CRAN Package Check Results for Package icmstate

Last updated on 2024-11-05 05:49:46 CET.

Flavor	Version	T_install	T_check	T_total	Status
r-devel-linux-x86_64-debian-clang	0.1.0	20.96	335.17	356.13	OK
r-devel-linux-x86_64-debian-gcc	0.1.0	15.27	214.91	230.18	OK
r-devel-linux-x86_64-fedora-clang	0.1.0			412.71	OK
r-devel-linux-x86_64-fedora-gcc	0.1.0			405.58	ERROR
r-devel-windows-x86_64	0.1.0	25.00	432.00	457.00	OK
r-patched-linux-x86_64	0.1.0	21.14	312.59	333.73	OK
r-release-linux-x86_64	0.1.0	18.72	311.99	330.71	OK
r-release-macos-arm64	0.1.0			111.00	ERROR
r-release-macos-x86_64	0.1.0			287.00	OK
r-release-windows-x86_64	0.1.0	27.00	445.00	472.00	OK
r-oldrel-macos-arm64	0.1.0			141.00	ERROR
r-oldrel-macos-x86_64	0.1.0			287.00	OK
r-oldrel-windows-x86_64	0.1.0	30.00	506.00	536.00	OK

Additional issues

M1mac

Check Details

Version: 0.1.0
Check: tests
Result: ERROR Running ‘testthat.R’ [36s/100s] Running the tests in ‘tests/testthat.R’ failed. Complete output: > library(testthat) > library(icmstate) > > test_check("icmstate") [1] "Frydman Support" [1] "MSM Support" *** caught segfault *** address 0x1, cause 'memory not mapped' Traceback: 1: Ccall.msm(params, do.what = "lik", ...) 2: fn(par, ...) 3: (function (par) fn(par, ...))(c(qbase = -3.06196393627199, qbase = -1.6756695751521, qbase = -1.4667876541959)) 4: optim(method = "BFGS", control = list(), par = c(qbase = -3.06196393627199, qbase = -1.6756695751521, qbase = -1.4667876541959), fn = function (params, ...) { assign("nliks", get("nliks", msm.globals) + 1, envir = msm.globals) args <- list(...) w <- args$msmdata$subject.weights if (!is.null(w)) { lik <- Ccall.msm(params, do.what = "lik.subj", ...) sum(w * lik) } else Ccall.msm(params, do.what = "lik", ...)}, hessian = TRUE, gr = function (params, ...) { w <- list(...)$msmdata$subject.weights if (!is.null(w)) { deriv <- Ccall.msm(params, do.what = "deriv.subj", ...) apply(w * deriv, 2, sum) } else Ccall.msm(params, do.what = "deriv", ...)}, msmdata = list(mf = list(`(subject)` = c(1, 1, 2, 2, 2, 3, 3, 4, 4, 4, 4, 5, 5, 5), `(state)` = c(1, 3, 1, 1, 3, 1, 3, 1, 2, 2, 3, 1, 1, 3), `(time)` = c(0, 0.813481943681836, 0, 7.9621127679944, 11.3083713456988, 0, 4.29615335352719, 0, 0.662206339277327, 4.34080645274371, 4.99749264931306, 0, 0.724425864871591, 4.28927130056545), `(obstype)` = c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1), `(obstrue)` = c(1, 3, 1, 1, 3, 1, 3, 1, 2, 2, 3, 1, 1, 3), `(obs)` = 1:14, `(pcomb)` = c(NA, 1L, NA, 2L, 3L, NA, 4L, NA, 5L, 6L, 7L, NA, 8L, 9L)), mf.agg = list(`(subject)` = c(4, 4, 5, 1, 2, 5, 4, 3, 2), `(fromstate)` = c(2, 1, 1, 1, 1, 1, 2, 1, 1), `(time)` = c(4.34080645274371, 0, 0, 0, 7.9621127679944, 0.724425864871591, 0.662206339277327, 0, 0), `(obstype)` = c(1, 1, 1, 1, 1, 1, 1, 1, 1), `(obstrue)` = c(2, 1, 1, 1, 1, 1, 2, 1, 1), `(obs)` = c(11L, 9L, 13L, 2L, 5L, 14L, 10L, 7L, 4L), `(tostate)` = c(3, 2, 1, 3, 3, 3, 2, 3, 1), `(timelag)` = c(0.656686196569353, 0.662206339277327, 0.724425864871591, 0.813481943681836, 3.34625857770443, 3.56484543569386, 3.67860011346638, 4.29615335352719, 7.9621127679944), `(nocc)` = c(1, 1, 1, 1, 1, 1, 1, 1, 1), `(whicha)` = 1:9, `(noccsum)` = c(NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_)), mm.cov = c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1), mm.cov.agg = c(1, 1, 1, 1, 1, 1, 1, 1, 1), mm.mcov = NULL, mm.hcov = NULL, mm.icov = NULL, subject.weights = NULL), qmodel = list(nstates = 3L, iso = 4, perm = c(1, 2, 3), qperm = 1:3, npars = 3, imatrix = c(0, 0, 0, 1, 0, 0, 1, 1, 0), qmatrix = c(-0.233978505697303, 0, 0, 0.0467957011394606, -0.230665272945204, 0, 0.187182804557843, 0.230665272945204, 0), inits = c(0.0467957011394606, 0.187182804557843, 0.230665272945204), constr = 1:3, ndpars = 3L, expm = 1), qcmodel = list(npars = 0, ncovs = 0, ndpars = 0), cmodel = list(ncens = 0, censor = NULL, states = NULL, states_list = NULL, index = NULL), hmodel = list( hidden = FALSE, models = c(0, 0, 0), nipars = 0, nicoveffs = 0, totpars = 0, ncoveffs = 0, ematrix = FALSE), paramdata = list( inits = c(qbase = -3.06196393627199, qbase = -1.6756695751521, qbase = -1.4667876541959), plabs = c("qbase", "qbase", "qbase"), allinits = c(qbase = -3.06196393627199, qbase = -1.6756695751521, qbase = -1.4667876541959), hmmpars = integer(0), fixed = FALSE, fixedpars = integer(0), optpars = 1:3, auxpars = integer(0), constr = c(1, 2, 3), npars = 3, duppars = integer(0), nfix = 0L, nopt = 3L, ndup = 0L, ranges = c(0, 0, 0, Inf, Inf, Inf), params = c(qbase = -3.06196393627199, qbase = -1.6756695751521, qbase = -1.4667876541959))) 5: do.call("optim", optim.args) 6: msm.optim.optim(p = list(inits = c(qbase = -3.06196393627199, qbase = -1.6756695751521, qbase = -1.4667876541959), plabs = c("qbase", "qbase", "qbase"), allinits = c(qbase = -3.06196393627199, qbase = -1.6756695751521, qbase = -1.4667876541959), hmmpars = integer(0), fixed = FALSE, fixedpars = integer(0), optpars = 1:3, auxpars = integer(0), constr = c(1, 2, 3), npars = 3, duppars = integer(0), nfix = 0L, nopt = 3L, ndup = 0L, ranges = c(0, 0, 0, Inf, Inf, Inf), params = c(qbase = -3.06196393627199, qbase = -1.6756695751521, qbase = -1.4667876541959)), gr = function (params, ...) { w <- list(...)$msmdata$subject.weights if (!is.null(w)) { deriv <- Ccall.msm(params, do.what = "deriv.subj", ...) apply(w * deriv, 2, sum) } else Ccall.msm(params, do.what = "deriv", ...)}, hessian = TRUE, msmdata = list(mf = list(`(subject)` = c(1, 1, 2, 2, 2, 3, 3, 4, 4, 4, 4, 5, 5, 5), `(state)` = c(1, 3, 1, 1, 3, 1, 3, 1, 2, 2, 3, 1, 1, 3), `(time)` = c(0, 0.813481943681836, 0, 7.9621127679944, 11.3083713456988, 0, 4.29615335352719, 0, 0.662206339277327, 4.34080645274371, 4.99749264931306, 0, 0.724425864871591, 4.28927130056545), `(obstype)` = c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1), `(obstrue)` = c(1, 3, 1, 1, 3, 1, 3, 1, 2, 2, 3, 1, 1, 3), `(obs)` = 1:14, `(pcomb)` = c(NA, 1L, NA, 2L, 3L, NA, 4L, NA, 5L, 6L, 7L, NA, 8L, 9L)), mf.agg = list(`(subject)` = c(4, 4, 5, 1, 2, 5, 4, 3, 2), `(fromstate)` = c(2, 1, 1, 1, 1, 1, 2, 1, 1), `(time)` = c(4.34080645274371, 0, 0, 0, 7.9621127679944, 0.724425864871591, 0.662206339277327, 0, 0), `(obstype)` = c(1, 1, 1, 1, 1, 1, 1, 1, 1), `(obstrue)` = c(2, 1, 1, 1, 1, 1, 2, 1, 1), `(obs)` = c(11L, 9L, 13L, 2L, 5L, 14L, 10L, 7L, 4L), `(tostate)` = c(3, 2, 1, 3, 3, 3, 2, 3, 1), `(timelag)` = c(0.656686196569353, 0.662206339277327, 0.724425864871591, 0.813481943681836, 3.34625857770443, 3.56484543569386, 3.67860011346638, 4.29615335352719, 7.9621127679944), `(nocc)` = c(1, 1, 1, 1, 1, 1, 1, 1, 1), `(whicha)` = 1:9, `(noccsum)` = c(NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_)), mm.cov = c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1), mm.cov.agg = c(1, 1, 1, 1, 1, 1, 1, 1, 1), mm.mcov = NULL, mm.hcov = NULL, mm.icov = NULL, subject.weights = NULL), qmodel = list(nstates = 3L, iso = 4, perm = c(1, 2, 3), qperm = 1:3, npars = 3, imatrix = c(0, 0, 0, 1, 0, 0, 1, 1, 0), qmatrix = c(-0.233978505697303, 0, 0, 0.0467957011394606, -0.230665272945204, 0, 0.187182804557843, 0.230665272945204, 0), inits = c(0.0467957011394606, 0.187182804557843, 0.230665272945204), constr = 1:3, ndpars = 3L, expm = 1), qcmodel = list(npars = 0, ncovs = 0, ndpars = 0), cmodel = list(ncens = 0, censor = NULL, states = NULL, states_list = NULL, index = NULL), hmodel = list( hidden = FALSE, models = c(0, 0, 0), nipars = 0, nicoveffs = 0, totpars = 0, ncoveffs = 0, ematrix = FALSE)) 7: do.call(optfn, args) 8: msm.optim(opt.method, p, hessian, use.deriv, msmdata, qmodel, qcmodel, cmodel, hmodel, ...) 9: msm::msm(state ~ time, subject = id, data = sim_dat, qmatrix = qmat_from_tmat(tmat), gen.inits = TRUE) 10: eval(code, test_env) 11: eval(code, test_env) 12: withCallingHandlers({ eval(code, test_env) if (!handled && !is.null(test)) { skip_empty() }}, expectation = handle_expectation, skip = handle_skip, warning = handle_warning, message = handle_message, error = handle_error) 13: doTryCatch(return(expr), name, parentenv, handler) 14: tryCatchOne(expr, names, parentenv, handlers[[1L]]) 15: tryCatchList(expr, names[-nh], parentenv, handlers[-nh]) 16: doTryCatch(return(expr), name, parentenv, handler) 17: tryCatchOne(tryCatchList(expr, names[-nh], parentenv, handlers[-nh]), names[nh], parentenv, handlers[[nh]]) 18: tryCatchList(expr, classes, parentenv, handlers) 19: tryCatch(withCallingHandlers({ eval(code, test_env) if (!handled && !is.null(test)) { skip_empty() }}, expectation = handle_expectation, skip = handle_skip, warning = handle_warning, message = handle_message, error = handle_error), error = handle_fatal, skip = function(e) { }) 20: test_code(desc, code, env = parent.frame(), default_reporter = local_interactive_reporter()) 21: test_that("tmat_from_msm", { eval_times <- function(n_obs, stop_time) { cumsum(c(0, runif(n_obs - 1, 0, 2 * (stop_time - 4)/(n_obs - 1)))) } set.seed(2) sim_dat <- sim_id_weib(n = 5, n_obs = 6, stop_time = 15, eval_times = eval_times, start_state = "stable", shape = c(1, 1, 1), scale = c(5, 10, 1)) tmat <- mstate::trans.illdeath() sim_dat <- remove_redundant_observations(sim_dat, tmat) msm_mod <- msm::msm(state ~ time, subject = id, data = sim_dat, qmatrix = qmat_from_tmat(tmat), gen.inits = TRUE) tmat_msm <- tmat_from_msm(msm_mod) expect_true(all(tmat_msm == tmat, na.rm = TRUE))}) 22: eval(code, test_env) 23: eval(code, test_env) 24: withCallingHandlers({ eval(code, test_env) if (!handled && !is.null(test)) { skip_empty() }}, expectation = handle_expectation, skip = handle_skip, warning = handle_warning, message = handle_message, error = handle_error) 25: doTryCatch(return(expr), name, parentenv, handler) 26: tryCatchOne(expr, names, parentenv, handlers[[1L]]) 27: tryCatchList(expr, names[-nh], parentenv, handlers[-nh]) 28: doTryCatch(return(expr), name, parentenv, handler) 29: tryCatchOne(tryCatchList(expr, names[-nh], parentenv, handlers[-nh]), names[nh], parentenv, handlers[[nh]]) 30: tryCatchList(expr, classes, parentenv, handlers) 31: tryCatch(withCallingHandlers({ eval(code, test_env) if (!handled && !is.null(test)) { skip_empty() }}, expectation = handle_expectation, skip = handle_skip, warning = handle_warning, message = handle_message, error = handle_error), error = handle_fatal, skip = function(e) { }) 32: test_code(test = NULL, code = exprs, env = env, default_reporter = StopReporter$new()) 33: source_file(path, env = env(env), desc = desc, error_call = error_call) 34: FUN(X[[i]], ...) 