Running examples in ‘cna-Ex.R’ failed
The error most likely occurred in:

> base::assign(".ptime", proc.time(), pos = "CheckExEnv")
> ### 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
Examples with CPU (user + system) or elapsed time > 5s
     user system elapsed
cna 3.439  0.411   5.006