Last updated on 2024-09-14 09:48:30 CEST.
| Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
|---|---|---|---|---|---|---|
| r-devel-linux-x86_64-debian-clang | 1.0-1 | 14.12 | 50.10 | 64.22 | OK | |
| r-devel-linux-x86_64-debian-gcc | 1.0-1 | 9.54 | 36.85 | 46.39 | OK | |
| r-devel-linux-x86_64-fedora-clang | 1.0-1 | 114.34 | OK | |||
| r-devel-linux-x86_64-fedora-gcc | 1.0-1 | 123.87 | OK | |||
| r-devel-windows-x86_64 | 1.0-1 | 21.00 | 95.00 | 116.00 | OK | |
| r-patched-linux-x86_64 | 1.0-1 | 14.99 | 47.52 | 62.51 | OK | |
| r-release-linux-x86_64 | 1.0-1 | 13.58 | 47.48 | 61.06 | OK | |
| r-release-macos-arm64 | 1.0-1 | 43.00 | ERROR | |||
| r-release-macos-x86_64 | 1.0-1 | 86.00 | ERROR | |||
| r-release-windows-x86_64 | 1.0-1 | 24.00 | 97.00 | 121.00 | OK | |
| r-oldrel-macos-arm64 | 1.0-1 | 39.00 | ERROR | |||
| r-oldrel-macos-x86_64 | 1.0-1 | 78.00 | ERROR | |||
| r-oldrel-windows-x86_64 | 1.0-1 | 20.00 | 88.00 | 108.00 | OK |
clang-ASAN gcc-ASAN gcc-UBSAN M1mac
Version: 1.0-1
Check: examples
Result: ERROR
Running examples in ‘ream-Ex.R’ failed
The error most likely occurred in:
> ### Name: CDSTP
> ### Title: Continuous Dual-Stage Two-Phase Model of Selective Attention
> ### Aliases: CDSTP dCDSTP pCDSTP rCDSTP
>
> ### ** Examples
>
> # Probability density function
> dCDSTP(rt = c(1.2, 0.6, 0.4), resp = c("upper", "lower", "lower"),
+ phi = c(0.3, 0.5, 0.5, -0.5, -1.0, -0.5, 8.0, 4.0, 1.0, 2.0, 1.3, 1.3, 0.0, 0.0, 1.0))
Flavors: r-release-macos-arm64, r-oldrel-macos-arm64
Version: 1.0-1
Check: re-building of vignette outputs
Result: ERROR
Error(s) in re-building vignettes:
--- re-building ‘guidline.Rmd’ using rmarkdown
DMC package:ream R Documentation
_<08>D_<08>i_<08>f_<08>f_<08>u_<08>s_<08>i_<08>o_<08>n _<08>M_<08>o_<08>d_<08>e_<08>l _<08>f_<08>o_<08>r _<08>C_<08>o_<08>n_<08>f_<08>l_<08>i_<08>c_<08>t _<08>T_<08>a_<08>s_<08>k_<08>s
_<08>D_<08>e_<08>s_<08>c_<08>r_<08>i_<08>p_<08>t_<08>i_<08>o_<08>n:
The DMC is a two-process evidence accumulation model for the study
of conflict tasks. It sums together a controlled and an automatic
process to generate a single accumulator for generating the
likelihood function. This accumulator has the same parameters as
the SDDM with the exception of the drift rate, given by
v(x,t) = s*A*exp(-t/tau)*[e*t/(tau*(alpha-1))]^{alpha-1}*[(alpha-1)/t - 1/tau] + mu_c.
_<08>U_<08>s_<08>a_<08>g_<08>e:
dDMC(rt, resp, phi, x_res = "default", t_res = "default")
pDMC(rt, resp, phi, x_res = "default", t_res = "default")
rDMC(n, phi, dt = 1e-05)
_<08>A_<08>r_<08>g_<08>u_<08>m_<08>e_<08>n_<08>t_<08>s:
rt: vector of response times
resp: vector of responses ("upper" and "lower")
phi: parameter vector in the following order:
1. Non-decision time (t_{nd}). Time for non-decision
processes such as stimulus encoding and response
execution. Total decision time t is the sum of the
decision and non-decision times.
2. Relative start (w). Sets the start point of accumulation
as a ratio of the two decision thresholds. Related to the
absolute start z point via equation z = b_l + w*(b_u -
b_l).
