Last updated on 2026-02-11 11:51:28 CET.
| Package | ERROR | NOTE | OK |
|---|---|---|---|
| bssm | 3 | 11 | |
| changer | 14 | ||
| diagis | 14 | ||
| ggstudent | 14 | ||
| KFAS | 3 | 11 | |
| ramcmc | 14 | ||
| Rlibeemd | 2 | 12 | |
| seqHMM | 2 | 12 | |
| tsPI | 2 | 12 | |
| walker | 3 | 11 |
Current CRAN status: NOTE: 3, OK: 11
Version: 2.0.3
Check: installed package size
Result: NOTE
installed size is 34.7Mb
sub-directories of 1Mb or more:
data 1.1Mb
doc 2.8Mb
libs 30.2Mb
Flavors: r-oldrel-macos-arm64, r-oldrel-macos-x86_64, r-oldrel-windows-x86_64
Current CRAN status: OK: 14
Current CRAN status: OK: 14
Current CRAN status: OK: 14
Current CRAN status: ERROR: 3, OK: 11
Version: 1.6.0
Check: tests
Result: ERROR
Running ‘test-all.R’ [12s/13s]
Running the tests in ‘tests/test-all.R’ failed.
Complete output:
> library(testthat)
> test_check("KFAS")
Loading required package: KFAS
Please cite KFAS in publications by using:
Jouni Helske (2017). KFAS: Exponential Family State Space Models in R. Journal of Statistical Software, 78(10), 1-39. doi:10.18637/jss.v078.i10.
Call:
SSModel(formula = t12 ~ SSMcycle(period = 10, type = "common",
Q = 2) + SSMcycle(period = 10, type = "distinct", P1 = diag(c(1,
1, 2, 2)), Q = diag(1:2)) + SSMtrend(2, type = "common",
Q = diag(c(1, 0.5))) + SSMtrend(2, type = "distinct", Q = list(diag(0.1,
2), diag(0.01, 2)), P1 = diag(c(0.1, 0.01, 0.1, 0.01))) +
SSMseasonal(period = 4, type = "common") + SSMseasonal(period = 4,
type = "distinct", Q = diag(c(2, 3)), P1 = diag(c(2, 2, 2,
3, 3, 3))) + SSMseasonal(period = 5, type = "common",
sea.type = "trig") + SSMseasonal(period = 5, type = "distinct",
sea.type = "trig", Q = diag(c(0.1, 0.2)), P1 = diag(rep(c(0.1,
0.2), each = 4))) + SSMarima(ar = 0.9, ma = 0.2) + SSMregression(~-1 +
x, index = 1, Q = 1, data = d))
State space model object of class SSModel
Dimensions:
[1] Number of time points: 100
[1] Number of time series: 2
[1] Number of disturbances: 25
[1] Number of states: 38
Names of the states:
[1] x.t1 level slope level.t1 slope.t1
[6] level.t2 slope.t2 sea_dummy1 sea_dummy2 sea_dummy3
[11] sea_dummy1.t1 sea_dummy2.t1 sea_dummy3.t1 sea_dummy1.t2 sea_dummy2.t2
[16] sea_dummy3.t2 sea_trig1 sea_trig*1 sea_trig2 sea_trig*2
[21] sea_trig1.t1 sea_trig*1.t1 sea_trig2.t1 sea_trig*2.t1 sea_trig1.t2
[26] sea_trig*1.t2 sea_trig2.t2 sea_trig*2.t2 cycle cycle*
[31] cycle.t1 cycle*.t1 cycle.t2 cycle*.t2 arima1.t1
[36] arima2.t1 arima1.t2 arima2.t2
Distributions of the time series:
[1] gaussian
Object is a valid object of class SSModel.Saving _problems/testGLM-203.R
Saving _problems/testGLM-214.R
Saving _problems/testGLM-225.R
Saving _problems/testGLM-236.R
Saving _problems/testGLM-247.R
[ FAIL 5 | WARN 0 | SKIP 0 | PASS 570 ]
══ Failed tests ════════════════════════════════════════════════════════════════
── Failure ('testGLM.R:202:3'): Residuals for Gaussian GLM works properly ──────
Expected `as.numeric(rstandard(kfas.gaussian, type = "pearson"))` to equal `rstandard(glm.gaussian, type = "pearson")`.