35: lapply(test_paths, test_one_file, env = env, desc = desc, error_call = error_call) 36: doTryCatch(return(expr), name, parentenv, handler) 37: tryCatchOne(expr, names, parentenv, handlers[[1L]]) 38: tryCatchList(expr, classes, parentenv, handlers) 39: tryCatch(code, testthat_abort_reporter = function(cnd) { cat(conditionMessage(cnd), "\n") NULL}) 40: with_reporter(reporters$multi, lapply(test_paths, test_one_file, env = env, desc = desc, error_call = error_call)) 41: test_files_serial(test_dir = test_dir, test_package = test_package, test_paths = test_paths, load_helpers = load_helpers, reporter = reporter, env = env, stop_on_failure = stop_on_failure, stop_on_warning = stop_on_warning, desc = desc, load_package = load_package, error_call = error_call) 42: test_files(test_dir = path, test_paths = test_paths, test_package = package, reporter = reporter, load_helpers = load_helpers, env = env, stop_on_failure = stop_on_failure, stop_on_warning = stop_on_warning, load_package = load_package, parallel = parallel) 43: test_dir("testthat", package = package, reporter = reporter, ..., load_package = "installed") 44: test_check("icmstate") An irrecoverable exception occurred. R is aborting now ... Flavor: r-devel-linux-x86_64-fedora-gcc

Version: 0.1.0
Check: re-building of vignette outputs
Result: ERROR Error(s) in re-building vignettes: --- re-building ‘Overview.Rmd’ using rmarkdown *** caught segfault *** address 0x1, cause 'memory not mapped' Traceback: 1: Ccall.msm(params, do.what = "lik", ...) 2: fn(par, ...) 3: (function (par) fn(par, ...))(c(qbase = -2.30258509299405, qbase = -2.30258509299405, qbase = -2.30258509299405)) 4: optim(method = "BFGS", control = list(), par = c(qbase = -2.30258509299405, qbase = -2.30258509299405, qbase = -2.30258509299405), fn = function (params, ...) { assign("nliks", get("nliks", msm.globals) + 1, envir = msm.globals) args <- list(...) w <- args$msmdata$subject.weights if (!is.null(w)) { lik <- Ccall.msm(params, do.what = "lik.subj", ...) sum(w * lik) } else Ccall.msm(params, do.what = "lik", ...)}, hessian = TRUE, gr = function (params, ...) { w <- list(...)$msmdata$subject.weights if (!is.null(w)) { deriv <- Ccall.msm(params, do.what = "deriv.subj", ...) apply(w * deriv, 2, sum) } else Ccall.msm(params, do.what = "deriv", ...)}, msmdata = list(mf = list(`(subject)` = c(1, 1, 1, 1, 1, 2, 2, 2, 3, 3, 3, 3, 4, 4, 5, 5, 5, 5, 5, 5, 5, 6, 6, 6, 6, 6, 6, 6, 7, 7, 7, 8, 8, 8, 9, 9, 9, 9, 9, 9, 9, 10, 10, 10, 10, 10, 11, 11, 11, 11, 12, 12, 12, 13, 13, 14, 14, 15, 15, 15, 15, 15, 15, 16, 16, 16, 16, 16, 16, 16, 17, 17, 17, 17, 17, 17, 18, 18, 18, 18, 18, 18, 18, 19, 19, 20, 20, 20, 21, 21, 22, 22, 23, 23, 24, 24, 24, 25, 25, 25, 25, 25, 25, 25, 26, 26, 26, 26, 26, 27, 27, 27, 27, 27, 27, 27, 28, 28, 28, 28, 29, 29, 29, 29, 29, 29, 29, 30, 30, 30, 30, 30, 30, 30, 31, 31, 31, 32, 32, 32, 32, 32, 32, 33, 33, 33, 33, 33, 33, 33, 34, 34, 34, 34, 34, 34, 34, 35, 35, 35, 35, 35, 36, 36, 36, 36, 36, 36, 36, 37, 37, 38, 38, 38, 38, 38, 38, 38, 39, 39, 39, 39, 39, 39, 39, 40, 40, 40, 40, 41, 41, 41, 41, 41, 41, 41, 42, 42, 43, 43, 43, 44, 44, 44, 44, 44, 44, 44, 45, 45, 45, 45, 46, 46, 47, 47, 47, 47, 47, 47, 47, 48, 48, 48, 48, 48, 48, 48, 49, 49, 49, 49, 50, 50, 51, 51, 51, 51, 51, 51, 51, 52, 52, 52, 52, 52, 52, 53, 53, 53, 53, 53, 54, 54, 54, 54, 54, 54, 55, 55, 55, 56, 56, 56, 56, 56, 56, 56, 57, 57, 57, 57, 57, 57, 57, 58, 58, 58, 58, 59, 59, 59, 59, 59, 59, 59, 60, 60, 60, 60, 60, 60, 61, 61, 61, 62, 62, 63, 63, 63, 63, 63, 63, 63, 64, 64, 64, 65, 65, 65, 65, 65, 65, 66, 66, 66, 66, 66, 67, 67, 67, 67, 67, 67, 67, 68, 68, 68, 69, 69, 69, 69, 69, 69, 69, 70, 70, 71, 71, 72, 72, 72, 72, 72, 73, 73, 73, 73, 73, 73, 73, 74, 74, 74, 74, 75, 75, 75, 76, 76, 76, 76, 76, 76, 76, 77, 77, 77, 77, 77, 78, 78, 79, 79, 80, 80, 81, 81, 81, 82, 82, 83, 83, 83, 84, 84, 84, 84, 84, 84, 84, 85, 85, 85, 85, 85, 85, 85, 86, 86, 86, 86, 86, 86, 87, 87, 87, 87, 87, 87, 87, 88, 88, 88, 88, 88, 89, 89, 89, 89, 89, 89, 89, 90, 90, 91, 91, 91, 91, 91, 91, 91, 92, 92, 92, 92, 92, 92, 92, 93, 93, 94, 94, 94, 94, 94, 94, 94, 95, 95, 95, 95, 95, 95, 95, 96, 96, 96, 96, 96, 96, 96, 97, 97, 97, 97, 97, 97, 97, 98, 98, 99, 99, 99, 99, 99, 99, 100, 100), `(state)` = c(1, 1, 1, 1, 3, 1, 1, 3, 1, 2, 2, 3, 1, 3, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 1, 1, 3, 1, 1, 3, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 3, 1, 1, 1, 3, 1, 1, 3, 1, 3, 1, 3, 1, 1, 1, 1, 1, 3, 1, 1, 1, 1, 2, 2, 2, 1, 1, 1, 1, 1, 3, 1, 1, 1, 1, 2, 2, 3, 1, 3, 1, 1, 3, 1, 3, 1, 3, 1, 3, 1, 1, 3, 1, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 3, 1, 1, 1, 2, 2, 2, 2, 1, 1, 1, 3, 1, 1, 1, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 3, 1, 1, 3, 1, 1, 2, 2, 2, 3, 1, 1, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 3, 1, 1, 1, 1, 1, 1, 1, 1, 3, 1, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 2, 2, 2, 1, 1, 1, 3, 1, 2, 2, 2, 2, 2, 2, 1, 3, 1, 1, 3, 1, 2, 2, 2, 2, 2, 3, 1, 2, 2, 3, 1, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 3, 1, 1, 1, 3, 1, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 3, 1, 1, 1, 1, 3, 1, 1, 1, 1, 1, 3, 1, 2, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 3, 1, 1, 1, 3, 1, 1, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 3, 1, 1, 3, 1, 3, 1, 1, 1, 2, 2, 2, 3, 1, 1, 3, 1, 1, 1, 1, 1, 3, 1, 1, 1, 1, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1, 3, 1, 1, 1, 1, 2, 2, 3, 1, 3, 1, 3, 1, 2, 2, 2, 3, 1, 1, 1, 1, 1, 2, 2, 1, 1, 1, 3, 1, 1, 3, 1, 2, 2, 2, 2, 2, 3, 1, 1, 2, 2, 3, 1, 3, 1, 3, 1, 3, 1, 1, 3, 1, 3, 1, 1, 3, 1, 2, 2, 2, 2, 2, 3, 1, 2, 2, 2, 2, 2, 3, 1, 1, 1, 1, 1, 3, 1, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 3, 1, 1, 1, 2, 2, 2, 2, 1, 3, 1, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 3, 1, 3, 1, 1, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 1, 1, 1, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 1, 3, 1, 1, 2, 2, 2, 3, 1, 3), `(time)` = c(0, 2.20636847149581, 4.89256869722158, 7.28020209027454, 9.98367724008858, 0, 2.34688466973603, 5.50964188901708, 0, 3.19142399262637, 5.14974439656362, 6.15412876056507, 0, 3.19618484470993, 0, 3.20308243529871, 4.47773735551164, 6.51633185334504, 9.45861924672499, 10.9051416628063, 12.3502535368316, 0, 3.49339653924108, 4.20997528079897, 7.72908989805728, 9.22928994335234, 11.1143190776929, 12.6575546381064, 0, 2.90626289090142, 5.00088194524869, 0, 2.42539903847501, 4.56958096195012, 0, 2.82624841900542, 4.12761696102098, 6.67097498336807, 9.44745189324021, 10.675230666995, 13.2608282449655, 0, 3.68122910801321, 5.71226332942024, 6.78271856205538, 8.76098777120933, 0, 3.48215729743242, 5.21060689305887, 7.80616322299466, 0, 3.00667897146195, 5.75411508604884, 0, 3.09529317403212, 0, 3.18114631762728, 0, 2.43729056976736, 5.03359367279336, 6.53790118405595, 8.36233665468171, 11.0371522749774, 0, 2.25831370893866, 4.51273520803079, 7.43587055196986, 9.92281987285241, 10.2002816931345, 13.5264453790151, 0, 3.89593270933256, 5.63726937677711, 6.61658466141671, 9.29915892099962, 11.906710902229, 0, 2.67995840683579, 4.52494822023436, 6.33090786635876, 8.64433611324057, 11.0202504131012, 13.8479369427077, 0, 3.02191939670593, 0, 2.26288464106619, 4.7489737290889, 0, 3.10980124631897, 0, 2.28520816424862, 0, 3.88591411849484, 0, 2.21761263301596, 4.49545366596431, 0, 2.63428934337571, 4.55593205895275, 7.57508101547137, 9.40492502506822, 10.3300552773289, 12.128915077541, 0, 3.50941124325618, 5.24082006607205, 6.33915353333578, 8.12442810460925, 0, 2.33862182917073, 4.59730508411303, 6.3844190700911, 8.51434004260227, 10.362463645637, 12.9546274193563, 0, 3.54147408576682, 4.05557424481958, 7.05462155397981, 0, 2.27725649345666, 4.64298424078152, 6.30966322263703, 8.26445634430274, 10.4426118554547, 12.4527615923434, 0, 2.26283306768164, 5.96312692062929, 6.65402745362371, 9.01387899415568, 11.3628850295208, 12.1983382063918, 0, 2.237805116456, 4.10087931901217, 0, 2.92310368129984, 4.75043306173757, 7.98219843907282, 8.35270142741501, 11.6268704170361, 0, 2.80089949350804, 4.28228865144774, 6.38661972479895, 9.68270343355834, 11.4398279767483, 12.5344241661951, 0, 2.99000328965485, 4.16622779564932, 6.70776848122478, 9.93841761071235, 11.2494283793494, 13.3292364994995, 0, 2.62497931299731, 4.81137922545895, 7.99215474305674, 9.71016471274197, 0, 3.56436423165724, 4.53575625782833, 7.52430305909365, 9.97262317826971, 10.5872110980563, 12.7987022139132, 0, 3.62426304770634, 0, 3.94032435771078, 5.97899966686964, 6.3529040729627, 9.08426084881648, 10.7686077840626, 13.3523281016387, 0, 2.53858755948022, 4.93850188469514, 6.34360016463324, 8.73837892478332, 11.4508105451241, 12.9722982086241, 0, 2.12760493345559, 5.56909245997667, 6.83664327161387, 0, 2.16447846172377, 5.77291750116274, 6.94386146171018, 8.21820192690939, 10.6665559690446, 13.6748331384733, 0, 2.55369968339801, 0, 2.9279247620143, 4.87386222090572, 0, 2.84043178660795, 4.66704161139205, 7.72961510065943, 8.35438907146454, 10.9866374572739, 12.859426733572, 0, 3.12852768599987, 5.31232463102788, 7.95710812555626, 0, 3.66334343003109, 0, 3.01407120516524, 4.30970203410834, 6.69660410424694, 9.31964205158874, 10.6235447488725, 12.7031468185596, 0, 2.29569141380489, 5.31775521812961, 6.37013992900029, 9.90875627379864, 11.7956969840452, 13.8873941088095, 0, 3.44738150248304, 4.74071413185447, 7.56203508051112, 0, 2.714811490383, 0, 2.61610489757732, 5.39002449251711, 7.64558661123738, 8.86943528149277, 11.0294653056189, 13.3260219353251, 0, 2.28633317304775, 4.68897478748113, 6.81152716465294, 8.17062201164663, 11.8651438564993, 0, 3.75886659231037, 5.87142493529245, 6.14492126693949, 8.75751888100058, 0, 3.60337541112676, 5.47928349161521, 6.10429802676663, 8.96433914639056, 11.841035681311, 0, 2.5879835980013, 5.0017009745352, 0, 3.88501064479351, 