CRAN Package Check Results for Package cna

Last updated on 2026-04-30 18:53:22 CEST.

Flavor	Version	Tinstall	Tcheck	Ttotal	Status	Flags
r-devel-linux-x86_64-debian-clang	4.0.3	70.24	164.65	234.89	OK
r-devel-linux-x86_64-debian-gcc	4.0.3	59.35	109.47	168.82	OK
r-devel-linux-x86_64-fedora-clang	4.0.3	127.00	277.15	404.15	OK
r-devel-linux-x86_64-fedora-gcc	4.0.3	170.00	286.59	456.59	OK
r-devel-windows-x86_64	4.0.3	96.00	199.00	295.00	OK
r-patched-linux-x86_64	4.0.3	77.83	149.27	227.10	OK
r-release-linux-x86_64	4.0.3	71.34	152.00	223.34	OK
r-release-macos-arm64	4.0.3	16.00	31.00	47.00	OK
r-release-macos-x86_64	4.0.3	45.00	123.00	168.00	ERROR
r-release-windows-x86_64	4.0.3	87.00	205.00	292.00	OK
r-oldrel-macos-arm64	4.0.3				OK
r-oldrel-macos-x86_64	4.0.3	39.00	108.00	147.00	OK
r-oldrel-windows-x86_64	4.0.3	104.00	252.00	356.00	OK