Differences:
20/20 mismatches (average diff: 0.0434)
[1] -1.305 - -1.375 == 0.0706
[2] 0.829 - 0.874 == -0.0449
[3] 0.224 - 0.236 == -0.0121
[4] 1.632 - 1.720 == -0.0883
[5] -0.805 - -0.849 == 0.0436
[6] -0.639 - -0.673 == 0.0346
[7] 0.209 - 0.220 == -0.0113
[8] -0.760 - -0.801 == 0.0411
[9] 0.451 - 0.475 == -0.0244
...
── Failure ('testGLM.R:213:3'): Residuals for Poisson GLM works properly ───────
Expected `as.numeric(rstandard(kfas.poisson, type = "pearson"))` to equal `rstandard(glm.poisson, type = "pearson")`.
Differences:
9/9 mismatches (average diff: 0.499)
[1] -1.053 - -1.693 == 0.6403
[2] 1.436 - 2.054 == -0.6178
[3] -0.249 - -0.368 == 0.1190
[4] -0.351 - -0.564 == 0.2134
[5] -1.306 - -1.867 == 0.5617
[6] 1.618 - 2.392 == -0.7734
[7] 1.404 - 2.257 == -0.8537
[8] -0.131 - -0.187 == 0.0562
[9] -1.369 - -2.024 == 0.6545
── Failure ('testGLM.R:224:3'): Residuals for Binomial GLM works properly ──────
Expected `as.numeric(rstandard(kfas.binomial, type = "pearson"))` to equal `rstandard(glm.binomial, type = "pearson")`.
Differences:
12/12 mismatches (average diff: 0.116)
[1] -0.1488 - -0.1804 == 0.03155
[2] 0.4013 - 0.5188 == -0.11747
[3] 0.2739 - 0.3370 == -0.06314
[4] -0.9065 - -1.1300 == 0.22357
[5] -0.0334 - -0.0414 == 0.00806
[6] 0.8971 - 1.0234 == -0.12624
[7] -1.1779 - -1.3858 == 0.20798
[8] -0.1792 - -0.2182 == 0.03891
[9] 0.8180 - 0.9725 == -0.15451
...
── Failure ('testGLM.R:235:3'): Residuals for Gamma GLM works properly ─────────
Expected `as.numeric(rstandard(kfas.gamma2, type = "pearson"))` to equal `rstandard(glm.gamma, type = "pearson")`.
Differences:
9/9 mismatches (average diff: 0.175)
[1] 2.355 - 3.2471 == -0.89260
[2] -0.406 - -0.4648 == 0.05920
[3] -0.884 - -0.9630 == 0.07860
[4] -0.922 - -0.9849 == 0.06282
[5] -1.006 - -1.0681 == 0.06168
[6] -0.425 - -0.4559 == 0.03046
[7] 0.041 - 0.0455 == -0.00451
[8] 0.609 - 0.7059 == -0.09684
[9] 1.342 - 1.6293 == -0.28763
── Failure ('testGLM.R:246:3'): Residuals for negative binomial GLM works properly ──
Expected `as.numeric(rstandard(kfas.NB, type = "pearson"))` to equal `rstandard(glm.NB, type = "pearson")`.
Differences:
146/146 mismatches (average diff: 0.0411)
[1] -1.1437 - -1.2378 == 0.0941
[2] -0.4169 - -0.4512 == 0.0343
[3] -0.1746 - -0.1890 == 0.0144
[4] -0.7612 - -0.8010 == 0.0398
[5] -0.7612 - -0.8010 == 0.0398
[6] 0.0268 - 0.0282 == -0.0014
[7] 0.7164 - 0.7538 == -0.0374
[8] 0.9134 - 0.9611 == -0.0477
[9] -0.2934 - -0.3186 == 0.0252
...
[ FAIL 5 | WARN 0 | SKIP 0 | PASS 570 ]
Error:
! Test failures.