4.2474471302703, 6.14006535848603, 9.92863407591358, 10.8850202164613, 12.7405447605997, 0, 2.34048716025427, 4.1083808587864, 7.31565613858402, 9.15632383339107, 11.974203527905, 13.2075848029926, 0, 2.12989983754233, 4.32421816373244, 6.95079584093764, 0, 3.87682749098167, 5.43166656745598, 6.32617095671594, 8.95237603690475, 11.3805134459399, 12.9217903590761, 0, 3.91029347665608, 5.42508024489507, 6.79429586650804, 8.23544122418389, 10.480232546106, 0, 2.87195280333981, 4.99573610397056, 0, 3.89363563805819, 0, 2.26822267565876, 4.04548244830221, 7.8782734121196, 8.58589744567871, 10.3286531488411, 12.7982051116414, 0, 2.91915082419291, 4.86806169711053, 0, 3.37655047513545, 5.3161151073873, 7.32668545050547, 8.94446840509772, 11.9390563322231, 0, 3.69910420756787, 5.51328981481493, 7.0652024387382, 9.74829932162538, 0, 3.45553400926292, 5.43317895755172, 6.37485274206847, 9.29213455365971, 11.0839585596696, 12.6706415195949, 0, 3.27581744641066, 5.65840212767944, 0, 3.85635509667918, 5.608781542629, 7.51739361416548, 9.91449977783486, 11.9878277573735, 13.2128819893114, 0, 2.05875433096662, 0, 2.67375661758706, 0, 2.65748743340373, 4.39429342700168, 6.23068464966491, 9.99193095741794, 0, 3.46543603530154, 5.74161110585555, 7.14434051932767, 8.02207214199007, 11.8126305225305, 13.5413072630763, 0, 2.76500924676657, 4.18809177773073, 6.09930716780946, 0, 2.2656556260772, 4.68361980235204, 0, 3.28821445722133, 4.56150042731315, 7.91527304146439, 8.31679191347212, 10.8366779834032, 12.5040195947513, 0, 2.18878053547814, 5.65543553931639, 7.05061100749299, 9.33549525495619, 0, 3.47461093589664, 0, 2.83063600305468, 0, 2.48591321799904, 0, 2.99012821121141, 4.75974518293515, 0, 3.49964332999662, 0, 2.21329663926736, 5.86952023301274, 0, 2.81158100022003, 4.30557628255337, 6.68046552268788, 9.25330969505012, 10.1147453566082, 13.7033352795988, 0, 2.42529069166631, 5.07892405567691, 6.27297517238185, 8.64973028097302, 11.2421525875106, 12.5119644971564, 0, 3.26975159766153, 4.97134421393275, 7.87635383335873, 9.71500307414681, 10.7417670753784, 0, 3.65706871962175, 4.90368302632123, 6.63175682444125, 8.19561707507819, 10.1298010731116, 13.3789147329517, 0, 3.33610120788217, 5.80909329559654, 6.60338654089719, 9.86561740934849, 0, 2.44926101760939, 4.0615131421946, 7.72406808240339, 9.37021501036361, 11.8841498191468, 13.3517075208947, 0, 3.68624029681087, 0, 3.17613278608769, 5.50300833629444, 7.73447757447138, 8.74359147436917, 11.597629099153, 12.116628785152, 0, 3.2468714187853, 4.71328281331807, 7.17585586477071, 9.82756928401068, 10.3988843658008, 12.738167247735, 0, 3.34281665552408, 0, 2.83994663972408, 4.72459656652063, 6.24685782054439, 8.59632305894047, 10.5533529794775, 13.5404507112689, 0, 3.55636260425672, 4.28757456131279, 7.03105230117217, 9.19448097096756, 11.0116860480048, 12.7721992661245, 0, 2.85219620168209, 4.02351946942508, 7.83866354078054, 8.15887938626111, 11.0147485095076, 13.640343233943, 0, 3.1967908362858, 4.84830705216154, 7.11862054141238, 9.57818894414231, 10.3354305159301, 13.9409034601413, 0, 2.94700619671494, 0, 2.14649373944849, 4.84711683681235, 7.06164872832596, 9.88540952466428, 11.4244491187856, 0, 2.94025723543018), `(obstype)` = c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1), `(obstrue)` = c(1, 1, 1, 1, 3, 1, 1, 3, 1, 2, 2, 3, 1, 3, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 1, 1, 3, 1, 1, 3, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 3, 1, 1, 1, 3, 1, 1, 3, 1, 3, 1, 3, 1, 1, 1, 1, 1, 3, 1, 1, 1, 1, 2, 2, 2, 1, 1, 1, 1, 1, 3, 1, 1, 1, 1, 2, 2, 3, 1, 3, 1, 1, 3, 1, 3, 1, 3, 1, 3, 1, 1, 3, 1, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 3, 1, 1, 1, 2, 2, 2, 2, 1, 1, 1, 3, 1, 1, 1, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 3, 1, 1, 3, 1, 1, 2, 2, 2, 3, 1, 1, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 3, 1, 1, 1, 1, 1, 1, 1, 1, 3, 1, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 2, 2, 2, 1, 1, 1, 3, 1, 2, 2, 2, 2, 2, 2, 1, 3, 1, 1, 3, 1, 2, 2, 2, 2, 2, 3, 1, 2, 2, 3, 1, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 3, 1, 1, 1, 3, 1, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 3, 1, 1, 1, 1, 3, 1, 1, 1, 1, 1, 3, 1, 2, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 3, 1, 1, 1, 3, 1, 1, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 3, 1, 1, 3, 1, 3, 1, 1, 1, 2, 2, 2, 3, 1, 1, 3, 1, 1, 1, 1, 1, 3, 1, 1, 1, 1, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1, 3, 1, 1, 1, 1, 2, 2, 3, 1, 3, 1, 3, 1, 2, 2, 2, 3, 1, 1, 1, 1, 1, 2, 2, 1, 1, 1, 3, 1, 1, 3, 1, 2, 2, 2, 2, 2, 3, 1, 1, 2, 2, 3, 1, 3, 1, 3, 1, 3, 1, 1, 3, 1, 3, 1, 1, 3, 1, 2, 2, 2, 2, 2, 3, 1, 2, 2, 2, 2, 2, 3, 1, 1, 1, 1, 1, 3, 1, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 3, 1, 1, 1, 2, 2, 2, 2, 1, 3, 1, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 3, 1, 3, 1, 1, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 1, 1, 1, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 1, 3, 1, 1, 2, 2, 2, 3, 1, 3), `(obs)` = 1:479, `(pcomb)` = c(NA, 1L, 2L, 3L, 4L, NA, 5L, 6L, NA, 7L, 8L, 9L, NA, 10L, NA, 11L, 12L, 13L, 14L, 15L, 16L, NA, 17L, 18L, 19L, 20L, 21L, 22L, NA, 23L, 24L, NA, 25L, 26L, NA, 27L, 28L, 29L, 30L, 31L, 32L, NA, 33L, 34L, 35L, 36L, NA, 37L, 38L, 39L, NA, 40L, 41L, NA, 42L, NA, 43L, NA, 44L, 45L, 46L, 47L, 48L, NA, 49L, 50L, 51L, 52L, 53L, 54L, NA, 55L, 56L, 57L, 58L, 59L, NA, 60L, 61L, 62L, 63L, 64L, 65L, NA, 66L, NA, 67L, 68L, NA, 69L, NA, 70L, NA, 71L, NA, 72L, 73L, NA, 74L, 75L, 76L, 77L, 78L, 79L, NA, 80L, 81L, 82L, 83L, NA, 84L, 85L, 86L, 87L, 88L, 89L, NA, 90L, 91L, 92L, NA, 93L, 94L, 95L, 96L, 97L, 98L, NA, 99L, 100L, 101L, 102L, 103L, 104L, NA, 105L, 106L, NA, 107L, 108L, 109L, 110L, 111L, NA, 112L, 113L, 114L, 115L, 116L, 117L, NA, 118L, 119L, 120L, 121L, 122L, 123L, NA, 124L, 125L, 126L, 127L, NA, 128L, 129L, 130L, 131L, 132L, 133L, NA, 134L, NA, 135L, 136L, 137L, 138L, 139L, 140L, NA, 141L, 142L, 143L, 144L, 145L, 146L, NA, 147L, 148L, 149L, NA, 150L, 151L, 152L, 153L, 154L, 155L, NA, 156L, NA, 157L, 158L, NA, 159L, 160L, 161L, 162L, 163L, 164L, NA, 165L, 166L, 167L, NA, 168L, NA, 169L, 170L, 171L, 172L, 173L, 174L, NA, 175L, 176L, 177L, 178L, 179L, 180L, NA, 181L, 182L, 183L, NA, 184L, NA, 185L, 186L, 187L, 188L, 189L, 190L, NA, 191L, 192L, 193L, 194L, 195L, NA, 196L, 197L, 198L, 199L, NA, 200L, 201L, 202L, 203L, 204L, NA, 205L, 206L, NA, 207L, 208L, 209L, 210L, 211L, 212L, NA, 213L, 214L, 215L, 216L, 217L, 218L, NA, 219L, 220L, 221L, NA, 222L, 223L, 224L, 225L, 226L, 227L, NA, 228L, 229L, 230L, 231L, 232L, NA, 233L, 234L, NA, 235L, NA, 236L, 237L, 238L, 239L, 240L, 241L, NA, 242L, 243L, NA, 244L, 245L, 246L, 247L, 248L, NA, 249L, 250L, 251L, 252L, NA, 253L, 254L, 255L, 256L, 257L, 258L, NA, 259L, 260L, NA, 261L, 262L, 263L, 264L, 265L, 266L, NA, 267L, NA, 268L, NA, 269L, 270L, 271L, 272L, NA, 273L, 274L, 275L, 276L, 277L, 278L, NA, 279L, 280L, 281L, NA, 282L, 283L, NA, 284L, 285L, 286L, 287L, 288L, 289L, NA, 290L, 291L, 292L, 293L, NA, 294L, NA, 295L, NA, 296L, NA, 297L, 298L, NA, 299L, NA, 300L, 301L, NA, 302L, 303L, 304L, 305L, 306L, 307L, NA, 308L, 309L, 310L, 311L, 312L, 313L, NA, 314L, 315L, 316L, 317L, 318L, NA, 319L, 320L, 321L, 322L, 323L, 324L, NA, 325L, 326L, 327L, 328L, NA, 329L, 330L, 331L, 332L, 333L, 334L, NA, 335L, NA, 336L, 337L, 338L, 339L, 340L, 341L, NA, 342L, 343L, 344L, 345L, 346L, 347L, NA, 348L, NA, 349L, 350L, 351L, 352L, 353L, 354L, NA, 355L, 356L, 357L, 358L, 359L, 360L, NA, 361L, 362L, 363L, 364L, 365L, 366L, NA, 367L, 368L, 369L, 370L, 371L, 372L, NA, 373L, NA, 374L, 375L, 376L, 377L, 378L, NA, 379L)), mf.agg = list(`(subject)` = c(53, 16, 96, 56, 32, 38, 76, 28, 91, 92, 36, 44, 54, 30, 63, 6, 95, 97, 88, 30, 84, 73, 59, 25, 67, 56, 36, 17, 3, 91, 86, 48, 10, 33, 26, 41, 96, 34, 85, 51, 69, 9, 57, 87, 40, 85, 76, 41, 5, 49, 47, 9, 47, 34, 52, 60, 77, 73, 39, 74, 60, 5, 5, 92, 89, 33, 84, 6, 15, 60, 39, 94, 99, 59, 6, 66, 59, 87, 67, 89, 65, 89, 97, 29, 76, 38, 86, 35, 87, 11, 73, 26, 72, 17, 63, 69, 33, 95, 57, 81, 63, 26, 27, 67, 25, 18, 66, 95, 15, 44, 32, 25, 72, 86, 57, 18, 27, 56, 31, 44, 54, 94, 6, 48, 56, 69, 74, 25, 87, 65, 43, 64, 29, 94, 3, 67, 10, 29, 65, 10, 5, 38, 70, 69, 47, 34, 48, 7, 33, 53, 52, 61, 40, 58, 27, 8, 99, 51, 95, 41, 29, 45, 35, 77, 58, 1, 36, 83, 99, 24, 91, 31, 60, 16, 51, 16, 27, 30, 20, 75, 63, 97, 73, 29, 24, 77, 22, 52, 48, 51, 18, 91, 27, 92, 57, 2, 30, 94, 30, 29, 84, 18, 85, 68, 47, 1, 39, 69, 39, 52, 55, 75, 85, 8, 59, 15, 36, 41, 89, 97, 92, 63, 88, 80, 20, 16, 89, 76, 39, 34, 9, 42, 84, 38, 9, 55, 27, 85, 11, 15, 17, 53, 51, 47, 35, 96, 59, 58, 44, 25, 45, 92, 85, 72, 71, 15, 18, 17, 66, 1, 99, 1, 39, 50, 38, 95, 12, 74, 51, 9, 33, 84, 57, 49, 99, 9, 18, 79, 94, 44, 96, 91, 96, 54, 61, 54, 86, 7, 67, 64, 32, 16, 43, 100, 5, 98, 94, 36, 34, 81, 65, 28, 12, 41, 47, 25, 19, 48, 44, 13, 21, 45, 2, 91, 35, 14, 3, 4, 97, 5, 57, 34, 32, 92, 87, 88, 86, 32, 68, 76, 33, 16, 88, 93, 76, 65, 40, 49, 67, 73, 77, 78, 11, 6, 82, 26, 6, 48, 28, 95, 36, 84, 54, 97, 41, 37, 83, 87, 89, 46, 10, 90, 52, 66, 30, 53, 72, 56, 73, 96, 63, 69, 59, 56, 23, 62, 17, 60, 38), `(fromstate)` = c(1, 2, 2, 1, 2, 2, 2, 1, 2, 1, 1, 2, 1, 2, 2, 2, 2, 2, 1, 2, 2, 1, 2, 2, 1, 1, 1, 1, 2, 2, 1, 1, 2, 2, 2, 2, 1, 1, 2, 1, 2, 1, 2, 2, 1, 2, 2, 2, 1, 1, 1, 1, 1, 1, 2, 2, 2, 1, 1, 1, 2, 1, 1, 1, 2, 1, 2, 2, 1, 2, 2, 2, 2, 2, 2, 1, 1, 2, 1, 1, 1, 2, 2, 1, 2, 2, 1, 1, 2, 1, 2, 2, 2, 1, 2, 1, 2, 2, 1, 1, 1, 2, 1, 1, 2, 1, 1, 2, 1, 2, 1, 2, 2, 1, 1, 1, 2, 1, 1, 2, 1, 1, 2, 1, 1, 1, 1, 2, 2, 1, 1, 1, 2, 2, 2, 1, 2, 2, 1, 2, 1, 2, 1, 2, 1, 1, 1, 1, 2, 1, 2, 1, 1, 1, 2, 1, 1, 1, 2, 1, 2, 2, 1, 1, 1, 1, 1, 1, 2, 1, 2, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 2, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 2, 2, 2, 1, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 2, 1, 1, 2, 1, 2, 1, 2, 1, 1, 1, 1, 2, 2, 1, 1, 1, 1, 2, 2, 1, 1, 2, 2, 1, 1, 1, 1, 1, 1, 1, 2, 2, 1, 2, 1, 2, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 2, 2, 1, 1, 1, 1, 1, 1, 2, 1, 2, 1, 2, 1, 1, 1, 1, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 2, 1, 2, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 2, 