Check Details

Version: 4.0.3
Check: examples
Result: ERROR Running examples in ‘cna-Ex.R’ failed The error most likely occurred in: > ### Name: randomConds > ### Title: Generate random solution formulas > ### Aliases: randomConds randomAsf randomCsf > > ### ** Examples > # randomAsf > # --------- > # Asf generated from explicitly specified binary factors. > randomAsf(full.ct("H*I*T*R*K")) [1] "r*T+R*i*k+T*K<->H" > randomAsf(full.ct("Johnny*Debby*Aurora*Mars*James*Sonja")) [1] "james*sonja<->DEBBY" > > # Asf generated from a specified number of binary factors. > randomAsf(full.ct(7)) [1] "F*B+b*f*a*E<->G" > # In shorthand form. > randomAsf(7) [1] "d*A*e*F+G*e*d*A+E*G*F<->C" > > # Randomly choose positive or negative outcome values. > replicate(10, randomAsf(7, positive = FALSE)) [1] "c*b+b*D*F<->g" "C*F*e+b*f*c+E*B<->A" [3] "D*g*e*b+G*B*C*D+E*B*g<->A" "E*b+D*G<->c" [5] "B*F*D+e*G*c*D+b*G<->a" "g*d*a+b*A+f*b+g*f<->E" [7] "E*g+E*A*F*c<->d" "A*g+e*c*g+e*b+a*D<->f" [9] "E*g*A+F*e+A*b<->d" "c*b*D*A+C*B*d*g+a*d*C*G<->e" > > # Asf generated from an existing data frame. > randomAsf(d.educate) [1] "U*d+U*G<->L" > > # Specify the outcome. > randomAsf(d.educate, outcome = "G") [1] "u*l*D+U*e<->G" > > # Specify the complexity. > # Initial complexity of 2 conjunctions and 2 disjunctions. > randomAsf(full.ct(7), compl = 2) [1] "F*c<->A" > # Initial complexity of 3:4 conjunctions and 3:4 disjunctions. > randomAsf(full.ct(7), compl = 3:4) [1] "A*g*e*F+D*g*f*E+e*f*G*a+F*C*E*D<->B" > # Initial complexity of 2 conjunctions and 3:4 disjunctions. > randomAsf(full.ct(7), compl = list(2,3:4)) [1] "d*g+a*G+e*B<->C" > > # Redundancy-freeness relative to x instead of full.ct(x). > randomAsf(d.educate, outcome = "G", how = "minimal") [1] "L*d<->G" > > # Asf with multi-value factors. > randomAsf(allCombs(c(3,4,3,5,3,4))) [1] "B=2*E=1*F=1+A=2*F=1+F=1*B=1*E=1<->C=3" > # Set the outcome value. > randomAsf(allCombs(c(3,4,3,5,3,4)), outcome = "B=4") [1] "E=3*F=1*D=2+A=2*E=1*F=1+F=4*A=2+D=4*C=1*A=2<->B=4" > # Choose a random value of factor B. > randomAsf(allCombs(c(3,4,3,5,3,4)), outcome = "B") [1] "E=2*F=2*A=3*D=5+E=1*D=5*A=3*C=2<->B=1" > > # Asf from fuzzy-set data. > randomAsf(d.jobsecurity) [1] "l*JSR*S*R+s*V*l*r<->C" > randomAsf(d.jobsecurity, outcome = "JSR") [1] "s*r*v+R*V+v*l*r<->JSR" > > # Generate 20 asf for outcome "e". > replicate(20, randomAsf(7, compl = list(2:3, 3:4), outcome = "e")) [1] "G*b*A+f*B+f*d*G<->e" "b*F*C+B*D+g*c*d<->e" [3] "F*G*a+a*d+A*c<->e" "G*B+D*a*C+A*b<->e" [5] "C*g*a+g*a*f+A*B+F*c*B<->e" "F*D+A*g*b+c*a<->e" [7] "A*F+a*f*g+d*f*b<->e" "d*B*f+b*F*d+g*F<->e" [9] "f*A*b+g*D+B*A*C<->e" "A*D+D*B+B*G+a*G*d<->e" [11] "d*g*b+c*a*D+b*a<->e" "A*B+A*C+G*d*B<->e" [13] "b*F+d*B+f*a<->e" "G*D+G*C+C*D+F*G<->e" [15] "F*b+g<->e" "a*f+B*D+g*A*F+d*C<->e" [17] "D*f*C+g*F*A+b*d*g<->e" "C*f*a+g*A*f+B*G+B*F<->e" [19] "D*A+f*c*A+a*B*C<->e" "B*c+c*D*F+d*f+C*b*a<->e" > > > # randomCsf > # --------- > # Csf generated from explicitly specified binary factors. > randomCsf(full.ct("H*I*T*R*K*Q*P")) [1] "(t*h*I+r*T*h*K<->P)*(i*t*r*H+P*T*I+h*I*R<->Q)" > > # Csf generated from a specified number of binary factors. > randomCsf(full.ct(7)) [1] "(B*e+C*F+B*F<->A)*(E*B+a*E+d*F*B<->G)" > # In shorthand form. > randomCsf(7) [1] "(b*f*e+F*C*b<->A)*(a*b+b*c+F*E*C<->D)*(C*D+A*c<->G)" > > # Randomly choose positive or negative outcome values. > replicate(5, randomCsf(7, positive = FALSE)) [1] "(g*E*B*d+B*g*e*D+G*d*e*b<->c)*(E*B+g*C*D<->A)*(G*A+c*a+G*E*D<->F)" [2] "(E*B+B*G<->c)*(G*E+G*B<->A)*(A<->F)" [3] "(G*C+B*G*a+g*A*B<->D)*(G*B+c*d*A+G*d<->f)*(a*g<->E)" [4] "(d*b+f*b*C<->A)*(F*d+B*F*C<->e)*(c*a<->g)" [5] "(F+A*c<->d)*(D*c+F*c<->B)*(B*d<->E)*(D*a+B<->G)" > > # Specify the outcomes. > randomCsf(d.volatile, outcome = c("RB","se")) [1] "(PG*OD*el+CS*vo2*PG+el*cs*PC<->RB)*(rb*PG+CS*vo2*pg*EL+UP*rb*PC*EL<->se)" > > # Specify the complexity. > randomCsf(d.volatile, outcome = c("RB","se"), compl = 2) [1] "(PG*EL<->RB)*(vo2*cs<->se)" > randomCsf(full.ct(7), compl = 3:4) [1] "(B*c*E+c*b*f+a*c*F<->G)*(c*g*f+b*E*F*a+a*g*E+b*g*a<->D)" > randomCsf(full.ct(7), compl = list(2,4)) [1] "(G*f+D*G<->B)*(B*D<->A)" > > # Specify the maximal number of factors. > randomCsf(d.highdim, maxVarNum = 10) [1] "(V48*V36*V46+V38*v48+v48*V24+V6*V38*v36*v44<->V40)*(v38*v40*V46+V44*v6*v46+V15*v48*V46+V6*v15<->V34)*(v40*v46*v34*V15+v24*v15*v34+v15*V34*V36+V24*v48<->V28)" > > # Specify the number of asf. > randomCsf(full.ct(7), n.asf = 3) [1] "(g*C+f*g*e<->D)*(G*e<->A)*(G<->B)" > > # Csf with multi-value factors. > randomCsf(allCombs(c(3,4,3,5,3,4))) [1] "(D=3*A=2<->F=2)*(F=4*D=3<->B=2)*(D=4*F=4<->E=3)*(E=1*A=3<->C=1)" > # Set the outcome values. > randomCsf(allCombs(c(3,4,3,5,3,4)), outcome = c("A=1","B=4")) [1] "(E=2*C=3*F=2+C=1*F=1*D=1*E=3+E=2*C=1*D=2*F=2<->A=1)*(E=2*D=1*C=3*F=3+F=4*A=3*C=3*D=2<->B=4)" > > # Generate 20 csf. > > > # Inverse searches > # ---------------- > # === Ideal Data === > # Draw the data generating structure. (Every run yields different > # targets and data.) > target <- randomCsf(full.ct(5), n.asf = 2) > target [1] "(C*A+D*a<->E)*(d*e*A+E*a<->B)" > # Select the cases compatible with the target. > x <- selectCases(target) > # Run CNA without an ordering. > mycna <- cna(x) > # Extract the csf. > csfs <- csf(mycna) > # Check whether the target is completely returned. > any(unlist(lapply(csfs$condition, identical.model, target))) [1] TRUE > > # === Data fragmentation (20% missing observations) === > # Draw the data generating structure. (Every run yields different > # targets and data.) > target <- randomCsf(full.ct(7), n.asf = 2) > target [1] "(c*A+d*c*F+G*f*c+g*A<->B)*(f*g*D+A*B*f<->E)" > # Generate the ideal data. > x <- ct2df(selectCases(target)) > # Introduce fragmentation. > x <- x[-sample(1:nrow(x), nrow(x)*0.2), ] > # Run CNA without an ordering. > mycna <- cna(x) > # Extract the csf. > csfs <- csf(mycna) > # Check whether (a causal submodel of) the target is returned. > any(unlist(lapply(csfs$condition, function(x) + frscore::causal_submodel(x, target)))) Error in loadNamespace(x) : there is no package called ‘frscore’ Calls: unlist ... loadNamespace -> withRestarts -> withOneRestart -> doWithOneRestart Execution halted Flavor: r-release-macos-x86_64