3. Coherence parameter (s). Sets stimulus coherence. If s =
1, coherent condition; if s = 0, neutral condition; if s
= -1, incoherent condition.
4. Automatic process amplitude (A). Max value of automatic
process.
5. Scale parameter (tau). Contributes to time automatic
process. Time to max t_{max} = (alpha – 1)*tau.
6. Shape parameter (alpha). Indicates the shape of the
automatic process. Must have value more than 1 (alpha >
1).
7. Drift rate of the controlled process (mu_c).
8. Noise scale (sigma). Model noise scale parameter.
9. Decision thresholds (b). Sets the location of each
decision threshold. The upper threshold b_u is above 0
and the lower threshold b_l is below 0 such that b_u =
-b_l = b. The threshold separation a = 2b.
10. Contamination (g). Sets the strength of the contamination
process. Contamination process is a uniform distribution
f_c(t) where f_c(t) = 1/(g_u-g_l) if g_l <= t <= g_u and
f_c(t) = 0 if t < g_l or t > g_u. It is combined with PDF
f_i(t) to give the final combined distribution f_{i,c}(t)
= g*f_c(t) + (1-g)*f_i(t), which is then output by the
program. If g = 0, it just outputs f_i(t).
11. Lower bound of contamination distribution (g_l). See
parameter g.
12. Upper bound of contamination distribution (g_u). See
parameter g.
x_res: spatial/evidence resolution
t_res: time resolution
n: number of samples
dt: step size of time. We recommend 0.00001 (1e-5)
_<08>V_<08>a_<08>l_<08>u_<08>e:
For the density a list of PDF values, log-PDF values, and the sum
of the log-PDFs, for the distribution function a list of of CDF
values, log-CDF values, and the sum of the log-CDFs, and for the
random sampler a list of response times (rt) and response
thresholds (resp).
_<08>A_<08>u_<08>t_<08>h_<08>o_<08>r(_<08>s):
Raphael Hartmann & Matthew Murrow
_<08>R_<08>e_<08>f_<08>e_<08>r_<08>e_<08>n_<08>c_<08>e_<08>s:
Ulrich, R., Schröter, H., Leuthold, H., & Birngruber, T. (2015).
Automatic and controlled stimulus processing in conflict tasks:
Superimposed diffusion processes and delta functions. _Cognitive
psychology, 78_, 148-174.
_<08>E_<08>x_<08>a_<08>m_<08>p_<08>l_<08>e_<08>s:
# Probability density function
dDMC(rt = c(1.2, 0.6, 0.4), resp = c("upper", "lower", "lower"),
phi = c(0.3, 0.5, -1.0, 0.2, 0.05, 2.5, 3.0, 1.0, 0.5, 0.0, 0.0, 1.0))
# Cumulative distribution function
pDMC(rt = c(1.2, 0.6, 0.4), resp = c("upper", "lower", "lower"),
phi = c(0.3, 0.5, -1.0, 0.2, 0.05, 2.5, 3.0, 1.0, 0.5, 0.0, 0.0, 1.0))
# Random sampling
rDMC(n = 100, phi = c(0.3, 0.5, -1.0, 0.2, 0.05, 2.5, 3.0, 1.0, 0.5, 0.0, 0.0, 1.0))
Flavor: r-release-macos-arm64
Version: 1.0-1
Check: examples
Result: ERROR
Running examples in ‘ream-Ex.R’ failed
The error most likely occurred in:
> ### Name: CDSTP
> ### Title: Continuous Dual-Stage Two-Phase Model of Selective Attention
> ### Aliases: CDSTP dCDSTP pCDSTP rCDSTP
>
> ### ** Examples
>
> # Probability density function
> dCDSTP(rt = c(1.2, 0.6, 0.4), resp = c("upper", "lower", "lower"),
+ phi = c(0.3, 0.5, 0.5, -0.5, -1.0, -0.5, 8.0, 4.0, 1.0, 2.0, 1.3, 1.3, 0.0, 0.0, 1.0))
*** caught illegal operation ***
address 0x10e7c67c4, cause 'illegal opcode'
Traceback:
1: dCDSTP(rt = c(1.2, 0.6, 0.4), resp = c("upper", "lower", "lower"), phi = c(0.3, 0.5, 0.5, -0.5, -1, -0.5, 8, 4, 1, 2, 1.3, 1.3, 0, 0, 1))
An irrecoverable exception occurred. R is aborting now ...
