29892229 0.28787879 0.3666667 0.00000000 > #, , 4 > # [,1] [,2] [,3] [,4] > # [1,] 0.00000000 0.2109890 0.0000000 0.20953237 > # [2,] 0.21848739 0.0000000 0.2068966 0.13218391 > # [3,] 0.08695652 0.1858407 0.0000000 0.09129213 > # [4,] 0.09926262 0.0000000 0.1190476 0.00000000 > > # semi-Markovian kernel estimated > estSeq3.NP$q , , 1 [,1] [,2] [,3] [,4] [1,] 0.00000000 0.10688406 0.11639493 0.3057065 [2,] 0.12304251 0.00000000 0.10178971 0.1588367 [3,] 0.06589147 0.04069767 0.00000000 0.1996124 [4,] 0.32058288 0.06284153 0.02322404 0.0000000 , , 2 [,1] [,2] [,3] [,4] [1,] 0.000000000 0.04347826 0.10099638 0.04438406 [2,] 0.073825503 0.00000000 0.04138702 0.12863535 [3,] 0.009689922 0.08139535 0.00000000 0.18507752 [4,] 0.157103825 0.01366120 0.02595628 0.00000000 , , 3 [,1] [,2] [,3] [,4] [1,] 0.00000000 0.01811594 0.07971014 0.05389493 [2,] 0.10402685 0.00000000 0.01454139 0.07046980 [3,] 0.01162791 0.04360465 0.00000000 0.21802326 [4,] 0.24180328 0.03051002 0.03506375 0.00000000 , , 4 [,1] [,2] [,3] [,4] [1,] 0.00000000 0.03034420 0.000000000 0.10009058 [2,] 0.09172260 0.00000000 0.036912752 0.05480984 [3,] 0.00872093 0.04651163 0.000000000 0.08914729 [4,] 0.08196721 0.00000000 0.007285974 0.00000000 > # , , 1 > # [,1] [,2] [,3] [,4] > # [1,] 0.00000000 0.09805694 0.11703570 0.3023046 > # [2,] 0.12400455 0.00000000 0.08987486 0.1490330 > # [3,] 0.06893204 0.05242718 0.00000000 0.1932039 > # [4,] 0.33625058 0.05435283 0.01934592 0.0000000 > # , , 2 > # [,1] [,2] [,3] [,4] > # [1,] 0.000000000 0.04473565 0.08178943 0.05286941 > # [2,] 0.067121729 0.00000000 0.04664391 0.11717861 > # [3,] 0.006796117 0.08155340 0.00000000 0.21359223 > # [4,] 0.152464302 0.01059420 0.03040074 0.00000000 > #, , 3 > # [,1] [,2] [,3] [,4] > # [1,] 0.000000000 0.01943064 0.09308631 0.04202440 > # [2,] 0.126279863 0.00000000 0.02047782 0.07736064 > # [3,] 0.005825243 0.04466019 0.00000000 0.22135922 > # [4,] 0.242745279 0.02625518 0.03546753 0.00000000 > #, , 4 > # [,1] [,2] [,3] [,4] > # [1,] 0.00000000 0.04338003 0.00000000 0.1052869 > # [2,] 0.08873720 0.00000000 0.04095563 0.0523322 > # [3,] 0.00776699 0.04077670 0.00000000 0.0631068 > # [4,] 0.08060801 0.00000000 0.01151543 0.0000000 > > #---------------------------------------------# > alphabet = c("0","1") > S = length(alphabet) > # creation of the transition matrix > Pij = matrix(c(0,1,1,0), nrow = S, ncol = S, byrow = TRUE) > distr = matrix(c("nbinom", "pois", "geom", "geom"), nrow = S, ncol = S, byrow = TRUE) > param = array(c(matrix(c(2,5,0.4,0.7), nrow = S, ncol = S, byrow = TRUE), matrix(c(6,0,0,0), + nrow = S, ncol = S, byrow = TRUE)), c(S,S,2)) > > ################################ > ## Parametric estimation of a trajectory (of length equal to 5000) > ## where the state space is {"0","1"} > ################################ > ## Simulation of a sequence of length 5000 > seq2 = simulSM(E = alphabet, NbSeq = 2, lengthSeq = c(5000,1000), TypeSojournTime = "fij", + init = c(1/2,1/2), Ptrans = Pij, distr = distr, param = param) > > > ## Estimation of the simulated sequence > estSeq2 = estimSM(seq = seq2, E = alphabet, TypeSojournTime = "fij", + distr = distr, cens.end = 1, cens.beg = 1) Warning in .comptage(J, L, S, Kmax) : Warning : missing transitions Warning in .comptage(J, L, S, Kmax) : Warning : missing letters Warning in .comptage(J, L, S, Kmax) : Warning : missing letters Warning in matrix(c(dpois(0:(Kmax - 1), lambda = par[(S * (S - 2) + p[1])]), : data length [18] is not a sub-multiple or multiple of the number of rows [5] Error in fuv[nr, ] <- MGM[, 1] : number of items to replace is not a multiple of replacement length Calls: estimSM -> .estim.plusTraj -> optim -> -> fn Execution halted * checking for unstated dependencies in vignettes ... OK * checking package vignettes ... OK * checking re-building of vignette outputs ... [11s/11s] OK * checking PDF version of manual ... OK * checking for non-standard things in the check directory ... OK * checking for detritus in the temp directory ... OK * checking for new files in some other directories ... OK * DONE Status: 1 ERROR, 1 NOTE See ‘/data/blackswan/ripley/R/packages/tests-devel/SMM.Rcheck/00check.log’ for details. Command exited with non-zero status 1 Time 1:17.23, 69.92 + 7.00