* using log directory ‘/data/blackswan/ripley/R/packages/tests-devel/psychonetrics.Rcheck’
* using R Under development (unstable) (2026-02-11 r89406)
* using platform: x86_64-pc-linux-gnu
* R was compiled by
    gcc (GCC) 14.2.1 20240912 (Red Hat 14.2.1-3)
    GNU Fortran (GCC) 14.2.1 20240912 (Red Hat 14.2.1-3)
* running under: Fedora Linux 40 (Workstation Edition)
* using session charset: UTF-8
* current time: 2026-02-11 22:29:07 UTC
* checking for file ‘psychonetrics/DESCRIPTION’ ... OK
* checking extension type ... Package
* this is package ‘psychonetrics’ version ‘0.13.2’
* checking package namespace information ... OK
* checking package dependencies ... OK
* checking if this is a source package ... OK
* checking if there is a namespace ... OK
* checking for executable files ... OK
* checking for hidden files and directories ... OK
* checking for portable file names ... OK
* checking for sufficient/correct file permissions ... OK
* checking whether package ‘psychonetrics’ can be installed ... [14m/10m] OK
* used C++ compiler: ‘g++ (GCC) 14.2.1 20240912 (Red Hat 14.2.1-3)’
* checking package directory ... OK
* checking DESCRIPTION meta-information ... OK
* checking top-level files ... OK
* checking for left-over files ... OK
* checking index information ... OK
* checking package subdirectories ... OK
* checking code files for non-ASCII characters ... OK
* checking R files for syntax errors ... OK
* checking whether the package can be loaded ... OK
* checking whether the package can be loaded with stated dependencies ... OK
* checking whether the package can be unloaded cleanly ... OK
* checking whether the namespace can be loaded with stated dependencies ... OK
* checking whether the namespace can be unloaded cleanly ... OK
* checking loading without being on the library search path ... OK
* checking whether startup messages can be suppressed ... OK
* checking dependencies in R code ... OK
* checking S3 generic/method consistency ... OK
* checking replacement functions ... OK
* checking foreign function calls ... OK
* checking R code for possible problems ... [43s/44s] OK
* checking Rd files ... OK
* checking Rd metadata ... OK
* checking Rd line widths ... OK
* checking Rd cross-references ... OK
* checking for missing documentation entries ... OK
* checking for code/documentation mismatches ... OK
* checking Rd \usage sections ... OK
* checking Rd contents ... OK
* checking for unstated dependencies in examples ... OK
* checking contents of ‘data’ directory ... OK
* checking data for non-ASCII characters ... OK
* checking data for ASCII and uncompressed saves ... OK
* checking line endings in C/C++/Fortran sources/headers ... OK
* checking line endings in Makefiles ... OK
* checking compilation flags in Makevars ... OK
* checking for GNU extensions in Makefiles ... OK
* checking for portable use of $(BLAS_LIBS) and $(LAPACK_LIBS) ... OK
* checking use of PKG_*FLAGS in Makefiles ... OK
* checking use of SHLIB_OPENMP_*FLAGS in Makefiles ... OK
* checking compilation flags used ... OK
* checking compiled code ... OK
* checking examples ... OK
* checking examples with --run-donttest ... [21s/21s] ERROR
Running examples in ‘psychonetrics-Ex.R’ failed
The error most likely occurred in:

> ### Name: lvm
> ### Title: Continuous latent variable family of psychonetrics models
> ### Aliases: lvm lnm rnm lrnm
> 
> ### ** Examples
> 
> library("dplyr")

Attaching package: ‘dplyr’