1, 1, 2, 1, 1, 2, 2, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 2, 1, 2, 2, 1, 1, 1, 1, 1, 1, 1, 2, 1, 2, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1), `(time)` = c(5.87142493529245, 9.92281987285241, 7.83866354078054, 3.88501064479351, 7.98219843907282, 5.97899966686964, 7.91527304146439, 3.54147408576682, 11.597629099153, 9.82756928401068, 9.97262317826971, 7.72961510065943, 5.47928349161521, 5.96312692062929, 7.8782734121196, 3.49339653924108, 3.55636260425672, 9.57818894414231, 5.80909329559654, 11.3628850295208, 9.25330969505012, 7.14434051932767, 5.43166656745598, 9.40492502506822, 5.43317895755172, 9.92863407591358, 3.56436423165724, 5.63726937677711, 5.14974439656362, 7.73447757447138, 9.71500307414681, 5.31775521812961, 5.71226332942024, 11.4398279767483, 5.24082006607205, 5.77291750116274, 2.85219620168209, 2.99000328965485, 5.07892405567691, 7.64558661123738, 11.9878277573735, 9.44745189324021, 11.974203527905, 3.65706871962175, 5.56909245997667, 11.2421525875106, 3.28821445722133, 6.94386146171018, 3.20308243529871, 3.44738150248304, 3.01407120516524, 2.82624841900542, 9.31964205158874, 9.93841761071235, 6.81152716465294, 5.42508024489507, 5.65543553931639, 5.74161110585555, 4.93850188469514, 2.76500924676657, 6.79429586650804, 10.9051416628063, 9.45861924672499, 3.2468714187853, 11.8841498191468, 2.80089949350804, 2.81158100022003, 7.72908989805728, 5.03359367279336, 3.91029347665608, 11.4508105451241, 4.72459656652063, 9.88540952466428, 11.3805134459399, 11.1143190776929, 5.51328981481493, 3.87682749098167, 6.63175682444125, 11.0839585596696, 2.44926101760939, 7.32668545050547, 7.72406808240339, 3.1967908362858, 4.64298424078152, 10.8366779834032, 9.08426084881648, 3.26975159766153, 7.99215474305674, 4.90368302632123, 3.48215729743242, 11.8126305225305, 3.50941124325618, 2.65748743340373, 3.89593270933256, 8.58589744567871, 3.85635509667918, 9.68270343355834, 11.0116860480048, 2.34048716025427, 2.99012821121141, 2.26822267565876, 6.33915353333578, 4.59730508411303, 9.29213455365971, 10.3300552773289, 4.52494822023436, 3.69910420756787, 9.19448097096756, 6.53790118405595, 2.84043178660795, 2.92310368129984, 7.57508101547137, 4.39429342700168, 7.87635383335873, 7.31565613858402, 2.67995840683579, 8.51434004260227, 10.8850202164613, 2.237805116456, 10.9866374572739, 3.60337541112676, 2.83994663972408, 9.22928994335234, 9.90875627379864, 4.2474471302703, 5.608781542629, 4.18809177773073, 2.63428934337571, 8.19561707507819, 3.37655047513545, 2.9279247620143, 2.91915082419291, 6.30966322263703, 8.59632305894047, 3.19142399262637, 3.45553400926292, 6.78271856205538, 10.4426118554547, 5.3161151073873, 3.68122910801321, 4.47773735551164, 3.94032435771078, 0, 9.91449977783486, 10.6235447488725, 11.2494283793494, 11.7956969840452, 2.90626289090142, 4.28228865144774, 3.75886659231037, 4.68897478748113, 2.87195280333981, 0, 0, 6.3844190700911, 2.42539903847501, 0, 8.86943528149277, 7.03105230117217, 0, 8.26445634430274, 3.12852768599987, 2.62497931299731, 0, 2.12989983754233, 0, 10.5872110980563, 0, 4.84711683681235, 0, 5.50300833629444, 0, 8.23544122418389, 2.25831370893866, 5.39002449251711, 0, 2.33862182917073, 0, 0, 0, 0, 4.84830705216154, 3.46543603530154, 0, 2.21761263301596, 7.05061100749299, 0, 0, 0, 11.0294653056189, 6.33090786635876, 3.17613278608769, 0, 10.3988843658008, 0, 0, 9.01387899415568, 6.24685782054439, 6.65402745362371, 2.27725649345666, 4.30557628255337, 8.64433611324057, 6.27297517238185, 3.27581744641066, 4.30970203410834, 4.89256869722158, 6.34360016463324, 7.51739361416548, 2.53858755948022, 2.28633317304775, 2.5879835980013, 2.2656556260772, 0, 0, 8.95237603690475, 0, 7.52430305909365, 8.21820192690939, 0, 7.11862054141238, 4.71328281331807, 10.3286531488411, 3.33610120788217, 0, 2.26288464106619, 7.43587055196986, 9.37021501036361, 8.31679191347212, 0, 4.16622779564932, 4.12761696102098, 0, 6.68046552268788, 10.7686077840626, 10.675230666995, 0, 10.362463645637, 8.64973028097302, 5.21060689305887, 2.43729056976736, 9.29915892099962, 6.14492126693949, 0, 6.69660410424694, 0, 11.0147485095076, 6.32617095671594, 4.32421816373244, 8.35438907146454, 0, 5.31232463102788, 7.17585586477071, 2.42529069166631, 0, 0, 8.36233665468171, 0, 6.61658466141671, 7.0652024387382, 2.20636847149581, 2.14649373944849, 7.28020209027454, 8.73837892478332, 0, 6.3529040729627, 4.28757456131279, 3.00667897146195, 0, 2.61610489757732, 6.67097498336807, 0, 0, 9.15632383339107, 4.74071413185447, 7.06164872832596, 0, 11.0202504131012, 0, 0, 0, 0, 8.74359147436917, 8.15887938626111, 6.10429802676663, 0, 8.96433914639056, 4.97134421393275, 0, 6.37485274206847, 0, 0, 4.51273520803079, 0, 0, 6.51633185334504, 0, 10.5533529794775, 4.53575625782833, 0, 0, 8.94446840509772, 4.05557424481958, 0, 10.6665559690446, 0, 4.55593205895275, 0, 2.29569141380489, 4.66704161139205, 0, 0, 0, 2.34688466973603, 0, 4.81137922545895, 0, 0, 0, 0, 0, 4.1083808587864, 6.70776848122478, 4.75043306173757, 0, 10.1298010731116, 6.60338654089719, 0, 8.35270142741501, 0, 0, 6.38661972479895, 10.2002816931345, 0, 0, 4.56150042731315, 0, 2.12760493345559, 0, 0, 0, 2.18878053547814, 0, 0, 0, 0, 0, 4.20997528079897, 6.37013992900029, 0, 0, 0, 10.1147453566082, 0, 10.3354305159301, 2.16447846172377, 0, 2.21329663926736, 0, 4.0615131421946, 0, 0, 0, 8.17062201164663, 0, 2.26283306768164, 0, 6.23068464966491, 6.14006535848603, 8.02207214199007, 4.02351946942508, 4.04548244830221, 0, 0, 0, 0, 0, 0, 0, 0), `(obstype)` = c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1), `(obstrue)` = c(1, 2, 2, 1, 2, 2, 2, 1, 2, 1, 1, 2, 1, 2, 2, 2, 2, 2, 1, 2, 2, 1, 2, 2, 1, 1, 1, 1, 2, 2, 1, 1, 2, 2, 2, 2, 1, 1, 2, 1, 2, 1, 2, 2, 1, 2, 2, 2, 1, 1, 1, 1, 1, 1, 2, 2, 2, 1, 1, 1, 2, 1, 1, 1, 2, 1, 2, 2, 1, 2, 2, 2, 2, 2, 2, 1, 1, 2, 1, 1, 1, 2, 2, 1, 2, 2, 1, 1, 2, 1, 2, 2, 2, 1, 2, 1, 2, 2, 1, 1, 1, 2, 1, 1, 2, 1, 1, 2, 1, 2, 1, 2, 2, 1, 1, 1, 2, 1, 1, 2, 1, 1, 2, 1, 1, 1, 1, 2, 2, 1, 1, 1, 2, 2, 2, 1, 2, 2, 1, 2, 1, 2, 1, 2, 1, 1, 1, 1, 2, 1, 2, 1, 1, 1, 2, 1, 1, 1, 2, 1, 2, 2, 1, 1, 1, 1, 1, 1, 2, 1, 2, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 2, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 2, 2, 2, 1, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 2, 1, 1, 2, 1, 2, 1, 2, 1, 1, 1, 1, 2, 2, 1, 1, 1, 1, 2, 2, 1, 1, 2, 2, 1, 1, 1, 1, 1, 1, 1, 2, 2, 1, 2, 1, 2, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 2, 2, 1, 1, 1, 1, 1, 1, 2, 1, 2, 1, 2, 1, 1, 1, 1, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 2, 1, 2, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 2, 1, 1, 2, 1, 1, 2, 2, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 2, 1, 2, 2, 1, 1, 1, 1, 1, 1, 1, 2, 1, 2, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1), `(obs)` = c(251L, 69L, 460L, 264L, 142L, 175L, 363L, 119L, 432L, 438L, 168L, 206L, 256L, 131L, 302L, 24L, 451L, 468L, 415L, 134L, 390L, 349L, 283L, 103L, 322L, 267L, 165L, 74L, 12L, 430L, 404L, 225L, 45L, 150L, 108L, 193L, 458L, 153L, 395L, 239L, 335L, 40L, 275L, 407L, 189L, 398L, 361L, 194L, 17L, 231L, 217L, 37L, 220L, 156L, 246L, 290L, 369L, 348L, 182L, 354L, 291L, 21L, 20L, 435L, 423L, 146L, 387L, 26L, 61L, 289L, 185L, 445L, 477L, 286L, 28L, 317L, 282L, 409L, 325L, 419L, 312L, 421L, 465L, 124L, 365L, 177L, 401L, 162L, 408L, 49L, 351L, 107L, 342L, 73L, 303L, 331L, 149L, 455L, 271L, 379L, 300L, 109L, 113L, 324L, 104L, 80L, 316L, 454L, 62L, 204L, 140L, 102L, 343L, 403L, 273L, 79L, 115L, 268L, 137L, 208L, 255L, 444L, 27L, 227L, 265L, 332L, 355L, 100L, 410L, 310L, 201L, 307L, 125L, 447L, 11L, 321L, 46L, 127L, 311L, 44L, 18L, 174L, 337L, 334L, 221L, 157L, 228L, 31L, 147L, 250L, 245L, 295L, 187L, 277L, 114L, 34L, 473L, 240L, 453L, 191L, 126L, 211L, 160L, 367L, 278L, 2L, 169L, 383L, 475L, 96L, 429L, 136L, 292L, 66L, 238L, 65L, 112L, 129L, 87L, 357L, 299L, 466L, 347L, 122L, 97L, 370L, 92L, 243L, 223L, 241L, 81L, 428L, 111L, 439L, 270L, 7L, 133L, 446L, 132L, 123L, 388L, 82L, 396L, 328L, 218L, 4L, 183L, 333L, 181L, 244L, 261L, 358L, 393L, 33L, 285L, 59L, 167L, 195L, 418L, 467L, 436L, 304L, 414L, 376L, 88L, 68L, 422L, 364L, 180L, 154L, 38L, 198L, 389L, 178L, 41L, 260L, 116L, 397L, 50L, 60L, 76L, 252L, 236L, 219L, 159L, 462L, 284L, 279L, 207L, 99L, 212L, 437L, 394L, 341L, 339L, 63L, 78L, 75L, 318L, 3L, 474L, 5L, 184L, 234L, 176L, 452L, 53L, 353L, 237L, 39L, 145L, 386L, 274L, 232L, 476L, 36L, 83L, 374L, 443L, 203L, 457L, 431L, 461L, 257L, 294L, 258L, 402L, 30L, 323L, 306L, 139L, 67L, 200L, 479L, 19L, 471L, 448L, 166L, 152L, 378L, 313L, 120L, 52L, 196L, 216L, 101L, 85L, 224L, 205L, 55L, 90L, 210L, 8L, 427L, 161L, 57L, 10L, 14L, 464L, 16L, 272L, 155L, 141L, 434L, 411L, 416L, 400L, 143L, 327L, 360L, 148L, 70L, 413L, 441L, 362L, 309L, 188L, 230L, 320L, 346L, 368L, 372L, 48L, 23L, 381L, 106L, 25L, 226L, 118L, 450L, 164L, 391L, 254L, 469L, 192L, 171L, 384L, 406L, 420L, 214L, 43L, 425L, 247L, 315L, 130L, 249L, 344L, 266L, 350L, 459L, 301L, 330L, 281L, 263L, 94L, 297L, 72L, 288L, 173L), `(tostate)` = c(1, 2, 2, 1, 2, 2, 2, 1, 2, 1, 1, 2, 1, 2, 2, 2, 2, 2, 1, 3, 2, 1, 2, 2, 1, 1, 1, 1, 3, 2, 3, 1, 2, 2, 2, 2, 1, 1, 2, 1, 3, 1, 3, 2, 3, 3, 2, 2, 1, 1, 1, 1, 1, 1, 2, 2, 2, 1, 1, 1, 2, 1, 1, 1, 2, 2, 2, 2, 1, 2, 2, 2, 3, 2, 2, 1, 2, 2, 1, 1, 1, 2, 2, 2, 3, 2, 1, 3, 2, 1, 2, 2, 2, 1, 2, 1, 2, 2, 1, 3, 1, 3, 2, 1, 2, 1, 1, 2, 1, 2, 2, 2, 2, 1, 2, 1, 2, 1, 3, 3, 1, 2, 2, 1, 1, 1, 3, 2, 2, 1, 3, 3, 2, 2, 2, 1, 3, 2, 1, 2, 1, 2, 3, 2, 1, 2, 3, 3, 2, 1, 2, 3, 1, 1, 2, 3, 1, 1, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 2, 1, 2, 1, 3, 1, 1, 1, 1, 2, 1, 1, 1, 2, 1, 1, 3, 3, 3, 1, 1, 1, 2, 2, 1, 3, 1, 1, 2, 2, 2, 1, 2, 2, 2, 3, 1, 1, 2, 2, 1, 2, 3, 3, 2, 1, 2, 1, 1, 2, 1, 2, 1, 3, 1, 3, 3, 2, 2, 2, 1, 1, 1, 3, 2, 2, 1, 2, 2, 2, 3, 1, 3, 3, 1, 1, 1, 2, 2, 3, 2, 2, 3, 1, 2, 2, 3, 3, 1, 1, 3, 1, 2, 3, 2, 3, 2, 2, 3, 1, 1, 1, 1, 2, 2, 3, 2, 1, 3, 3, 1, 2, 1, 2, 2, 1, 1, 3, 1, 1, 1, 1, 1, 1, 1, 3, 1, 3, 2, 1, 1, 1, 3, 3, 1, 2, 1, 2, 3, 1, 2, 3, 3, 2, 3, 2, 1, 3, 2, 3, 2, 1, 1, 1, 2, 1, 2, 3, 1, 3, 1, 2, 2, 2, 1, 3, 2, 1, 1, 1, 1, 1, 2, 3, 1, 2, 3, 2, 2, 1, 1, 2, 1, 3, 1, 2, 2, 3, 3, 2, 2, 3, 2, 3, 3, 1, 2, 1, 3, 1, 2, 2, 2, 1, 1, 1, 3, 3, 1, 2, 