Execution halted
Flavor: r-devel-linux-x86_64-debian-gcc
Version: 1.6.0
Check: tests
Result: ERROR
Running ‘test-all.R’ [28s/30s]
Running the tests in ‘tests/test-all.R’ failed.
Complete output:
> library(testthat)
> test_check("KFAS")
Loading required package: KFAS
Please cite KFAS in publications by using:
Jouni Helske (2017). KFAS: Exponential Family State Space Models in R. Journal of Statistical Software, 78(10), 1-39. doi:10.18637/jss.v078.i10.
Call:
SSModel(formula = t12 ~ SSMcycle(period = 10, type = "common",
Q = 2) + SSMcycle(period = 10, type = "distinct", P1 = diag(c(1,
1, 2, 2)), Q = diag(1:2)) + SSMtrend(2, type = "common",
Q = diag(c(1, 0.5))) + SSMtrend(2, type = "distinct", Q = list(diag(0.1,
2), diag(0.01, 2)), P1 = diag(c(0.1, 0.01, 0.1, 0.01))) +
SSMseasonal(period = 4, type = "common") + SSMseasonal(period = 4,
type = "distinct", Q = diag(c(2, 3)), P1 = diag(c(2, 2, 2,
3, 3, 3))) + SSMseasonal(period = 5, type = "common",
sea.type = "trig") + SSMseasonal(period = 5, type = "distinct",
sea.type = "trig", Q = diag(c(0.1, 0.2)), P1 = diag(rep(c(0.1,
0.2), each = 4))) + SSMarima(ar = 0.9, ma = 0.2) + SSMregression(~-1 +
x, index = 1, Q = 1, data = d))
State space model object of class SSModel
Dimensions:
[1] Number of time points: 100
[1] Number of time series: 2
[1] Number of disturbances: 25
[1] Number of states: 38
Names of the states:
[1] x.t1 level slope level.t1 slope.t1
[6] level.t2 slope.t2 sea_dummy1 sea_dummy2 sea_dummy3
[11] sea_dummy1.t1 sea_dummy2.t1 sea_dummy3.t1 sea_dummy1.t2 sea_dummy2.t2
[16] sea_dummy3.t2 sea_trig1 sea_trig*1 sea_trig2 sea_trig*2
[21] sea_trig1.t1 sea_trig*1.t1 sea_trig2.t1 sea_trig*2.t1 sea_trig1.t2
[26] sea_trig*1.t2 sea_trig2.t2 sea_trig*2.t2 cycle cycle*
[31] cycle.t1 cycle*.t1 cycle.t2 cycle*.t2 arima1.t1
[36] arima2.t1 arima1.t2 arima2.t2
Distributions of the time series:
[1] gaussian
Object is a valid object of class SSModel.Saving _problems/testGLM-203.R
Saving _problems/testGLM-214.R
Saving _problems/testGLM-225.R
Saving _problems/testGLM-236.R
Saving _problems/testGLM-247.R
[ FAIL 5 | WARN 0 | SKIP 0 | PASS 570 ]
══ Failed tests ════════════════════════════════════════════════════════════════
── Failure ('testGLM.R:202:3'): Residuals for Gaussian GLM works properly ──────
Expected `as.numeric(rstandard(kfas.gaussian, type = "pearson"))` to equal `rstandard(glm.gaussian, type = "pearson")`.
Differences:
20/20 mismatches (average diff: 0.0434)
[1] -1.305 - -1.375 == 0.0706
[2] 0.829 - 0.874 == -0.0449
[3] 0.224 - 0.236 == -0.0121
[4] 1.632 - 1.720 == -0.0883
[5] -0.805 - -0.849 == 0.0436
[6] -0.639 - -0.673 == 0.0346
[7] 0.209 - 0.220 == -0.0113
[8] -0.760 - -0.801 == 0.0411
[9] 0.451 - 0.475 == -0.0244
...
── Failure ('testGLM.R:213:3'): Residuals for Poisson GLM works properly ───────
Expected `as.numeric(rstandard(kfas.poisson, type = "pearson"))` to equal `rstandard(glm.poisson, type = "pearson")`.