The following objects are masked from ‘package:stats’:

    filter, lag

The following objects are masked from ‘package:base’:

    intersect, setdiff, setequal, union

> 
> ### Confirmatory Factor Analysis ###
> 
> # Example also shown in https://youtu.be/Hdu5z-fwuk8
> 
> # Load data:
> data(StarWars)
> 
> # Originals only:
> Lambda <- matrix(1,4)
> 
> # Model:
> mod0 <- lvm(StarWars, lambda = Lambda, vars = c("Q1","Q5","Q6","Q7"), 
+             identification = "variance", latents = "Originals")
>             
> # Run model:
> mod0 <- mod0 %>% runmodel
> 
> # Evaluate fit:
> mod0 %>% fit
           Measure    Value
              logl -1676.50
 unrestricted.logl -1676.04
     baseline.logl -1791.67
              nvar        4
              nobs       14
              npar       12
                df        2
         objective     5.02
             chisq     0.93
            pvalue     0.63
    baseline.chisq   231.26
     baseline.npar        8
       baseline.df        6
   baseline.pvalue      ~ 0
               nfi      1.0
              pnfi     0.33
               tli     1.01
              nnfi     1.01
               rfi     0.99
               ifi     1.00
               rni     1.00
               cfi        1
             rmsea      ~ 0
    rmsea.ci.lower      ~ 0
    rmsea.ci.upper    0.096
      rmsea.pvalue     0.78
            aic.ll  3377.00
           aic.ll2  3378.21
             aic.x    -3.07
            aic.x2    24.93
               bic  3420.23
              bic2  3382.18
           ebic.25  3436.86
            ebic.5  3453.50
           ebic.75  3466.81
             ebic1  3486.77
> 
> ## No test: 
> # Full analysis
> # Factor loadings matrix:
> Lambda <- matrix(0, 10, 3)
> Lambda[1:4,1] <- 1
> Lambda[c(1,5:7),2] <- 1
> Lambda[c(1,8:10),3] <- 1
> 
> # Observed variables:
> obsvars <- paste0("Q",1:10)
> 
> # Latents:
> latents <- c("Prequels","Original","Sequels")
> 
> # Make model:
> mod1 <- lvm(StarWars, lambda = Lambda, vars = obsvars, 
+             identification = "variance", latents = latents)
> 
> # Run model:
> mod1 <- mod1 %>% runmodel
> 
> # Look at fit:
> mod1
                       _                      _        _          
                      | |                    | |      (_)         
  _ __  ___ _   _  ___| |__   ___  _ __   ___| |_ _ __ _  ___ ___ 
 |  _ \/ __| | | |/ __|  _ \ / _ \|  _ \ / _ \ __|  __| |/ __/ __|
 | |_) \__ \ |_| | (__| | | | (_) | | | |  __/ |_| |  | | (__\__ \
 | .__/|___/\__, |\___|_| |_|\___/|_| |_|\___|\__|_|  |_|\___|___/
 | |         __/ |                                                
 |_|        |___/                                  Version: 0.13.2

General: 
	- psychonetrics version: 0.13.2 
	- Model last edited at: 2026-02-11 22:41:45.03815

Sample: 
	- Number of cases: 271 
	- Number of groups: 1 
	- Number of observed summary statistics: 65

Model: 
	- Model used: Latent variable model (LVM) 
	- Submodel used: Structural equation model (SEM) 
	- Number of parameters: 35

Estimation: 
	- Optimizer used: nlminb 
	- Estimator used: Maximum likelihood estimation (ML) 
	- Message: relative convergence (4)

Fit: 
	- Model Fit Test Statistic: 34.58 
	- Degrees of freedom: 30 
	- p-value (Chi-square): 0.26

Tips: 
	- Use 'psychonetrics::compare' to compare psychonetrics models 
	- Use 'psychonetrics::fit' to inspect model fit 
	- Use 'psychonetrics::parameters' to inspect model parameters 
	- Use 'psychonetrics::MIs' to inspect modification indices
> 
> # Look at parameter estimates:
> mod1 %>% parameters

 Parameters for group fullsample
	-  nu  
 var1 op var2  est    se        p row col par
   Q1 ~1      2.76 0.088 < 0.0001   1   1   1
   Q2 ~1      4.19 0.069 < 0.0001   2   1   2
   Q3 ~1      2.82 0.068 < 0.0001   3   1   3
   Q4 ~1      3.21 0.074 < 0.0001   4   1   4
   Q5 ~1      2.44 0.073 < 0.0001   5   1   5
   Q6 ~1      2.69 0.073 < 0.0001   6   1   6
   Q7 ~1      3.00 0.074 < 0.0001   7   1   7
   Q8 ~1      2.38 0.084 < 0.0001   8   1   8
   Q9 ~1      3.25 0.063 < 0.0001   9   1   9
  Q10 ~1      2.12 0.062 < 0.0001  10   1  10