Flavor: r-release-macos-x86_64
Version: 1.0-1
Check: re-building of vignette outputs
Result: ERROR
Error(s) in re-building vignettes:
Error in readRDS(file) : error reading from connection
--- re-building ‘guidline.Rmd’ using rmarkdown
DMC package:ream R Documentation
_<08>D_<08>i_<08>f_<08>f_<08>u_<08>s_<08>i_<08>o_<08>n _<08>M_<08>o_<08>d_<08>e_<08>l _<08>f_<08>o_<08>r _<08>C_<08>o_<08>n_<08>f_<08>l_<08>i_<08>c_<08>t _<08>T_<08>a_<08>s_<08>k_<08>s
_<08>D_<08>e_<08>s_<08>c_<08>r_<08>i_<08>p_<08>t_<08>i_<08>o_<08>n:
The DMC is a two-process evidence accumulation model for the study
of conflict tasks. It sums together a controlled and an automatic
process to generate a single accumulator for generating the
likelihood function. This accumulator has the same parameters as
the SDDM with the exception of the drift rate, given by
v(x,t) = s*A*exp(-t/tau)*[e*t/(tau*(alpha-1))]^{alpha-1}*[(alpha-1)/t - 1/tau] + mu_c.
_<08>U_<08>s_<08>a_<08>g_<08>e:
dDMC(rt, resp, phi, x_res = "default", t_res = "default")
pDMC(rt, resp, phi, x_res = "default", t_res = "default")
rDMC(n, phi, dt = 1e-05)
_<08>A_<08>r_<08>g_<08>u_<08>m_<08>e_<08>n_<08>t_<08>s:
rt: vector of response times
resp: vector of responses ("upper" and "lower")
phi: parameter vector in the following order:
1. Non-decision time (t_{nd}). Time for non-decision
processes such as stimulus encoding and response
execution. Total decision time t is the sum of the
decision and non-decision times.
2. Relative start (w). Sets the start point of accumulation
as a ratio of the two decision thresholds. Related to the
absolute start z point via equation z = b_l + w*(b_u -
b_l).
3. Coherence parameter (s). Sets stimulus coherence. If s =
1, coherent condition; if s = 0, neutral condition; if s
= -1, incoherent condition.
4. Automatic process amplitude (A). Max value of automatic
process.
5. Scale parameter (tau). Contributes to time automatic
process. Time to max t_{max} = (alpha – 1)*tau.
6. Shape parameter (alpha). Indicates the shape of the
automatic process. Must have value more than 1 (alpha >
1).
7. Drift rate of the controlled process (mu_c).
8. Noise scale (sigma). Model noise scale parameter.
9. Decision thresholds (b). Sets the location of each
decision threshold. The upper threshold b_u is above 0
and the lower threshold b_l is below 0 such that b_u =
-b_l = b. The threshold separation a = 2b.
10. Contamination (g). Sets the strength of the contamination
process. Contamination process is a uniform distribution
f_c(t) where f_c(t) = 1/(g_u-g_l) if g_l <= t <= g_u and
f_c(t) = 0 if t < g_l or t > g_u. It is combined with PDF
f_i(t) to give the final combined distribution f_{i,c}(t)
= g*f_c(t) + (1-g)*f_i(t), which is then output by the
program. If g = 0, it just outputs f_i(t).
11. Lower bound of contamination distribution (g_l). See
parameter g.
12. Upper bound of contamination distribution (g_u). See
parameter g.
x_res: spatial/evidence resolution
t_res: time resolution
n: number of samples
dt: step size of time. We recommend 0.00001 (1e-5)
_<08>V_<08>a_<08>l_<08>u_<08>e:
For the density a list of PDF values, log-PDF values, and the sum
of the log-PDFs, for the distribution function a list of of CDF
values, log-CDF values, and the sum of the log-CDFs, and for the
random sampler a list of response times (rt) and response
thresholds (resp).
_<08>A_<08>u_<08>t_<08>h_<08>o_<08>r(_<08>s):
Raphael Hartmann & Matthew Murrow
_<08>R_<08>e_<08>f_<08>e_<08>r_<08>e_<08>n_<08>c_<08>e_<08>s:
Ulrich, R., Schröter, H., Leuthold, H., & Birngruber, T. (2015).