Differences:
9/9 mismatches (average diff: 0.499)
[1] -1.053 - -1.693 == 0.6403
[2] 1.436 - 2.054 == -0.6178
[3] -0.249 - -0.368 == 0.1190
[4] -0.351 - -0.564 == 0.2134
[5] -1.306 - -1.867 == 0.5617
[6] 1.618 - 2.392 == -0.7734
[7] 1.404 - 2.257 == -0.8537
[8] -0.131 - -0.187 == 0.0562
[9] -1.369 - -2.024 == 0.6545
── Failure ('testGLM.R:224:3'): Residuals for Binomial GLM works properly ──────
Expected `as.numeric(rstandard(kfas.binomial, type = "pearson"))` to equal `rstandard(glm.binomial, type = "pearson")`.
Differences:
12/12 mismatches (average diff: 0.116)
[1] -0.1488 - -0.1804 == 0.03155
[2] 0.4013 - 0.5188 == -0.11747
[3] 0.2739 - 0.3370 == -0.06314
[4] -0.9065 - -1.1300 == 0.22357
[5] -0.0334 - -0.0414 == 0.00806
[6] 0.8971 - 1.0234 == -0.12624
[7] -1.1779 - -1.3858 == 0.20798
[8] -0.1792 - -0.2182 == 0.03891
[9] 0.8180 - 0.9725 == -0.15451
...
── Failure ('testGLM.R:235:3'): Residuals for Gamma GLM works properly ─────────
Expected `as.numeric(rstandard(kfas.gamma2, type = "pearson"))` to equal `rstandard(glm.gamma, type = "pearson")`.
Differences:
9/9 mismatches (average diff: 0.175)
[1] 2.355 - 3.2471 == -0.89260
[2] -0.406 - -0.4648 == 0.05920
[3] -0.884 - -0.9630 == 0.07860
[4] -0.922 - -0.9849 == 0.06282
[5] -1.006 - -1.0681 == 0.06168
[6] -0.425 - -0.4559 == 0.03046
[7] 0.041 - 0.0455 == -0.00451
[8] 0.609 - 0.7059 == -0.09684
[9] 1.342 - 1.6293 == -0.28763
── Failure ('testGLM.R:246:3'): Residuals for negative binomial GLM works properly ──
Expected `as.numeric(rstandard(kfas.NB, type = "pearson"))` to equal `rstandard(glm.NB, type = "pearson")`.
Differences:
146/146 mismatches (average diff: 0.0411)
[1] -1.1437 - -1.2378 == 0.0941
[2] -0.4169 - -0.4512 == 0.0343
[3] -0.1746 - -0.1890 == 0.0144
[4] -0.7612 - -0.8010 == 0.0398
[5] -0.7612 - -0.8010 == 0.0398
[6] 0.0268 - 0.0282 == -0.0014
[7] 0.7164 - 0.7538 == -0.0374
[8] 0.9134 - 0.9611 == -0.0477
[9] -0.2934 - -0.3186 == 0.0252
...
[ FAIL 5 | WARN 0 | SKIP 0 | PASS 570 ]
Error:
! Test failures.
Execution halted
Flavor: r-devel-linux-x86_64-fedora-clang
Version: 1.6.0
Check: tests
Result: ERROR
Running ‘test-all.R’ [26s/27s]
Running the tests in ‘tests/test-all.R’ failed.
Complete output:
> library(testthat)
> test_check("KFAS")
Loading required package: KFAS
Please cite KFAS in publications by using:
Jouni Helske (2017). KFAS: Exponential Family State Space Models in R. Journal of Statistical Software, 78(10), 1-39. doi:10.18637/jss.v078.i10.