	-  lambda  
 var1 op     var2     est    se        p row col par
   Q1 ~= Prequels    0.21  0.15     0.18   1   1  11
   Q2 ~= Prequels    0.30  0.10   0.0031   2   1  12
   Q3 ~= Prequels    0.63  0.12 < 0.0001   3   1  13
   Q4 ~= Prequels   -0.49  0.11 < 0.0001   4   1  14
   Q1 ~= Original    1.02  0.20 < 0.0001   1   2  15
   Q5 ~= Original    0.57 0.077 < 0.0001   5   2  16
   Q6 ~= Original    0.94 0.074 < 0.0001   6   2  17
   Q7 ~= Original    0.67 0.077 < 0.0001   7   2  18
   Q1 ~=  Sequels -0.0049  0.25     0.98   1   3  19
   Q8 ~=  Sequels    0.85  0.10 < 0.0001   8   3  20
   Q9 ~=  Sequels    0.15 0.076    0.055   9   3  21
  Q10 ~=  Sequels    0.63 0.074 < 0.0001  10   3  22

	-  sigma_zeta (symmetric) 
     var1 op     var2  est    se        p row col par
 Prequels ~~ Prequels    1                  1   1   0
 Original ~~ Prequels 0.29  0.11   0.0080   2   1  23
  Sequels ~~ Prequels 0.50  0.12 < 0.0001   3   1  24
 Original ~~ Original    1                  2   2   0
  Sequels ~~ Original 0.72 0.078 < 0.0001   3   2  25
  Sequels ~~  Sequels    1                  3   3   0

	-  sigma_epsilon (symmetric) 
 var1 op var2  est    se        p row col par
   Q1 ~~   Q1 0.90  0.13 < 0.0001   1   1  26
   Q2 ~~   Q2 1.19  0.11 < 0.0001   2   2  27
   Q3 ~~   Q3 0.86  0.15 < 0.0001   3   3  28
   Q4 ~~   Q4 1.25  0.14 < 0.0001   4   4  29
   Q5 ~~   Q5 1.10  0.10 < 0.0001   5   5  30
   Q6 ~~   Q6 0.57 0.093 < 0.0001   6   6  31
   Q7 ~~   Q7 1.05  0.10 < 0.0001   7   7  32
   Q8 ~~   Q8 1.20  0.15 < 0.0001   8   8  33
   Q9 ~~   Q9 1.05 0.091 < 0.0001   9   9  34
  Q10 ~~  Q10 0.65 0.083 < 0.0001  10  10  35
> 
> # Look at modification indices:
> mod1 %>% MIs

Top 10 modification indices:

 var1 op     var2 est   mi    pmi   epc        matrix row col      group
  Q10 ~~       Q4   0 9.28 0.0023 -0.21 sigma_epsilon  10   4 fullsample
   Q5 ~~       Q3   0 5.12  0.024  0.16 sigma_epsilon   5   3 fullsample
   Q9 ~= Original   0 3.81  0.051 -0.28        lambda   9   2 fullsample
   Q5 ~= Prequels   0 3.30  0.069  0.21        lambda   5   1 fullsample
   Q8 ~~       Q2   0 3.25  0.071 -0.15 sigma_epsilon   8   2 fullsample
   Q5 ~=  Sequels   0 3.01  0.083  0.30        lambda   5   3 fullsample
  Q10 ~~       Q8   0 2.67   0.10 -0.53 sigma_epsilon  10   8 fullsample
   Q9 ~~       Q1   0 2.56   0.11 -0.11 sigma_epsilon   9   1 fullsample
  Q10 ~~       Q3   0 2.53   0.11 -0.11 sigma_epsilon  10   3 fullsample
   Q7 ~~       Q2   0 2.51   0.11 -0.12 sigma_epsilon   7   2 fullsample
 group_id
        1
        1
        1
        1
        1
        1
        1
        1
        1
        1
> 
> # Add and refit:
> mod2 <- mod1 %>% freepar("sigma_epsilon","Q10","Q4") %>% runmodel
> 
> # Compare:
> compare(original = mod1, adjusted = mod2)
    model DF     AIC     BIC RMSEA Chisq Chisq_diff DF_diff p_value
 adjusted 29 8315.19 8444.87   ~ 0 25.07                           
 original 30 8322.70 8448.78 0.024 34.58       9.51       1  0.0020