Automatic and controlled stimulus processing in conflict tasks:
Superimposed diffusion processes and delta functions. _Cognitive
psychology, 78_, 148-174.
_<08>E_<08>x_<08>a_<08>m_<08>p_<08>l_<08>e_<08>s:
# Probability density function
dDMC(rt = c(1.2, 0.6, 0.4), resp = c("upper", "lower", "lower"),
phi = c(0.3, 0.5, -1.0, 0.2, 0.05, 2.5, 3.0, 1.0, 0.5, 0.0, 0.0, 1.0))
# Cumulative distribution function
pDMC(rt = c(1.2, 0.6, 0.4), resp = c("upper", "lower", "lower"),
phi = c(0.3, 0.5, -1.0, 0.2, 0.05, 2.5, 3.0, 1.0, 0.5, 0.0, 0.0, 1.0))
# Random sampling
rDMC(n = 100, phi = c(0.3, 0.5, -1.0, 0.2, 0.05, 2.5, 3.0, 1.0, 0.5, 0.0, 0.0, 1.0))
*** caught illegal operation ***
address 0x1104187c4, cause 'illegal opcode'
Traceback:
1: rDMC(n = 10, phi = c(0.3, 0.5, -1, 0.2, 0.05, 2.5, 3, 1, 0.5, 0, 0, 1), dt = 1e-04)
2: eval(expr, envir, enclos)
3: eval(expr, envir, enclos)
4: eval_with_user_handlers(expr, envir, enclos, user_handlers)
5: withVisible(eval_with_user_handlers(expr, envir, enclos, user_handlers))
6: withCallingHandlers(withVisible(eval_with_user_handlers(expr, envir, enclos, user_handlers)), warning = wHandler, error = eHandler, message = mHandler)
7: handle(ev <- withCallingHandlers(withVisible(eval_with_user_handlers(expr, envir, enclos, user_handlers)), warning = wHandler, error = eHandler, message = mHandler))
8: timing_fn(handle(ev <- withCallingHandlers(withVisible(eval_with_user_handlers(expr, envir, enclos, user_handlers)), warning = wHandler, error = eHandler, message = mHandler)))
9: evaluate_call(expr, parsed$src[[i]], envir = envir, enclos = enclos, debug = debug, last = i == length(out), use_try = stop_on_error != 2L, keep_warning = keep_warning, keep_message = keep_message, log_echo = log_echo, log_warning = log_warning, output_handler = output_handler, include_timing = include_timing)
10: evaluate::evaluate(...)
11: evaluate(code, envir = env, new_device = FALSE, keep_warning = if (is.numeric(options$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))
12: in_dir(input_dir(), expr)
13: 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$render, options)))
14: eng_r(options)
15: block_exec(params)
16: call_block(x)
17: process_group(group)
18: withCallingHandlers(if (tangle) process_tangle(group) else process_group(group), error = function(e) if (xfun::pkg_available("rlang", "1.0.0")) rlang::entrace(e))
19: xfun:::handle_error(withCallingHandlers(if (tangle) process_tangle(group) else process_group(group), error = function(e) if (xfun::pkg_available("rlang", "1.0.0")) rlang::entrace(e)), function(loc) { setwd(wd) write_utf8(res, output %n% stdout()) paste0("\nQuitting from lines ", loc) }, if (labels[i] != "") sprintf(" [%s]", labels[i]), get_loc)
20: process_file(text, output)
21: knitr::knit(knit_input, knit_output, envir = envir, quiet = quiet)
22: rmarkdown::render(file, encoding = encoding, quiet = quiet, envir = globalenv(), output_dir = getwd(), ...)
23: vweave_rmarkdown(...)
24: engine$weave(file, quiet = quiet, encoding = enc)
25: doTryCatch(return(expr), name, parentenv, handler)
26: tryCatchOne(expr, names, parentenv, handlers[[1L]])
27: tryCatchList(expr, classes, parentenv, handlers)
28: 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) } outputs <- c(outputs, output)}, error = function(e) { thisOK <<- FALSE fails <<- c(fails, file) message(gettextf("Error: processing vignette '%s' failed with diagnostics:\n%s", file, conditionMessage(e)))})
29: tools::buildVignettes(dir = "/Volumes/Builds/packages/big-sur-x86_64/results/4.4/ream.Rcheck/vign_test/ream", skip = TRUE, ser_elibs = "/var/folders/2b/t0kwbtmn3p7brv2mvx39c9cr0000gn/T//RtmpBxf2qF/file13649467d3d88.rds")
An irrecoverable exception occurred. R is aborting now ...