Call:
SSModel(formula = t12 ~ SSMcycle(period = 10, type = "common",
Q = 2) + SSMcycle(period = 10, type = "distinct", P1 = diag(c(1,
1, 2, 2)), Q = diag(1:2)) + SSMtrend(2, type = "common",
Q = diag(c(1, 0.5))) + SSMtrend(2, type = "distinct", Q = list(diag(0.1,
2), diag(0.01, 2)), P1 = diag(c(0.1, 0.01, 0.1, 0.01))) +
SSMseasonal(period = 4, type = "common") + SSMseasonal(period = 4,
type = "distinct", Q = diag(c(2, 3)), P1 = diag(c(2, 2, 2,
3, 3, 3))) + SSMseasonal(period = 5, type = "common",
sea.type = "trig") + SSMseasonal(period = 5, type = "distinct",
sea.type = "trig", Q = diag(c(0.1, 0.2)), P1 = diag(rep(c(0.1,
0.2), each = 4))) + SSMarima(ar = 0.9, ma = 0.2) + SSMregression(~-1 +
x, index = 1, Q = 1, data = d))
State space model object of class SSModel
Dimensions:
[1] Number of time points: 100
[1] Number of time series: 2
[1] Number of disturbances: 25
[1] Number of states: 38
Names of the states:
[1] x.t1 level slope level.t1 slope.t1
[6] level.t2 slope.t2 sea_dummy1 sea_dummy2 sea_dummy3
[11] sea_dummy1.t1 sea_dummy2.t1 sea_dummy3.t1 sea_dummy1.t2 sea_dummy2.t2
[16] sea_dummy3.t2 sea_trig1 sea_trig*1 sea_trig2 sea_trig*2
[21] sea_trig1.t1 sea_trig*1.t1 sea_trig2.t1 sea_trig*2.t1 sea_trig1.t2
[26] sea_trig*1.t2 sea_trig2.t2 sea_trig*2.t2 cycle cycle*
[31] cycle.t1 cycle*.t1 cycle.t2 cycle*.t2 arima1.t1
[36] arima2.t1 arima1.t2 arima2.t2
Distributions of the time series:
[1] gaussian
Object is a valid object of class SSModel.Saving _problems/testGLM-203.R
Saving _problems/testGLM-214.R
Saving _problems/testGLM-225.R
Saving _problems/testGLM-236.R
Saving _problems/testGLM-247.R
[ FAIL 5 | WARN 0 | SKIP 0 | PASS 570 ]
══ Failed tests ════════════════════════════════════════════════════════════════
── Failure ('testGLM.R:202:3'): Residuals for Gaussian GLM works properly ──────
Expected `as.numeric(rstandard(kfas.gaussian, type = "pearson"))` to equal `rstandard(glm.gaussian, type = "pearson")`.
Differences:
20/20 mismatches (average diff: 0.0434)
[1] -1.305 - -1.375 == 0.0706
[2] 0.829 - 0.874 == -0.0449
[3] 0.224 - 0.236 == -0.0121
[4] 1.632 - 1.720 == -0.0883
[5] -0.805 - -0.849 == 0.0436
[6] -0.639 - -0.673 == 0.0346
[7] 0.209 - 0.220 == -0.0113
[8] -0.760 - -0.801 == 0.0411
[9] 0.451 - 0.475 == -0.0244
...
── Failure ('testGLM.R:213:3'): Residuals for Poisson GLM works properly ───────
Expected `as.numeric(rstandard(kfas.poisson, type = "pearson"))` to equal `rstandard(glm.poisson, type = "pearson")`.
Differences:
9/9 mismatches (average diff: 0.499)
[1] -1.053 - -1.693 == 0.6403
[2] 1.436 - 2.054 == -0.6178
[3] -0.249 - -0.368 == 0.1190
[4] -0.351 - -0.564 == 0.2134
[5] -1.306 - -1.867 == 0.5617
[6] 1.618 - 2.392 == -0.7734
[7] 1.404 - 2.257 == -0.8537
[8] -0.131 - -0.187 == 0.0562
[9] -1.369 - -2.024 == 0.6545
── Failure ('testGLM.R:224:3'): Residuals for Binomial GLM works properly ──────
Expected `as.numeric(rstandard(kfas.binomial, type = "pearson"))` to equal `rstandard(glm.binomial, type = "pearson")`.