Note: Chi-square difference test assumes models are nested.> 
> # Fit measures:
> mod2 %>% fit
           Measure    Value
              logl -4121.60
 unrestricted.logl -4109.06
     baseline.logl -4322.24
              nvar       10
              nobs       65
              npar       36
                df       29
         objective    12.04
             chisq    25.07
            pvalue     0.67
    baseline.chisq   426.35
     baseline.npar       20
       baseline.df       45
   baseline.pvalue      ~ 0
               nfi     0.94
              pnfi     0.61
               tli     1.02
              nnfi     1.02
               rfi     0.91
               ifi     1.01
               rni     1.01
               cfi        1
             rmsea      ~ 0
    rmsea.ci.lower      ~ 0
    rmsea.ci.upper    0.038
      rmsea.pvalue     0.99
            aic.ll  8315.19
           aic.ll2  8326.58
             aic.x   -32.93
            aic.x2    97.07
               bic  8444.87
              bic2  8330.73
           ebic.25  8527.76
            ebic.5  8610.66
           ebic.75  8676.97
             ebic1  8776.44
> 
> ### Path diagrams ###
> # semPlot is not (yet) supported by default, but can be used as follows:
> # Load packages:
> library("semPlot")
> 
> # Estimates:
> lambdaEst <- getmatrix(mod2, "lambda")
> psiEst <- getmatrix(mod2, "sigma_zeta")
> thetaEst <- getmatrix(mod2, "sigma_epsilon")
> 
> # LISREL Model: LY = Lambda (lambda-y), TE = Theta (theta-epsilon), PS = Psi
> mod <- lisrelModel(LY =  lambdaEst, PS = psiEst, TE = thetaEst)
> 
> # Plot with semPlot:
> semPaths(mod, "std", "est", as.expression = "nodes")
> 
> 
> # We can make this nicer (set whatLabels = "none" to hide labels):
> semPaths(mod,
+ 
+ # this argument controls what the color of edges represent. In this case, 
+ # standardized parameters:
+     what = "std", 
+     
+ # This argument controls what the edge labels represent. In this case, parameter 
+ # estimates:
+     whatLabels = "est", 
+     
+ # This argument draws the node and edge labels as mathematical exprssions:    
+     as.expression = "nodes", 
+   
+ # This will plot residuals as arrows, closer to what we use in class:
+     style = "lisrel",
+     
+ # This makes the residuals larger:
+     residScale = 10, 
+     
+ # qgraph colorblind friendly theme:
+     theme = "colorblind",
+     
+ # tree layout options are "tree", "tree2", and "tree3":
+     layout = "tree2", 
+ 
+ # This makes the latent covariances connect at a cardinal center point:
+     cardinal = "lat cov",
+ 
+ # Changes curve into rounded straight lines:
+     curvePivot = TRUE, 
+     
+ # Size of manifest variables:
+     sizeMan = 4, 
+     
+ # Size of latent varibales:
+     sizeLat = 10, 
+     
+ # Size of edge labels:
+     edge.label.cex = 1,
+     
+ # Sets the margins:
+     mar = c(9,1,8,1), 
+     
+ # Prevents re-ordering of ovbserved variables:
+     reorder = FALSE, 
+     
+ # Width of the plot:
+     width = 8, 
+     
+ # Height of plot:
+     height = 5, 
+ 
+ # Colors according to latents:
+     groups = "latents",
+     
+ # Pastel colors:
+     pastel = TRUE, 
+     
+ # Disable borders:
+     borders = FALSE 
+     )
>     
> # Use arguments filetype = "pdf" and filename = "semPlotExample1" to store PDF
> 
> ### Latent Network Modeling ###
> 
> # Latent network model:
> lnm <- lvm(StarWars, lambda = Lambda, vars = obsvars,
+            latents = latents, identification = "variance",
+            latent = "ggm")
> 
> # Run model:
> lnm <- lnm %>% runmodel
> 
> # Look at parameters:
> lnm %>% parameters