Flavor: r-release-macos-x86_64
Version: 1.0-1
Check: running R code from vignettes
Result: ERROR
Errors in running code in vignettes:
when running code in ‘guidline.Rmd’
...
# Random sampling
rDMC(n = 100, phi = c(0.3, 0.5, -1.0, 0.2, 0.05, 2.5, 3.0, 1.0, 0.5, 0.0, 0.0, 1.0))
> (samp <- rDMC(n = 10, phi = c(0.3, 0.5, -1, 0.2, 0.05,
+ 2.5, 3, 1, 0.5, 0, 0, 1), dt = 1e-04))
... incomplete output. Crash?
‘guidline.Rmd’ using ‘UTF-8’... failed to complete the test
Flavor: r-oldrel-macos-arm64
Version: 1.0-1
Check: re-building of vignette outputs
Result: NOTE
Error(s) in re-building vignettes:
--- re-building ‘guidline.Rmd’ using rmarkdown
DMC package:ream R Documentation
_<08>D_<08>i_<08>f_<08>f_<08>u_<08>s_<08>i_<08>o_<08>n _<08>M_<08>o_<08>d_<08>e_<08>l _<08>f_<08>o_<08>r _<08>C_<08>o_<08>n_<08>f_<08>l_<08>i_<08>c_<08>t _<08>T_<08>a_<08>s_<08>k_<08>s
_<08>D_<08>e_<08>s_<08>c_<08>r_<08>i_<08>p_<08>t_<08>i_<08>o_<08>n:
The DMC is a two-process evidence accumulation model for the study
of conflict tasks. It sums together a controlled and an automatic
process to generate a single accumulator for generating the
likelihood function. This accumulator has the same parameters as
the SDDM with the exception of the drift rate, given by
v(x,t) = s*A*exp(-t/tau)*[e*t/(tau*(alpha-1))]^{alpha-1}*[(alpha-1)/t - 1/tau] + mu_c.
_<08>U_<08>s_<08>a_<08>g_<08>e:
dDMC(rt, resp, phi, x_res = "default", t_res = "default")
pDMC(rt, resp, phi, x_res = "default", t_res = "default")
rDMC(n, phi, dt = 1e-05)
_<08>A_<08>r_<08>g_<08>u_<08>m_<08>e_<08>n_<08>t_<08>s:
rt: vector of response times
resp: vector of responses ("upper" and "lower")
phi: parameter vector in the following order:
1. Non-decision time (t_{nd}). Time for non-decision
processes such as stimulus encoding and response
execution. Total decision time t is the sum of the
decision and non-decision times.
2. Relative start (w). Sets the start point of accumulation
as a ratio of the two decision thresholds. Related to the
absolute start z point via equation z = b_l + w*(b_u -
b_l).
3. Coherence parameter (s). Sets stimulus coherence. If s =
1, coherent condition; if s = 0, neutral condition; if s
= -1, incoherent condition.
4. Automatic process amplitude (A). Max value of automatic
process.
5. Scale parameter (tau). Contributes to time automatic
process. Time to max t_{max} = (alpha – 1)*tau.
6. Shape parameter (alpha). Indicates the shape of the
automatic process. Must have value more than 1 (alpha >
1).
7. Drift rate of the controlled process (mu_c).
8. Noise scale (sigma). Model noise scale parameter.
9. Decision thresholds (b). Sets the location of each
decision threshold. The upper threshold b_u is above 0
and the lower threshold b_l is below 0 such that b_u =
-b_l = b. The threshold separation a = 2b.
10. Contamination (g). Sets the strength of the contamination
process. Contamination process is a uniform distribution
f_c(t) where f_c(t) = 1/(g_u-g_l) if g_l <= t <= g_u and
f_c(t) = 0 if t < g_l or t > g_u. It is combined with PDF
f_i(t) to give the final combined distribution f_{i,c}(t)
= g*f_c(t) + (1-g)*f_i(t), which is then output by the
program. If g = 0, it just outputs f_i(t).