2), `(timelag)` = c(0.273496331647038, 0.277461820282042, 0.320215845480561, 0.362436485476792, 0.370502988342196, 0.373904406093061, 0.401518872007728, 0.514100159052759, 0.518999685999006, 0.57131508179009, 0.614587919786572, 0.624773970805109, 0.625014535151422, 0.690900532994419, 0.707624033559114, 0.716578741557896, 0.731211957056075, 0.757241571787745, 0.794293245300651, 0.835453176870942, 0.861435661558062, 0.877731622662395, 0.894504389259964, 0.925130252260715, 0.941673784516752, 0.956386140547693, 0.971392026171088, 0.979315284639597, 1.00438436400145, 1.00911389989778, 1.02676400123164, 1.05238471087068, 1.07045523263514, 1.09459618944675, 1.09833346726373, 1.17094396054745, 1.17132326774299, 1.17622450599447, 1.19405111670494, 1.22384867025539, 1.22505423193797, 1.22777877375484, 1.23338127508759, 1.24661430669948, 1.26755081163719, 1.2698119096458, 1.27328597009182, 1.2743404651992, 1.27465492021292, 1.29333262937143, 1.2956308289431, 1.30136854201555, 1.30390269728377, 1.31101076863706, 1.35909484699368, 1.36921562161297, 1.3951754681766, 1.40272941347212, 1.4050982799381, 1.42308253096417, 1.44114535767585, 1.44511187402532, 1.44652241608128, 1.46641139453277, 1.46755770174786, 1.4813891579397, 1.49399528233334, 1.50020004529506, 1.5043075112626, 1.51478676823899, 1.52148766350001, 1.52226125402376, 1.53903959412128, 1.54127691313624, 1.54323556041345, 1.55191262392327, 1.55483907647431, 1.56386025063694, 1.58668295992538, 1.61225212458521, 1.61778295459226, 1.64614692796022, 1.65151621587574, 1.66667898185551, 1.66734161134809, 1.68434693524614, 1.70159261627123, 1.71800996968523, 1.72807379812002, 1.72844959562644, 1.72867674054578, 1.73140882281587, 1.73680599359795, 1.74133666744456, 1.7427557031624, 1.75242644594982, 1.75712454319, 1.76051321811974, 1.76789369853213, 1.76961697172374, 1.77725977264345, 1.78527457127348, 1.78711398597807, 1.79182400600985, 1.79885980021209, 1.80595964612439, 1.81418560724705, 1.81720507703722, 1.82443547062576, 1.8266098247841, 1.82732938043773, 1.82984400959685, 1.83639122266322, 1.83864924078807, 1.84066769480705, 1.84498981339857, 1.84812360303476, 1.85552454413846, 1.86307420255616, 1.87278927629814, 1.87590808048844, 1.88464992679656, 1.88502913434058, 1.88694071024656, 1.89261822821572, 1.90861207153648, 1.91121539007872, 1.92164271557704, 1.9341839980334, 1.93956463225186, 1.94593745889142, 1.94891087291762, 1.95479312166572, 1.95702992053702, 1.95832040393725, 1.9776449482888, 1.97826920915395, 2.01014973688871, 2.01057034311816, 2.03103422140703, 2.0385944978334, 2.03867530915886, 2.05875433096662, 2.07332797953859, 2.0796020696871, 2.07980812015012, 2.09169712476432, 2.09461905434728, 2.1043310733512, 2.11255834298208, 2.12255237717181, 2.12378330063075, 2.12760493345559, 2.12989983754233, 2.12992097251117, 2.14418192347512, 2.14649373944849, 2.16003002412617, 2.16342866979539, 2.16447846172377, 2.178155511152, 2.18379694502801, 2.18639991246164, 2.18878053547814, 2.19431832619011, 2.20636847149581, 2.21149111585692, 2.21329663926736, 2.21453189151362, 2.21761263301596, 2.23146923817694, 2.237805116456, 2.24479132192209, 2.25442149909213, 2.25556211872026, 2.25831370893866, 2.2586832549423, 2.26283306768164, 2.26288464106619, 2.2656556260772, 2.26822267565876, 2.27031348925084, 2.27617507055402, 2.27725649345666, 2.27784103294834, 2.2848842474632, 2.28520816424862, 2.28633317304775, 2.29569141380489, 2.29655662970617, 2.31342824688181, 2.32687555020675, 2.33862182917073, 2.3392828819342, 2.34048716025427, 2.34688466973603, 2.34900603536516, 2.34946523839608, 2.35985154053196, 2.36572774732485, 2.37488924013451, 2.3759142998606, 2.37675510859117, 2.38258468126878, 2.3869020701386, 2.38763339305297, 2.39477876015007, 2.39710616366938, 2.39991432521492, 2.40264161443338, 2.4137173765339, 2.41796417627484, 2.42529069166631, 2.42539903847501, 2.42813740903512, 2.43729056976736, 2.44832011917606, 2.44835404213518, 2.44926101760939, 2.45956840272993, 2.46257305145264, 2.46955196280032, 2.47299208771437, 2.48591321799904, 2.48608908802271, 2.48694932088256, 2.51393480878323, 2.51988606993109, 2.53858755948022, 2.54154068557546, 2.54335802234709, 2.55369968339801, 2.57284417236224, 2.58372031757608, 2.58559757797047, 2.5879835980013, 2.59216377371922, 2.59242230653763, 2.59555632993579, 2.596303103026, 2.60755198122934, 2.61259761406109, 2.61610489757732, 2.6230379473418, 2.62497931299731, 2.62559472443536, 2.62620508018881, 2.6265776772052, 2.63224838580936, 2.63428934337571, 2.64478349452838, 2.65171341923997, 2.6536333640106, 2.65748743340373, 2.67375661758706, 2.6748156202957, 2.67995840683579, 2.68257425958291, 2.68309688288718, 2.68620022572577, 2.70062309736386, 2.70347514981404, 2.71243162034079, 2.714811490383, 2.73135677585378, 2.74347773985937, 2.74743611458689, 2.76500924676657, 2.7739195949398, 2.77647690987214, 2.80089949350804, 2.81158100022003, 2.81787969451398, 2.82132094865665, 2.82376079633832, 2.82624841900542, 2.82768652960658, 2.83063600305468, 2.83994663972408, 2.84043178660795, 2.85219620168209, 2.85403762478381, 2.85586912324652, 2.86004111962393, 2.87195280333981, 2.87669653492048, 2.90500961942598, 2.90626289090142, 2.91728181159124, 2.91915082419291, 2.92310368129984, 2.92313534393907, 2.9279247620143, 2.94025723543018, 2.94228739337996, 2.94700619671494, 2.98709773179144, 2.98854680126533, 2.99000328965485, 2.99012821121141, 2.99458792712539, 2.99904730916023, 3.00667897146195, 3.00827716942877, 3.01407120516524, 3.01914895651862, 3.02191939670593, 3.02206380432472, 3.06257348926738, 3.09529317403212, 3.10980124631897, 3.12852768599987, 3.16275721928105, 3.17613278608769, 3.18077551759779, 3.18114631762728, 3.19142399262637, 3.19618484470993, 3.1967908362858, 3.20308243529871, 3.20727527979761, 3.23064912948757, 3.23176537733525, 3.2468714187853, 3.24911365984008, 3.2622308684513, 3.26975159766153, 3.2741689896211, 3.27581744641066, 3.28821445722133, 3.2960837087594, 3.3261636858806, 3.33610120788217, 3.34281665552408, 3.35377261415124, 3.37655047513545, 3.44148752652109, 3.44738150248304, 3.45553400926292, 3.46543603530154, 3.46665500383824, 3.47461093589664, 3.48215729743242, 3.49339653924108, 3.49964332999662, 3.50941124325618, 3.51911461725831, 3.53861634479836, 3.54147408576682, 3.55636260425672, 3.56436423165724, 3.58858992299065, 3.60337541112676, 3.60547294421121, 3.60843903943896, 3.62426304770634, 3.65622359374538, 3.65706871962175, 3.66255494020879, 3.66334343003109, 3.68122910801321, 3.68624029681087, 3.69452184485272, 3.69910420756787, 3.70029385294765, 3.75886659231037, 3.76124630775303, 3.78856871742755, 3.79055838054046, 3.81514407135546, 3.83279096381739, 3.85635509667918, 3.87682749098167, 3.88501064479351, 3.88591411849484, 3.89363563805819, 3.89593270933256, 3.91029347665608, 3.94032435771078 ), `(nocc)` = c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1), `(whicha)` = 1:379, `(noccsum)` = c(NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, 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NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_ )), mm.cov = c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 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1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1), mm.cov.agg = c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1), mm.mcov = NULL, mm.hcov = NULL, mm.icov = NULL, subject.weights = NULL), qmodel = list(nstates = 3L, iso = 4, perm = c(1, 2, 3), qperm = 1:3, npars = 3, imatrix = c(0, 0, 0, 1, 0, 0, 1, 1, 0), qmatrix = c(-0.2, 0, 0, 0.1, -0.1, 0, 0.1, 0.1, 0), inits = c(0.1, 0.1, 0.1), constr = 1:3, ndpars = 3L, expm = 1), qcmodel = list(npars = 0, ncovs = 0, ndpars = 0), cmodel = list(ncens = 0, censor = NULL, states = NULL, states_list = NULL, index = NULL), hmodel = list(hidden = FALSE, models = c(0, 0, 0), nipars = 0, nicoveffs = 0, totpars = 0, ncoveffs = 0, ematrix = FALSE), paramdata = list(inits = c(qbase = -2.30258509299405, qbase = -2.30258509299405, qbase = -2.30258509299405), plabs = c("qbase", "qbase", "qbase"), allinits = c(qbase = -2.30258509299405, qbase = -2.30258509299405, qbase = -2.30258509299405), hmmpars = integer(0), fixed = FALSE, fixedpars = integer(0), optpars = 1:3, auxpars = integer(0), constr = c(1, 2, 3), npars = 3, duppars = integer(0), nfix = 0L, nopt = 3L, ndup = 0L, ranges = c(0, 0, 0, Inf, Inf, Inf), params = c(qbase = -2.30258509299405, qbase = -2.30258509299405, qbase = -2.30258509299405))) 5: do.call("optim", optim.args) 6: msm.optim.optim(p = list(inits = c(qbase = -2.30258509299405, qbase = -2.30258509299405, qbase = -2.30258509299405), plabs = c("qbase", "qbase", "qbase"), allinits = c(qbase = -2.30258509299405, qbase = -2.30258509299405, qbase = -2.30258509299405), hmmpars = integer(0), fixed = FALSE, fixedpars = integer(0), optpars = 1:3, auxpars = integer(0), constr = c(1, 2, 3), npars = 3, duppars = integer(0), nfix = 0L, nopt = 3L, ndup = 0L, ranges = c(0, 0, 0, Inf, Inf, Inf), params = c(qbase = -2.30258509299405, qbase = -2.30258509299405, qbase = -2.30258509299405)), gr = function (params, ...) { w <- list(...)$msmdata$subject.weights if (!is.null(w)) { deriv <- Ccall.msm(params, do.what = "deriv.subj", ...) apply(w * deriv, 2, sum) } else Ccall.msm(params, do.what = "deriv", ...)