Differences:
12/12 mismatches (average diff: 0.116)
[1] -0.1488 - -0.1804 == 0.03155
[2] 0.4013 - 0.5188 == -0.11747
[3] 0.2739 - 0.3370 == -0.06314
[4] -0.9065 - -1.1300 == 0.22357
[5] -0.0334 - -0.0414 == 0.00806
[6] 0.8971 - 1.0234 == -0.12624
[7] -1.1779 - -1.3858 == 0.20798
[8] -0.1792 - -0.2182 == 0.03891
[9] 0.8180 - 0.9725 == -0.15451
...
── Failure ('testGLM.R:235:3'): Residuals for Gamma GLM works properly ─────────
Expected `as.numeric(rstandard(kfas.gamma2, type = "pearson"))` to equal `rstandard(glm.gamma, type = "pearson")`.
Differences:
9/9 mismatches (average diff: 0.175)
[1] 2.355 - 3.2471 == -0.89260
[2] -0.406 - -0.4648 == 0.05920
[3] -0.884 - -0.9630 == 0.07860
[4] -0.922 - -0.9849 == 0.06282
[5] -1.006 - -1.0681 == 0.06168
[6] -0.425 - -0.4559 == 0.03046
[7] 0.041 - 0.0455 == -0.00451
[8] 0.609 - 0.7059 == -0.09684
[9] 1.342 - 1.6293 == -0.28763
── Failure ('testGLM.R:246:3'): Residuals for negative binomial GLM works properly ──
Expected `as.numeric(rstandard(kfas.NB, type = "pearson"))` to equal `rstandard(glm.NB, type = "pearson")`.
Differences:
146/146 mismatches (average diff: 0.0411)
[1] -1.1437 - -1.2378 == 0.0941
[2] -0.4169 - -0.4512 == 0.0343
[3] -0.1746 - -0.1890 == 0.0144
[4] -0.7612 - -0.8010 == 0.0398
[5] -0.7612 - -0.8010 == 0.0398
[6] 0.0268 - 0.0282 == -0.0014
[7] 0.7164 - 0.7538 == -0.0374
[8] 0.9134 - 0.9611 == -0.0477
[9] -0.2934 - -0.3186 == 0.0252
...
[ FAIL 5 | WARN 0 | SKIP 0 | PASS 570 ]
Error:
! Test failures.
Execution halted
Flavor: r-devel-linux-x86_64-fedora-gcc
Current CRAN status: OK: 14
Current CRAN status: NOTE: 2, OK: 12
Version: 1.4.4
Check: CRAN incoming feasibility
Result: NOTE
Maintainer: ‘Jouni Helske <jouni.helske@iki.fi>’
Found the following (possibly) invalid file URI:
URI: https//cranlogs.r-pkg.org:443/badges/Rlibeemd
From: README.md
Flavors: r-devel-linux-x86_64-debian-clang, r-devel-linux-x86_64-debian-gcc
Current CRAN status: NOTE: 2, OK: 12
Version: 2.1.0
Check: installed package size
Result: NOTE
installed size is 25.6Mb
sub-directories of 1Mb or more:
libs 23.3Mb
Flavors: r-oldrel-macos-arm64, r-oldrel-macos-x86_64
Current CRAN status: NOTE: 2, OK: 12
Version: 1.0.4
Check: CRAN incoming feasibility
Result: NOTE
Maintainer: ‘Jouni Helske <jouni.helske@iki.fi>’
No Authors@R field in DESCRIPTION.
Please add one, modifying
Authors@R: person(given = "Jouni",
family = "Helske",
role = c("aut", "cre"),
email = "jouni.helske@iki.fi")
as necessary.
Flavors: r-devel-linux-x86_64-debian-clang, r-devel-linux-x86_64-debian-gcc
Current CRAN status: NOTE: 3, OK: 11
Version: 1.0.10
Check: installed package size
Result: NOTE
installed size is 115.6Mb
sub-directories of 1Mb or more:
libs 114.2Mb
Flavors: r-oldrel-macos-arm64, r-oldrel-macos-x86_64, r-oldrel-windows-x86_64
Version: 1.0.10
Check: for GNU extensions in Makefiles
Result: NOTE
GNU make is a SystemRequirements.
Flavors: r-oldrel-macos-arm64, r-oldrel-macos-x86_64