11. Lower bound of contamination distribution (g_l). See
parameter g.
12. Upper bound of contamination distribution (g_u). See
parameter g.
x_res: spatial/evidence resolution
t_res: time resolution
n: number of samples
dt: step size of time. We recommend 0.00001 (1e-5)
_<08>V_<08>a_<08>l_<08>u_<08>e:
For the density a list of PDF values, log-PDF values, and the sum
of the log-PDFs, for the distribution function a list of of CDF
values, log-CDF values, and the sum of the log-CDFs, and for the
random sampler a list of response times (rt) and response
thresholds (resp).
_<08>A_<08>u_<08>t_<08>h_<08>o_<08>r(_<08>s):
Raphael Hartmann & Matthew Murrow
_<08>R_<08>e_<08>f_<08>e_<08>r_<08>e_<08>n_<08>c_<08>e_<08>s:
Ulrich, R., Schröter, H., Leuthold, H., & Birngruber, T. (2015).
Automatic and controlled stimulus processing in conflict tasks:
Superimposed diffusion processes and delta functions. _Cognitive
psychology, 78_, 148-174.
_<08>E_<08>x_<08>a_<08>m_<08>p_<08>l_<08>e_<08>s:
# Probability density function
dDMC(rt = c(1.2, 0.6, 0.4), resp = c("upper", "lower", "lower"),
phi = c(0.3, 0.5, -1.0, 0.2, 0.05, 2.5, 3.0, 1.0, 0.5, 0.0, 0.0, 1.0))
# Cumulative distribution function
pDMC(rt = c(1.2, 0.6, 0.4), resp = c("upper", "lower", "lower"),
phi = c(0.3, 0.5, -1.0, 0.2, 0.05, 2.5, 3.0, 1.0, 0.5, 0.0, 0.0, 1.0))
# Random sampling
rDMC(n = 100, phi = c(0.3, 0.5, -1.0, 0.2, 0.05, 2.5, 3.0, 1.0, 0.5, 0.0, 0.0, 1.0))
Flavor: r-oldrel-macos-arm64
Version: 1.0-1
Check: examples
Result: ERROR
Running examples in ‘ream-Ex.R’ failed
The error most likely occurred in:
> ### Name: CDSTP
> ### Title: Continuous Dual-Stage Two-Phase Model of Selective Attention
> ### Aliases: CDSTP dCDSTP pCDSTP rCDSTP
>
> ### ** Examples
>
> # Probability density function
> dCDSTP(rt = c(1.2, 0.6, 0.4), resp = c("upper", "lower", "lower"),
+ phi = c(0.3, 0.5, 0.5, -0.5, -1.0, -0.5, 8.0, 4.0, 1.0, 2.0, 1.3, 1.3, 0.0, 0.0, 1.0))
*** caught illegal operation ***
address 0x10c5687c4, cause 'illegal opcode'
Traceback:
1: dCDSTP(rt = c(1.2, 0.6, 0.4), resp = c("upper", "lower", "lower"), phi = c(0.3, 0.5, 0.5, -0.5, -1, -0.5, 8, 4, 1, 2, 1.3, 1.3, 0, 0, 1))
An irrecoverable exception occurred. R is aborting now ...
Flavor: r-oldrel-macos-x86_64
Version: 1.0-1
Check: running R code from vignettes
Result: ERROR
Errors in running code in vignettes:
when running code in ‘guidline.Rmd’
...
4: withVisible(eval(ei, envir))
5: source(output, echo = TRUE)
6: doTryCatch(return(expr), name, parentenv, handler)
7: tryCatchOne(expr, names, parentenv, handlers[[1L]])
8: tryCatchList(expr, classes, parentenv, handlers)
9: tryCatch({ source(output, echo = TRUE)}, error = function(e) { cat("\n When sourcing ", sQuote(output), ":\n", sep = "") stop(conditionMessage(e), call. = FALSE, domain = NA)})
10: tools:::.run_one_vignette("guidline.Rmd", "/Volumes/Builds/packages/big-sur-x86_64/results/4.3/ream.Rcheck/00_pkg_src/ream/vignettes", encoding = "UTF-8", pkgdir = "/Volumes/Builds/packages/big-sur-x86_64/results/4.3/ream.Rcheck/00_pkg_src/ream")
An irrecoverable exception occurred. R is aborting now ...