}, hessian = TRUE, msmdata = list(mf = list(`(subject)` = c(1, 1, 1, 1, 1, 2, 2, 2, 3, 3, 3, 3, 4, 4, 5, 5, 5, 5, 5, 5, 5, 6, 6, 6, 6, 6, 6, 6, 7, 7, 7, 8, 8, 8, 9, 9, 9, 9, 9, 9, 9, 10, 10, 10, 10, 10, 11, 11, 11, 11, 12, 12, 12, 13, 13, 14, 14, 15, 15, 15, 15, 15, 15, 16, 16, 16, 16, 16, 16, 16, 17, 17, 17, 17, 17, 17, 18, 18, 18, 18, 18, 18, 18, 19, 19, 20, 20, 20, 21, 21, 22, 22, 23, 23, 24, 24, 24, 25, 25, 25, 25, 25, 25, 25, 26, 26, 26, 26, 26, 27, 27, 27, 27, 27, 27, 27, 28, 28, 28, 28, 29, 29, 29, 29, 29, 29, 29, 30, 30, 30, 30, 30, 30, 30, 31, 31, 31, 32, 32, 32, 32, 32, 32, 33, 33, 33, 33, 33, 33, 33, 34, 34, 34, 34, 34, 34, 34, 35, 35, 35, 35, 35, 36, 36, 36, 36, 36, 36, 36, 37, 37, 38, 38, 38, 38, 38, 38, 38, 39, 39, 39, 39, 39, 39, 39, 40, 40, 40, 40, 41, 41, 41, 41, 41, 41, 41, 42, 42, 43, 43, 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95, 95, 95, 95, 95, 96, 96, 96, 96, 96, 96, 96, 97, 97, 97, 97, 97, 97, 97, 98, 98, 99, 99, 99, 99, 99, 99, 100, 100), `(state)` = c(1, 1, 1, 1, 3, 1, 1, 3, 1, 2, 2, 3, 1, 3, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 1, 1, 3, 1, 1, 3, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 3, 1, 1, 1, 3, 1, 1, 3, 1, 3, 1, 3, 1, 1, 1, 1, 1, 3, 1, 1, 1, 1, 2, 2, 2, 1, 1, 1, 1, 1, 3, 1, 1, 1, 1, 2, 2, 3, 1, 3, 1, 1, 3, 1, 3, 1, 3, 1, 3, 1, 1, 3, 1, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 3, 1, 1, 1, 2, 2, 2, 2, 1, 1, 1, 3, 1, 1, 1, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 3, 1, 1, 3, 1, 1, 2, 2, 2, 3, 1, 1, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 3, 1, 1, 1, 1, 1, 1, 1, 1, 3, 1, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 2, 2, 2, 1, 1, 1, 3, 1, 2, 2, 2, 2, 2, 2, 1, 3, 1, 1, 3, 1, 2, 2, 2, 2, 2, 3, 1, 2, 2, 3, 1, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 3, 1, 1, 1, 3, 1, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 3, 1, 1, 1, 1, 3, 1, 1, 1, 1, 1, 3, 1, 2, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 3, 1, 1, 1, 3, 1, 1, 2, 2, 2, 2, 2, 1, 2, 2, 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3.89363563805819, 0, 2.26822267565876, 4.04548244830221, 7.8782734121196, 8.58589744567871, 10.3286531488411, 12.7982051116414, 0, 2.91915082419291, 4.86806169711053, 0, 3.37655047513545, 5.3161151073873, 7.32668545050547, 8.94446840509772, 11.9390563322231, 0, 3.69910420756787, 5.51328981481493, 7.0652024387382, 9.74829932162538, 0, 3.45553400926292, 5.43317895755172, 6.37485274206847, 9.29213455365971, 11.0839585596696, 12.6706415195949, 0, 3.27581744641066, 5.65840212767944, 0, 3.85635509667918, 5.608781542629, 7.51739361416548, 9.91449977783486, 11.9878277573735, 13.2128819893114, 0, 2.05875433096662, 0, 2.67375661758706, 0, 2.65748743340373, 4.39429342700168, 6.23068464966491, 9.99193095741794, 0, 3.46543603530154, 5.74161110585555, 7.14434051932767, 8.02207214199007, 11.8126305225305, 13.5413072630763, 0, 2.76500924676657, 4.18809177773073, 6.09930716780946, 0, 2.2656556260772, 4.68361980235204, 0, 3.28821445722133, 4.56150042731315, 7.91527304146439, 8.31679191347212, 10.8366779834032, 12.5040195947513, 0, 2.18878053547814, 5.65543553931639, 7.05061100749299, 9.33549525495619, 0, 3.47461093589664, 0, 2.83063600305468, 0, 2.48591321799904, 0, 2.99012821121141, 4.75974518293515, 0, 3.49964332999662, 0, 2.21329663926736, 5.86952023301274, 0, 2.81158100022003, 4.30557628255337, 6.68046552268788, 9.25330969505012, 10.1147453566082, 13.7033352795988, 0, 2.42529069166631, 5.07892405567691, 6.27297517238185, 8.64973028097302, 11.2421525875106, 12.5119644971564, 0, 3.26975159766153, 4.97134421393275, 7.87635383335873, 9.71500307414681, 10.7417670753784, 0, 3.65706871962175, 4.90368302632123, 6.63175682444125, 8.19561707507819, 10.1298010731116, 13.3789147329517, 0, 3.33610120788217, 5.80909329559654, 6.60338654089719, 9.86561740934849, 0, 2.44926101760939, 4.0615131421946, 7.72406808240339, 9.37021501036361, 11.8841498191468, 13.3517075208947, 0, 3.68624029681087, 0, 3.17613278608769, 5.50300833629444, 7.73447757447138, 8.74359147436917, 11.597629099153, 12.116628785152, 0, 3.2468714187853, 4.71328281331807, 7.17585586477071, 9.82756928401068, 10.3988843658008, 12.738167247735, 0, 3.34281665552408, 0, 2.83994663972408, 4.72459656652063, 6.24685782054439, 8.59632305894047, 10.5533529794775, 13.5404507112689, 0, 3.55636260425672, 4.28757456131279, 7.03105230117217, 9.19448097096756, 11.0116860480048, 12.7721992661245, 0, 2.85219620168209, 4.02351946942508, 7.83866354078054, 8.15887938626111, 11.0147485095076, 13.640343233943, 0, 3.1967908362858, 4.84830705216154, 7.11862054141238, 9.57818894414231, 10.3354305159301, 13.9409034601413, 0, 2.94700619671494, 0, 2.14649373944849, 4.84711683681235, 7.06164872832596, 9.88540952466428, 11.4244491187856, 0, 2.94025723543018), `(obstype)` = c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1), `(obstrue)` = c(1, 1, 1, 1, 3, 1, 1, 3, 1, 2, 2, 3, 1, 3, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 1, 1, 3, 1, 1, 3, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 3, 1, 1, 1, 3, 1, 1, 3, 1, 3, 1, 3, 1, 1, 1, 1, 1, 3, 1, 1, 1, 1, 2, 2, 2, 1, 1, 1, 1, 1, 3, 1, 1, 1, 1, 2, 2, 3, 1, 3, 1, 1, 3, 1, 3, 1, 3, 1, 3, 1, 1, 3, 1, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 3, 1, 1, 1, 2, 2, 2, 2, 1, 1, 1, 3, 1, 1, 1, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 3, 1, 1, 3, 1, 1, 2, 2, 2, 3, 1, 1, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 3, 1, 1, 1, 1, 1, 1, 1, 1, 3, 1, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 2, 2, 2, 1, 1, 1, 3, 1, 2, 2, 2, 2, 2, 2, 1, 3, 1, 1, 3, 1, 2, 2, 2, 2, 2, 3, 1, 2, 2, 3, 1, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 3, 1, 1, 1, 3, 1, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 3, 1, 1, 1, 1, 3, 1, 1, 1, 1, 1, 3, 1, 2, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 3, 1, 1, 1, 3, 1, 1, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 3, 1, 1, 3, 1, 3, 1, 1, 1, 2, 2, 2, 3, 1, 1, 3, 1, 1, 1, 1, 1, 3, 1, 1, 1, 1, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1, 3, 1, 1, 1, 1, 2, 2, 3, 1, 3, 1, 3, 1, 2, 2, 2, 3, 1, 1, 1, 1, 1, 2, 2, 1, 1, 1, 3, 1, 1, 3, 1, 2, 2, 2, 2, 2, 3, 1, 1, 2, 2, 3, 1, 3, 1, 3, 1, 3, 1, 1, 3, 1, 3, 1, 1, 3, 1, 2, 2, 2, 2, 2, 3, 1, 2, 2, 2, 2, 2, 3, 1, 1, 1, 1, 1, 3, 1, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 3, 1, 1, 1, 2, 2, 2, 2, 1, 3, 1, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 3, 1, 3, 1, 1, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 1, 1, 1, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 1, 3, 1, 1, 2, 2, 2, 3, 1, 3), `(obs)` = 1:479, `(pcomb)` = c(NA, 1L, 2L, 3L, 4L, NA, 5L, 6L, NA, 7L, 8L, 9L, NA, 10L, NA, 11L, 12L, 13L, 14L, 15L, 16L, NA, 17L, 18L, 19L, 20L, 21L, 22L, NA, 23L, 24L, NA, 25L, 26L, NA, 27L, 28L, 29L, 30L, 31L, 32L, NA, 33L, 34L, 35L, 36L, NA, 37L, 38L, 39L, NA, 40L, 41L, NA, 42L, NA, 43L, NA, 44L, 45L, 46L, 47L, 48L, NA, 49L, 50L, 51L, 52L, 53L, 54L, NA, 55L, 56L, 57L, 58L, 59L, NA, 60L, 61L, 62L, 63L, 64L, 65L, NA, 66L, NA, 67L, 68L, NA, 69L, NA, 70L, NA, 71L, NA, 72L, 73L, NA, 74L, 75L, 76L, 77L, 78L, 79L, NA, 80L, 81L, 82L, 83L, NA, 84L, 85L, 86L, 87L, 88L, 89L, NA, 90L, 91L, 92L, NA, 93L, 94L, 95L, 96L, 97L, 98L, NA, 99L, 100L, 101L, 102L, 103L, 104L, NA, 105L, 106L, NA, 107L, 108L, 109L, 110L, 111L, NA, 112L, 113L, 114L, 115L, 116L, 117L, NA, 118L, 119L, 120L, 121L, 122L, 123L, NA, 124L, 125L, 126L, 127L, NA, 128L, 129L, 130L, 131L, 132L, 133L, NA, 134L, NA, 135L, 136L, 137L, 138L, 139L, 140L, NA, 141L, 142L, 143L, 144L, 145L, 146L, NA, 147L, 148L, 149L, NA, 150L, 151L, 152L, 153L, 154L, 155L, NA, 156L, NA, 157L, 158L, NA, 159L, 160L, 161L, 162L, 163L, 164L, NA, 165L, 166L, 167L, NA, 168L, NA, 169L, 170L, 171L, 172L, 173L, 174L, NA, 175L, 176L, 177L, 178L, 179L, 180L, NA, 181L, 182L, 183L, NA, 184L, NA, 185L, 186L, 187L, 188L, 189L, 190L, NA, 191L, 192L, 193L, 194L, 195L, NA, 196L, 197L, 198L, 199L, NA, 200L, 201L, 202L, 203L, 204L, NA, 205L, 206L, NA, 207L, 208L, 209L, 210L, 211L, 212L, NA, 213L, 214L, 215L, 216L, 217L, 218L, NA, 219L, 220L, 221L, NA, 222L, 223L, 224L, 225L, 226L, 227L, NA, 228L, 229L, 230L, 231L, 232L, NA, 233L, 234L, NA, 235L, NA, 236L, 237L, 238L, 239L, 240L, 241L, NA, 242L, 243L, NA, 244L, 245L, 246L, 247L, 248L, NA, 249L, 250L, 251L, 252L, NA, 253L, 254L, 255L, 256L, 257L, 258L, NA, 259L, 260L, NA, 261L, 262L, 263L, 264L, 265L, 266L, NA, 267L, NA, 268L, NA, 269L, 270L, 271L, 272L, NA, 273L, 274L, 275L, 276L, 277L, 278L, NA, 279L, 280L, 281L, NA, 282L, 283L, NA, 284L, 285L, 286L, 287L, 288L, 289L, NA, 290L, 291L, 292L, 293L, NA, 294L, NA, 295L, NA, 296L, NA, 297L, 298L, NA, 299L, NA, 300L, 301L, NA, 302L, 303L, 304L, 305L, 306L, 307L, NA, 308L, 309L, 310L, 311L, 312L, 313L, NA, 314L, 315L, 316L, 317L, 318L, NA, 319L, 320L, 321L, 322L, 323L, 324L, NA, 325L, 326L, 327L, 328L, NA, 329L, 330L, 331L, 332L, 333L, 334L, NA, 335L, NA, 336L, 337L, 338L, 339L, 340L, 341L, NA, 342L, 343L, 344L, 345L, 346L, 347L, NA, 348L, NA, 349L, 350L, 351L, 352L, 353L, 354L, NA, 355L, 356L, 357L, 358L, 359L, 360L, NA, 361L, 362L, 363L, 364L, 365L, 366L, NA, 367L, 368L, 369L, 370L, 371L, 372L, NA, 373L, NA, 374L, 375L, 376L, 377L, 378L, NA, 379L)), mf.agg = list(`(subject)` = c(53, 16, 96, 56, 32, 38, 76, 28, 91, 92, 36, 44, 54, 30, 63, 6, 95, 97, 88, 30, 84, 73, 59, 25, 67, 56, 36, 17, 3, 91, 86, 48, 10, 33, 26, 41, 96, 34, 85, 51, 69, 9, 57, 87, 40, 85, 76, 41, 5, 49, 47, 9, 47, 34, 52, 60, 77, 73, 39, 74, 60, 5, 5, 92, 89, 33, 84, 6, 15, 60, 39, 94, 99, 59, 6, 66, 59, 87, 67, 89, 65, 89, 97, 29, 76, 38, 86, 35, 87, 11, 73, 26, 72, 17, 63, 69, 33, 95, 57, 81, 63, 26, 27, 67, 25, 18, 66, 95, 15, 44, 32, 25, 72, 86, 57, 18, 27, 56, 31, 44, 54, 94, 6, 48, 56, 69, 74, 25, 87, 65, 43, 64, 29, 94, 3, 67, 10, 29, 65, 10, 5, 38, 70, 69, 47, 34, 48, 7, 33, 53, 52, 61, 40, 58, 27, 8, 99, 51, 95, 41, 29, 45, 35, 77, 58, 1, 36, 83, 99, 24, 91, 31, 60, 16, 51, 16, 27, 30, 20, 75, 63, 97, 73, 29, 24, 77, 22, 52, 48, 51, 18, 91, 27, 92, 57, 2, 30, 94, 30, 29, 84, 18, 85, 68, 47, 1, 39, 69, 39, 52, 55, 75, 85, 8, 59, 15, 36, 41, 89, 97, 92, 63, 88, 80, 20, 16, 89, 76, 39, 34, 9, 42, 84, 38, 9, 55, 27, 85, 11, 15, 17, 53, 51, 47, 35, 96, 59, 58, 44, 25, 45, 92, 85, 72, 71, 15, 18, 17, 66, 1, 99, 1, 39, 50, 38, 95, 12, 74, 51, 9, 33, 84, 57, 49, 99, 9, 18, 79, 94, 44, 96, 91, 96, 54, 61, 54, 86, 7, 67, 64, 32, 16, 43, 100, 5, 98, 94, 36, 34, 81, 65, 28, 12, 41, 47, 25, 19, 48, 44, 13, 21, 45, 2, 91, 35, 14, 3, 4, 97, 5, 57, 34, 32, 92, 87, 88, 86, 32, 68, 76, 33, 16, 88, 93, 76, 65, 40, 49, 67, 73, 77, 78, 11, 6, 82, 26, 6, 48, 28, 95, 36, 84, 54, 97, 41, 37, 83, 87, 89, 46, 10, 90, 52, 66, 30, 53, 72, 56, 73, 96, 63, 69, 59, 56, 23, 62, 17, 60, 38), `(fromstate)` = c(1, 2, 2, 1, 2, 2, 2, 1, 2, 1, 1, 2, 1, 2, 2, 2, 2, 2, 1, 2, 2, 1, 2, 2, 1, 1, 1, 1, 2, 2, 1, 1, 2, 2, 2, 2, 1, 1, 2, 1, 2, 1, 2, 2, 1, 2, 2, 2, 1, 1, 1, 1, 1, 1, 2, 2, 2, 1, 1, 1, 2, 1, 1, 1, 2, 1, 2, 2, 1, 2, 2, 2, 2, 2, 2, 1, 1, 2, 1, 1, 1, 2, 2, 1, 2, 2, 1, 1, 2, 1, 2, 2, 2, 1, 2, 1, 2, 2, 1, 1, 1, 2, 1, 1, 2, 1, 1, 2, 1, 2, 1, 