... incomplete output. Crash?
‘guidline.Rmd’ using ‘UTF-8’... failed to complete the test
Flavor: r-oldrel-macos-x86_64
Version: 1.0-1
Check: re-building of vignette outputs
Result: NOTE
Error(s) in re-building vignettes:
--- re-building ‘guidline.Rmd’ using rmarkdown
DMC package:ream R Documentation
_<08>D_<08>i_<08>f_<08>f_<08>u_<08>s_<08>i_<08>o_<08>n _<08>M_<08>o_<08>d_<08>e_<08>l _<08>f_<08>o_<08>r _<08>C_<08>o_<08>n_<08>f_<08>l_<08>i_<08>c_<08>t _<08>T_<08>a_<08>s_<08>k_<08>s
_<08>D_<08>e_<08>s_<08>c_<08>r_<08>i_<08>p_<08>t_<08>i_<08>o_<08>n:
The DMC is a two-process evidence accumulation model for the study
of conflict tasks. It sums together a controlled and an automatic
process to generate a single accumulator for generating the
likelihood function. This accumulator has the same parameters as
the SDDM with the exception of the drift rate, given by
v(x,t) = s*A*exp(-t/tau)*[e*t/(tau*(alpha-1))]^{alpha-1}*[(alpha-1)/t - 1/tau] + mu_c.
_<08>U_<08>s_<08>a_<08>g_<08>e:
dDMC(rt, resp, phi, x_res = "default", t_res = "default")
pDMC(rt, resp, phi, x_res = "default", t_res = "default")
rDMC(n, phi, dt = 1e-05)
_<08>A_<08>r_<08>g_<08>u_<08>m_<08>e_<08>n_<08>t_<08>s:
rt: vector of response times
resp: vector of responses ("upper" and "lower")
phi: parameter vector in the following order:
1. Non-decision time (t_{nd}). Time for non-decision
processes such as stimulus encoding and response
execution. Total decision time t is the sum of the
decision and non-decision times.
2. Relative start (w). Sets the start point of accumulation
as a ratio of the two decision thresholds. Related to the
absolute start z point via equation z = b_l + w*(b_u -
b_l).
3. Coherence parameter (s). Sets stimulus coherence. If s =
1, coherent condition; if s = 0, neutral condition; if s
= -1, incoherent condition.
4. Automatic process amplitude (A). Max value of automatic
process.
5. Scale parameter (tau). Contributes to time automatic
process. Time to max t_{max} = (alpha – 1)*tau.
6. Shape parameter (alpha). Indicates the shape of the
automatic process. Must have value more than 1 (alpha >
1).
7. Drift rate of the controlled process (mu_c).
8. Noise scale (sigma). Model noise scale parameter.
9. Decision thresholds (b). Sets the location of each
decision threshold. The upper threshold b_u is above 0
and the lower threshold b_l is below 0 such that b_u =
-b_l = b. The threshold separation a = 2b.
10. Contamination (g). Sets the strength of the contamination
process. Contamination process is a uniform distribution
f_c(t) where f_c(t) = 1/(g_u-g_l) if g_l <= t <= g_u and
f_c(t) = 0 if t < g_l or t > g_u. It is combined with PDF
f_i(t) to give the final combined distribution f_{i,c}(t)
= g*f_c(t) + (1-g)*f_i(t), which is then output by the
program. If g = 0, it just outputs f_i(t).
11. Lower bound of contamination distribution (g_l). See
parameter g.
12. Upper bound of contamination distribution (g_u). See
parameter g.
x_res: spatial/evidence resolution
t_res: time resolution
n: number of samples
dt: step size of time. We recommend 0.00001 (1e-5)
_<08>V_<08>a_<08>l_<08>u_<08>e:
For the density a list of PDF values, log-PDF values, and the sum
of the log-PDFs, for the distribution function a list of of CDF
values, log-CDF values, and the sum of the log-CDFs, and for the
random sampler a list of response times (rt) and response
thresholds (resp).
_<08>A_<08>u_<08>t_<08>h_<08>o_<08>r(_<08>s):
Raphael Hartmann & Matthew Murrow
_<08>R_<08>e_<08>f_<08>e_<08>r_<08>e_<08>n_<08>c_<08>e_<08>s:
Ulrich, R., Schröter, H., Leuthold, H., & Birngruber, T. (2015).