2, 2, 1, 1, 1, 2, 1, 1, 2, 1, 1, 2, 1, 1, 1, 1, 2, 2, 1, 1, 1, 2, 2, 2, 1, 2, 2, 1, 2, 1, 2, 1, 2, 1, 1, 1, 1, 2, 1, 2, 1, 1, 1, 2, 1, 1, 1, 2, 1, 2, 2, 1, 1, 1, 1, 1, 1, 2, 1, 2, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 2, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 2, 2, 2, 1, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 2, 1, 1, 2, 1, 2, 1, 2, 1, 1, 1, 1, 2, 2, 1, 1, 1, 1, 2, 2, 1, 1, 2, 2, 1, 1, 1, 1, 1, 1, 1, 2, 2, 1, 2, 1, 2, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 2, 2, 1, 1, 1, 1, 1, 1, 2, 1, 2, 1, 2, 1, 1, 1, 1, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 2, 1, 2, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 2, 1, 1, 2, 1, 1, 2, 2, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 2, 1, 2, 2, 1, 1, 1, 1, 1, 1, 1, 2, 1, 2, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1), `(time)` = c(5.87142493529245, 9.92281987285241, 7.83866354078054, 3.88501064479351, 7.98219843907282, 5.97899966686964, 7.91527304146439, 3.54147408576682, 11.597629099153, 9.82756928401068, 9.97262317826971, 7.72961510065943, 5.47928349161521, 5.96312692062929, 7.8782734121196, 3.49339653924108, 3.55636260425672, 9.57818894414231, 5.80909329559654, 11.3628850295208, 9.25330969505012, 7.14434051932767, 5.43166656745598, 9.40492502506822, 5.43317895755172, 9.92863407591358, 3.56436423165724, 5.63726937677711, 5.14974439656362, 7.73447757447138, 9.71500307414681, 5.31775521812961, 5.71226332942024, 11.4398279767483, 5.24082006607205, 5.77291750116274, 2.85219620168209, 2.99000328965485, 5.07892405567691, 7.64558661123738, 11.9878277573735, 9.44745189324021, 11.974203527905, 3.65706871962175, 5.56909245997667, 11.2421525875106, 3.28821445722133, 6.94386146171018, 3.20308243529871, 3.44738150248304, 3.01407120516524, 2.82624841900542, 9.31964205158874, 9.93841761071235, 6.81152716465294, 5.42508024489507, 5.65543553931639, 5.74161110585555, 4.93850188469514, 2.76500924676657, 6.79429586650804, 10.9051416628063, 9.45861924672499, 3.2468714187853, 11.8841498191468, 2.80089949350804, 2.81158100022003, 7.72908989805728, 5.03359367279336, 3.91029347665608, 11.4508105451241, 4.72459656652063, 9.88540952466428, 11.3805134459399, 11.1143190776929, 5.51328981481493, 3.87682749098167, 6.63175682444125, 11.0839585596696, 2.44926101760939, 7.32668545050547, 7.72406808240339, 3.1967908362858, 4.64298424078152, 10.8366779834032, 9.08426084881648, 3.26975159766153, 7.99215474305674, 4.90368302632123, 3.48215729743242, 11.8126305225305, 3.50941124325618, 2.65748743340373, 3.89593270933256, 8.58589744567871, 3.85635509667918, 9.68270343355834, 11.0116860480048, 2.34048716025427, 2.99012821121141, 2.26822267565876, 6.33915353333578, 4.59730508411303, 9.29213455365971, 10.3300552773289, 4.52494822023436, 3.69910420756787, 9.19448097096756, 6.53790118405595, 2.84043178660795, 2.92310368129984, 7.57508101547137, 4.39429342700168, 7.87635383335873, 7.31565613858402, 2.67995840683579, 8.51434004260227, 10.8850202164613, 2.237805116456, 10.9866374572739, 3.60337541112676, 2.83994663972408, 9.22928994335234, 9.90875627379864, 4.2474471302703, 5.608781542629, 4.18809177773073, 2.63428934337571, 8.19561707507819, 3.37655047513545, 2.9279247620143, 2.91915082419291, 6.30966322263703, 8.59632305894047, 3.19142399262637, 3.45553400926292, 6.78271856205538, 10.4426118554547, 5.3161151073873, 3.68122910801321, 4.47773735551164, 3.94032435771078, 0, 9.91449977783486, 10.6235447488725, 11.2494283793494, 11.7956969840452, 2.90626289090142, 4.28228865144774, 3.75886659231037, 4.68897478748113, 2.87195280333981, 0, 0, 6.3844190700911, 2.42539903847501, 0, 8.86943528149277, 7.03105230117217, 0, 8.26445634430274, 3.12852768599987, 2.62497931299731, 0, 2.12989983754233, 0, 10.5872110980563, 0, 4.84711683681235, 0, 5.50300833629444, 0, 8.23544122418389, 2.25831370893866, 5.39002449251711, 0, 2.33862182917073, 0, 0, 0, 0, 4.84830705216154, 3.46543603530154, 0, 2.21761263301596, 7.05061100749299, 0, 0, 0, 11.0294653056189, 6.33090786635876, 3.17613278608769, 0, 10.3988843658008, 0, 0, 9.01387899415568, 6.24685782054439, 6.65402745362371, 2.27725649345666, 4.30557628255337, 8.64433611324057, 6.27297517238185, 3.27581744641066, 4.30970203410834, 4.89256869722158, 6.34360016463324, 7.51739361416548, 2.53858755948022, 2.28633317304775, 2.5879835980013, 2.2656556260772, 0, 0, 8.95237603690475, 0, 7.52430305909365, 8.21820192690939, 0, 7.11862054141238, 4.71328281331807, 10.3286531488411, 3.33610120788217, 0, 2.26288464106619, 7.43587055196986, 9.37021501036361, 8.31679191347212, 0, 4.16622779564932, 4.12761696102098, 0, 6.68046552268788, 10.7686077840626, 10.675230666995, 0, 10.362463645637, 8.64973028097302, 5.21060689305887, 2.43729056976736, 9.29915892099962, 6.14492126693949, 0, 6.69660410424694, 0, 11.0147485095076, 6.32617095671594, 4.32421816373244, 8.35438907146454, 0, 5.31232463102788, 7.17585586477071, 2.42529069166631, 0, 0, 8.36233665468171, 0, 6.61658466141671, 7.0652024387382, 2.20636847149581, 2.14649373944849, 7.28020209027454, 8.73837892478332, 0, 6.3529040729627, 4.28757456131279, 3.00667897146195, 0, 2.61610489757732, 6.67097498336807, 0, 0, 9.15632383339107, 4.74071413185447, 7.06164872832596, 0, 11.0202504131012, 0, 0, 0, 0, 8.74359147436917, 8.15887938626111, 6.10429802676663, 0, 8.96433914639056, 4.97134421393275, 0, 6.37485274206847, 0, 0, 4.51273520803079, 0, 0, 6.51633185334504, 0, 10.5533529794775, 4.53575625782833, 0, 0, 8.94446840509772, 4.05557424481958, 0, 10.6665559690446, 0, 4.55593205895275, 0, 2.29569141380489, 4.66704161139205, 0, 0, 0, 2.34688466973603, 0, 4.81137922545895, 0, 0, 0, 0, 0, 4.1083808587864, 6.70776848122478, 4.75043306173757, 0, 10.1298010731116, 6.60338654089719, 0, 8.35270142741501, 0, 0, 6.38661972479895, 10.2002816931345, 0, 0, 4.56150042731315, 0, 2.12760493345559, 0, 0, 0, 2.18878053547814, 0, 0, 0, 0, 0, 4.20997528079897, 6.37013992900029, 0, 0, 0, 10.1147453566082, 0, 10.3354305159301, 2.16447846172377, 0, 2.21329663926736, 0, 4.0615131421946, 0, 0, 0, 8.17062201164663, 0, 2.26283306768164, 0, 6.23068464966491, 6.14006535848603, 8.02207214199007, 4.02351946942508, 4.04548244830221, 0, 0, 0, 0, 0, 0, 0, 0), `(obstype)` = c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1), `(obstrue)` = c(1, 2, 2, 1, 2, 2, 2, 1, 2, 1, 1, 2, 1, 2, 2, 2, 2, 2, 1, 2, 2, 1, 2, 2, 1, 1, 1, 1, 2, 2, 1, 1, 2, 2, 2, 2, 1, 1, 2, 1, 2, 1, 2, 2, 1, 2, 2, 2, 1, 1, 1, 1, 1, 1, 2, 2, 2, 1, 1, 1, 2, 1, 1, 1, 2, 1, 2, 2, 1, 2, 2, 2, 2, 2, 2, 1, 1, 2, 1, 1, 1, 2, 2, 1, 2, 2, 1, 1, 2, 1, 2, 2, 2, 1, 2, 1, 2, 2, 1, 1, 1, 2, 1, 1, 2, 1, 1, 2, 1, 2, 1, 2, 2, 1, 1, 1, 2, 1, 1, 2, 1, 1, 2, 1, 1, 1, 1, 2, 2, 1, 1, 1, 2, 2, 2, 1, 2, 2, 1, 2, 1, 2, 1, 2, 1, 1, 1, 1, 2, 1, 2, 1, 1, 1, 2, 1, 1, 1, 2, 1, 2, 2, 1, 1, 1, 1, 1, 1, 2, 1, 2, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 2, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 2, 2, 2, 1, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 2, 1, 1, 2, 1, 2, 1, 2, 1, 1, 1, 1, 2, 2, 1, 1, 1, 1, 2, 2, 1, 1, 2, 2, 1, 1, 1, 1, 1, 1, 1, 2, 2, 1, 2, 1, 2, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 2, 2, 1, 1, 1, 1, 1, 1, 2, 1, 2, 1, 2, 1, 1, 1, 1, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 2, 1, 2, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 2, 1, 1, 2, 1, 1, 2, 2, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 2, 1, 2, 2, 1, 1, 1, 1, 1, 1, 1, 2, 1, 2, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1), `(obs)` = c(251L, 69L, 460L, 264L, 142L, 175L, 363L, 119L, 432L, 438L, 168L, 206L, 256L, 131L, 302L, 24L, 451L, 468L, 415L, 134L, 390L, 349L, 283L, 103L, 322L, 267L, 165L, 74L, 12L, 430L, 404L, 225L, 45L, 150L, 108L, 193L, 458L, 153L, 395L, 239L, 335L, 40L, 275L, 407L, 189L, 398L, 361L, 194L, 17L, 231L, 217L, 37L, 220L, 156L, 246L, 290L, 369L, 348L, 182L, 354L, 291L, 21L, 20L, 435L, 423L, 146L, 387L, 26L, 61L, 289L, 185L, 445L, 477L, 286L, 28L, 317L, 282L, 409L, 325L, 419L, 312L, 421L, 465L, 124L, 365L, 177L, 401L, 162L, 408L, 49L, 351L, 107L, 342L, 73L, 303L, 331L, 149L, 455L, 271L, 379L, 300L, 109L, 113L, 324L, 104L, 80L, 316L, 454L, 62L, 204L, 140L, 102L, 343L, 403L, 273L, 79L, 115L, 268L, 137L, 208L, 255L, 444L, 27L, 227L, 265L, 332L, 355L, 100L, 410L, 310L, 201L, 307L, 125L, 447L, 11L, 321L, 46L, 127L, 311L, 44L, 18L, 174L, 337L, 334L, 221L, 157L, 228L, 31L, 147L, 250L, 245L, 295L, 187L, 277L, 114L, 34L, 473L, 240L, 453L, 191L, 126L, 211L, 160L, 367L, 278L, 2L, 169L, 383L, 475L, 96L, 429L, 136L, 292L, 66L, 238L, 65L, 112L, 129L, 87L, 357L, 299L, 466L, 347L, 122L, 97L, 370L, 92L, 243L, 223L, 241L, 81L, 428L, 111L, 439L, 270L, 7L, 133L, 446L, 132L, 123L, 388L, 82L, 396L, 328L, 218L, 4L, 183L, 333L, 181L, 244L, 261L, 358L, 393L, 33L, 285L, 59L, 167L, 195L, 418L, 467L, 436L, 304L, 414L, 376L, 88L, 68L, 422L, 364L, 180L, 154L, 38L, 198L, 389L, 178L, 41L, 260L, 116L, 397L, 50L, 60L, 76L, 252L, 236L, 219L, 159L, 462L, 284L, 279L, 207L, 99L, 212L, 437L, 394L, 341L, 339L, 63L, 78L, 75L, 318L, 3L, 474L, 5L, 184L, 234L, 176L, 452L, 53L, 353L, 237L, 39L, 145L, 386L, 274L, 232L, 476L, 36L, 83L, 374L, 443L, 203L, 457L, 431L, 461L, 257L, 294L, 258L, 402L, 30L, 323L, 306L, 139L, 67L, 200L, 479L, 19L, 471L, 448L, 166L, 152L, 378L, 313L, 120L, 52L, 196L, 216L, 101L, 85L, 224L, 205L, 55L, 90L, 210L, 8L, 427L, 161L, 57L, 10L, 14L, 464L, 16L, 272L, 155L, 141L, 434L, 411L, 416L, 400L, 143L, 327L, 360L, 148L, 70L, 413L, 441L, 362L, 309L, 188L, 230L, 320L, 346L, 368L, 372L, 48L, 23L, 381L, 106L, 25L, 226L, 118L, 450L, 164L, 391L, 254L, 469L, 192L, 171L, 384L, 406L, 420L, 214L, 43L, 425L, 247L, 315L, 130L, 249L, 344L, 266L, 350L, 459L, 301L, 330L, 281L, 263L, 94L, 297L, 72L, 288L, 173L), `(tostate)` = c(1, 2, 2, 1, 2, 2, 2, 1, 2, 1, 1, 2, 1, 2, 2, 2, 2, 2, 1, 3, 2, 1, 2, 2, 1, 1, 1, 1, 3, 2, 3, 1, 2, 2, 2, 2, 1, 1, 2, 1, 3, 1, 3, 2, 3, 3, 2, 2, 1, 1, 1, 1, 1, 1, 2, 2, 2, 1, 1, 1, 2, 1, 1, 1, 2, 2, 2, 2, 1, 2, 2, 2, 3, 2, 2, 1, 2, 2, 1, 1, 1, 2, 2, 2, 3, 2, 1, 3, 2, 1, 2, 2, 2, 1, 2, 1, 2, 2, 1, 3, 1, 3, 2, 1, 2, 1, 1, 2, 1, 2, 2, 2, 2, 1, 2, 1, 2, 1, 3, 3, 1, 2, 2, 1, 1, 1, 3, 2, 2, 1, 3, 3, 2, 2, 2, 1, 3, 2, 1, 2, 1, 2, 3, 2, 1, 2, 3, 3, 2, 1, 2, 3, 1, 1, 2, 3, 1, 1, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 2, 1, 2, 1, 3, 1, 1, 1, 1, 2, 1, 1, 1, 2, 1, 1, 3, 3, 3, 1, 1, 1, 2, 2, 1, 3, 1, 1, 2, 2, 2, 1, 2, 2, 2, 3, 1, 1, 2, 2, 1, 2, 3, 3, 2, 1, 2, 1, 1, 2, 1, 2, 1, 3, 1, 3, 3, 2, 2, 2, 1, 1, 1, 3, 2, 2, 1, 2, 2, 2, 3, 1, 3, 3, 1, 1, 1, 2, 2, 3, 2, 2, 3, 1, 2, 2, 3, 3, 1, 1, 3, 1, 2, 3, 2, 3, 2, 2, 3, 1, 1, 1, 1, 2, 2, 3, 2, 1, 3, 3, 1, 2, 1, 2, 2, 1, 1, 3, 1, 1, 1, 1, 1, 1, 1, 3, 1, 3, 2, 1, 1, 1, 3, 3, 1, 2, 1, 2, 3, 1, 2, 3, 3, 2, 3, 2, 1, 3, 2, 3, 2, 1, 1, 1, 2, 1, 2, 3, 1, 3, 1, 2, 2, 2, 1, 3, 2, 1, 1, 1, 1, 1, 2, 3, 1, 2, 3, 2, 2, 1, 1, 2, 1, 3, 1, 2, 2, 3, 3, 2, 2, 3, 2, 3, 3, 1, 2, 1, 3, 1, 2, 2, 2, 1, 1, 1, 3, 3, 1, 2, 2), `(timelag)` = c(0.273496331647038, 0.277461820282042, 0.320215845480561, 0.362436485476792, 0.370502988342196, 0.373904406093061, 0.401518872007728, 0.514100159052759, 0.518999685999006, 0.57131508179009, 0.614587919786572, 0.624773970805109, 0.625014535151422, 0.690900532994419, 0.707624033559114, 0.716578741557896, 0.731211957056075, 0.757241571787745, 0.794293245300651, 0.835453176870942, 0.861435661558062, 0.877731622662395, 0.894504389259964, 0.925130252260715, 0.941673784516752, 0.956386140547693, 0.971392026171088, 0.979315284639597, 1.00438436400145, 1.00911389989778, 1.02676400123164, 1.05238471087068, 1.07045523263514, 1.09459618944675, 1.09833346726373, 1.17094396054745, 1.17132326774299, 1.17622450599447, 1.19405111670494, 1.22384867025539, 1.22505423193797, 1.22777877375484, 1.23338127508759, 1.24661430669948, 1.26755081163719, 1.2698119096458, 1.27328597009182, 1.2743404651992, 1.27465492021292, 1.29333262937143, 1.2956308289431, 1.30136854201555, 1.30390269728377, 1.31101076863706, 1.35909484699368, 1.36921562161297, 1.3951754681766, 1.40272941347212, 1.4050982799381, 1.42308253096417, 1.44114535767585, 1.44511187402532, 1.44652241608128, 1.46641139453277, 1.46755770174786, 1.4813891579397, 1.49399528233334, 1.50020004529506, 1.5043075112626, 1.51478676823899, 1.52148766350001, 1.52226125402376, 1.53903959412128, 1.54127691313624, 1.54323556041345, 1.55191262392327, 1.55483907647431, 1.56386025063694, 1.58668295992538, 1.61225212458521, 1.61778295459226, 1.64614692796022, 1.65151621587574, 1.66667898185551, 1.66734161134809, 1.68434693524614, 1.70159261627123, 1.71800996968523, 1.72807379812002, 1.72844959562644, 1.72867674054578, 1.73140882281587, 1.73680599359795, 1.74133666744456, 1.7427557031624, 1.75242644594982, 1.75712454319, 1.76051321811974, 1.76789369853213, 1.76961697172374, 1.77725977264345, 1.78527457127348, 1.78711398597807, 1.79182400600985, 1.79885980021209, 1.80595964612439, 1.81418560724705, 1.81720507703722, 1.82443547062576, 1.8266098247841, 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3.35377261415124, 3.37655047513545, 3.44148752652109, 3.44738150248304, 3.45553400926292, 3.46543603530154, 3.46665500383824, 3.47461093589664, 3.48215729743242, 3.49339653924108, 3.49964332999662, 3.50941124325618, 3.51911461725831, 3.53861634479836, 3.54147408576682, 3.55636260425672, 3.56436423165724, 3.58858992299065, 3.60337541112676, 3.60547294421121, 3.60843903943896, 3.62426304770634, 3.65622359374538, 3.65706871962175, 3.66255494020879, 3.66334343003109, 3.68122910801321, 3.68624029681087, 3.69452184485272, 3.69910420756787, 3.70029385294765, 3.75886659231037, 3.76124630775303, 3.78856871742755, 3.79055838054046, 3.81514407135546, 3.83279096381739, 3.85635509667918, 3.87682749098167, 3.88501064479351, 3.88591411849484, 3.89363563805819, 3.89593270933256, 3.91029347665608, 3.94032435771078 ), `(nocc)` = c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1), `(whicha)` = 1:379, `(noccsum)` = c(NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, NA_real_ )), mm.cov = c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1), mm.cov.agg = c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1), mm.mcov = NULL, mm.hcov = NULL, mm.icov = NULL, subject.weights = NULL), qmodel = list(nstates = 3L, iso = 4, perm = c(1, 2, 3), qperm = 1:3, npars = 3, imatrix = c(0, 0, 0, 1, 0, 0, 1, 1, 0), qmatrix = c(-0.2, 0, 0, 0.1, -0.1, 0, 0.1, 0.1, 0), inits = c(0.1, 0.1, 0.1), constr = 1:3, ndpars = 3L, expm = 1), qcmodel = list(npars = 0, ncovs = 0, ndpars = 0), cmodel = list(ncens = 0, censor = NULL, states = NULL, states_list = NULL, index = NULL), hmodel = list(hidden = FALSE, models = c(0, 0, 0), nipars = 0, nicoveffs = 0, totpars = 0, ncoveffs = 0, ematrix = FALSE)) 7: do.call(optfn, args) 8: msm.optim(opt.method, p, hessian, use.deriv, msmdata, qmodel, qcmodel, cmodel, hmodel, ...) 9: msm(state ~ time, data = dat_ID, subject = id, qmatrix = qmatrix) 10: eval(expr, envir) 11: eval(expr, envir) 12: withVisible(eval(expr, envir)) 13: withCallingHandlers(code, message = function (cnd) { watcher$capture_plot_and_output() if (on_message$capture) { watcher$push(cnd) } if (on_message$silence) { invokeRestart("muffleMessage") }}, warning = function (cnd) { if (getOption("warn") >= 2 || getOption("warn") < 0) { return() } watcher$capture_plot_and_output() if (on_warning$capture) { cnd <- sanitize_call(cnd) watcher$push(cnd) } if (on_warning$silence) { invokeRestart("muffleWarning") }}, error = function (cnd) { watcher$capture_plot_and_output() cnd <- sanitize_call(cnd) watcher$push(cnd) switch(on_error, continue = invokeRestart("eval_continue"), stop = invokeRestart("eval_stop"), error = invokeRestart("eval_error", cnd))}) 14: eval(call) 15: eval(call) 16: with_handlers({ for (expr in tle$exprs) { ev <- withVisible(eval(expr, envir)) watcher$capture_plot_and_output() watcher$print_value(ev$value, ev$visible, envir) } TRUE}, handlers) 17: doWithOneRestart(return(expr), restart) 18: withOneRestart(expr, restarts[[1L]]) 19: withRestartList(expr, restarts[-nr]) 20: doWithOneRestart(return(expr), restart) 21: withOneRestart(withRestartList(expr, restarts[-nr]), restarts[[nr]]) 22: withRestartList(expr, restarts[-nr]) 23: doWithOneRestart(return(expr), restart) 24: withOneRestart(withRestartList(expr, restarts[-nr]), restarts[[nr]]) 25: withRestartList(expr, restarts) 26: withRestarts(with_handlers({ for (expr in tle$exprs) { ev <- withVisible(eval(expr, envir)) watcher$capture_plot_and_output() watcher$print_value(ev$value, ev$visible, envir) } TRUE}, handlers), eval_continue = function() TRUE, eval_stop = function() FALSE, eval_error = function(cnd) { signalCondition(cnd) stop(cnd) }) 27: evaluate::evaluate(...) 28: evaluate(code, envir = env, new_device = FALSE, keep_warning = if (is.numeric(options$warning)) TRUE else options$warning, keep_message = if (is.numeric(options$message)) TRUE else options$message, stop_on_error = if (is.numeric(options$error)) options$error else { if (options$error && options$include) 0L else 2L }, output_handler = knit_handlers(options$render, options)) 29: in_dir(input_dir(), expr) 30: in_input_dir(evaluate(code, envir = env, new_device = FALSE, keep_warning = if (is.numeric(options$warning)) TRUE else options$warning, keep_message = if (is.numeric(options$message)) TRUE else options$message, stop_on_error = if (is.numeric(options$error)) options$error else { if (options$error && options$include) 0L else 2L }, output_handler = knit_handlers(options$render, options))) 31: eng_r(options) 32: block_exec(params) 33: call_block(x) 34: process_group(group) 35: withCallingHandlers(if (tangle) process_tangle(group) else process_group(group), error = function(e) if (xfun::pkg_available("rlang", "1.0.0")) rlang::entrace(e)) 36: xfun:::handle_error(withCallingHandlers(if (tangle) process_tangle(group) else process_group(group), error = function(e) if (xfun::pkg_available("rlang", "1.0.0")) rlang::entrace(e)), function(loc) { setwd(wd) write_utf8(res, output %n% stdout()) paste0("\nQuitting from lines ", loc) }, if (labels[i] != "") sprintf(" [%s]", labels[i]), get_loc) 37: process_file(text, output) 38: knitr::knit(knit_input, knit_output, envir = envir, quiet = quiet) 39: rmarkdown::render(file, encoding = encoding, quiet = quiet, envir = globalenv(), output_dir = getwd(), ...) 40: vweave_rmarkdown(...) 41: engine$weave(file, quiet = quiet, encoding = enc) 42: doTryCatch(return(expr), name, parentenv, handler) 43: tryCatchOne(expr, names, parentenv, handlers[[1L]]) 44: tryCatchList(expr, classes, parentenv, handlers) 45: tryCatch({ engine$weave(file, quiet = quiet, encoding = enc) setwd(startdir) output <- find_vignette_product(name, by = "weave", engine = engine) if (!have.makefile && vignette_is_tex(output)) { texi2pdf(file = output, clean = FALSE, quiet = quiet) output <- find_vignette_product(name, by = "texi2pdf", engine = engine) }}, error = function(e) { OK <<- FALSE message(gettextf("Error: processing vignette '%s' failed with diagnostics:\n%s", file, conditionMessage(e)))}) 46: tools:::.buildOneVignette("Overview.Rmd", "/data/gannet/ripley/R/packages/tests-devel/icmstate.Rcheck/vign_test/icmstate", TRUE, FALSE, "Overview", "UTF-8", "/tmp/RtmpkSJwuX/working_dir/RtmpX9fFOB/file2a711142801ccb.rds") An irrecoverable exception occurred. R is aborting now ... --- re-building ‘comparison-with-known-results.Rmd’ using rmarkdown --- finished re-building ‘comparison-with-known-results.Rmd’ --- re-building ‘sim_msm.Rmd’ using rmarkdown --- finished re-building ‘sim_msm.Rmd’ SUMMARY: processing the following file failed: ‘Overview.Rmd’ Error: Vignette re-building failed. Execution halted Flavor: r-devel-linux-x86_64-fedora-gcc

Version: 0.1.0
Check: re-building of vignette outputs
Result: ERROR Error(s) in re-building vignettes: ... --- re-building ‘Overview.Rmd’ using rmarkdown --- finished re-building ‘Overview.Rmd’ --- re-building ‘comparison-with-known-results.Rmd’ using rmarkdown --- finished re-building ‘comparison-with-known-results.Rmd’ --- re-building ‘sim_msm.Rmd’ using rmarkdown Quitting from lines 173-177 [visstartprobs] (sim_msm.Rmd) Error: processing vignette 'sim_msm.Rmd' failed with diagnostics: 1 assertions failed: * Variable 'sum(startprobs)': FALSE. --- failed re-building ‘sim_msm.Rmd’ SUMMARY: processing the following file failed: ‘sim_msm.Rmd’ Error: Vignette re-building failed. Execution halted Flavors: r-release-macos-arm64, r-oldrel-macos-arm64