Automatic and controlled stimulus processing in conflict tasks:
Superimposed diffusion processes and delta functions. _Cognitive
psychology, 78_, 148-174.
_<08>E_<08>x_<08>a_<08>m_<08>p_<08>l_<08>e_<08>s:
# Probability density function
dDMC(rt = c(1.2, 0.6, 0.4), resp = c("upper", "lower", "lower"),
phi = c(0.3, 0.5, -1.0, 0.2, 0.05, 2.5, 3.0, 1.0, 0.5, 0.0, 0.0, 1.0))
# Cumulative distribution function
pDMC(rt = c(1.2, 0.6, 0.4), resp = c("upper", "lower", "lower"),
phi = c(0.3, 0.5, -1.0, 0.2, 0.05, 2.5, 3.0, 1.0, 0.5, 0.0, 0.0, 1.0))
# Random sampling
rDMC(n = 100, phi = c(0.3, 0.5, -1.0, 0.2, 0.05, 2.5, 3.0, 1.0, 0.5, 0.0, 0.0, 1.0))
*** caught illegal operation ***
address 0x1077f07c4, cause 'illegal opcode'
Traceback:
1: rDMC(n = 10, phi = c(0.3, 0.5, -1, 0.2, 0.05, 2.5, 3, 1, 0.5, 0, 0, 1), dt = 1e-04)
2: eval(expr, envir, enclos)
3: eval(expr, envir, enclos)
4: eval_with_user_handlers(expr, envir, enclos, user_handlers)
5: withVisible(eval_with_user_handlers(expr, envir, enclos, user_handlers))
6: withCallingHandlers(withVisible(eval_with_user_handlers(expr, envir, enclos, user_handlers)), warning = wHandler, error = eHandler, message = mHandler)
7: handle(ev <- withCallingHandlers(withVisible(eval_with_user_handlers(expr, envir, enclos, user_handlers)), warning = wHandler, error = eHandler, message = mHandler))
8: timing_fn(handle(ev <- withCallingHandlers(withVisible(eval_with_user_handlers(expr, envir, enclos, user_handlers)), warning = wHandler, error = eHandler, message = mHandler)))
9: evaluate_call(expr, parsed$src[[i]], envir = envir, enclos = enclos, debug = debug, last = i == length(out), use_try = stop_on_error != 2L, keep_warning = keep_warning, keep_message = keep_message, log_echo = log_echo, log_warning = log_warning, output_handler = output_handler, include_timing = include_timing)
10: evaluate::evaluate(...)
11: 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$render, options))
12: in_dir(input_dir(), expr)
13: in_input_dir(evaluate(code, envir = env, new_device = FALSE, keep_warning = if (is.numeric(options$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)))
14: eng_r(options)
15: block_exec(params)
16: call_block(x)
17: process_group(group)
18: withCallingHandlers(if (tangle) process_tangle(group) else process_group(group), error = function(e) if (xfun::pkg_available("rlang", "1.0.0")) rlang::entrace(e))
19: 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)
20: process_file(text, output)
21: knitr::knit(knit_input, knit_output, envir = envir, quiet = quiet)
22: rmarkdown::render(file, encoding = encoding, quiet = quiet, envir = globalenv(), output_dir = getwd(), ...)
23: vweave_rmarkdown(...)
24: engine$weave(file, quiet = quiet, encoding = enc)
25: doTryCatch(return(expr), name, parentenv, handler)
26: tryCatchOne(expr, names, parentenv, handlers[[1L]])
27: tryCatchList(expr, classes, parentenv, handlers)
28: tryCatch({ engine$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) } outputs <- c(outputs, output)}, error = function(e) { thisOK <<- FALSE fails <<- c(fails, file) message(gettextf("Error: processing vignette '%s' failed with diagnostics:\n%s", file, conditionMessage(e)))})
29: tools::buildVignettes(dir = "/Volumes/Builds/packages/big-sur-x86_64/results/4.3/ream.Rcheck/vign_test/ream", skip = TRUE, ser_elibs = "/var/folders/2b/t0kwbtmn3p7brv2mvx39c9cr0000gn/T//Rtmp2uIaac/file166d659bd8d4.rds")
An irrecoverable exception occurred. R is aborting now ...
Flavor: r-oldrel-macos-x86_64