 Parameters for group fullsample
	-  nu  
 var1 op var2  est    se        p row col par
   Q1 ~1      2.76 0.088 < 0.0001   1   1   1
   Q2 ~1      4.19 0.069 < 0.0001   2   1   2
   Q3 ~1      2.82 0.068 < 0.0001   3   1   3
   Q4 ~1      3.21 0.074 < 0.0001   4   1   4
   Q5 ~1      2.44 0.073 < 0.0001   5   1   5
   Q6 ~1      2.69 0.073 < 0.0001   6   1   6
   Q7 ~1      3.00 0.074 < 0.0001   7   1   7
   Q8 ~1      2.38 0.084 < 0.0001   8   1   8
   Q9 ~1      3.25 0.063 < 0.0001   9   1   9
  Q10 ~1      2.12 0.062 < 0.0001  10   1  10

	-  lambda  
 var1 op     var2     est    se        p row col par
   Q1 ~= Prequels    0.18  0.13     0.17   1   1  11
   Q2 ~= Prequels    0.25 0.089   0.0043   2   1  12
   Q3 ~= Prequels    0.54  0.13 < 0.0001   3   1  13
   Q4 ~= Prequels   -0.42  0.11 < 0.0001   4   1  14
   Q1 ~= Original    0.70  0.13 < 0.0001   1   2  15
   Q5 ~= Original    0.39 0.071 < 0.0001   5   2  16
   Q6 ~= Original    0.65 0.099 < 0.0001   6   2  17
   Q7 ~= Original    0.46 0.077 < 0.0001   7   2  18
   Q1 ~=  Sequels -0.0034  0.16     0.98   1   3  19
   Q8 ~=  Sequels    0.53  0.11 < 0.0001   8   3  20
   Q9 ~=  Sequels   0.091 0.049    0.067   9   3  21
  Q10 ~=  Sequels    0.39 0.082 < 0.0001  10   3  22

	-  omega_zeta (symmetric) 
     var1 op     var2   est    se        p row col par
 Original -- Prequels -0.11  0.19     0.56   2   1  23
  Sequels -- Prequels  0.43  0.17    0.011   3   1  24
  Sequels -- Original  0.70 0.097 < 0.0001   3   2  25

	-  delta_zeta (diagonal) 
     var1  op     var2 est se p row col par
 Prequels ~/~ Prequels   1        1   1   0
 Original ~/~ Original   1        2   2   0
  Sequels ~/~  Sequels   1        3   3   0

	-  sigma_epsilon (symmetric) 
 var1 op var2  est    se        p row col par
   Q1 ~~   Q1 0.89  0.13 < 0.0001   1   1  26
   Q2 ~~   Q2 1.19  0.11 < 0.0001   2   2  27
   Q3 ~~   Q3 0.86  0.15 < 0.0001   3   3  28
   Q4 ~~   Q4 1.25  0.14 < 0.0001   4   4  29
   Q5 ~~   Q5 1.10  0.10 < 0.0001   5   5  30
   Q6 ~~   Q6 0.57 0.093 < 0.0001   6   6  31
   Q7 ~~   Q7 1.05  0.10 < 0.0001   7   7  32
   Q8 ~~   Q8 1.20  0.15 < 0.0001   8   8  33
   Q9 ~~   Q9 1.05 0.091 < 0.0001   9   9  34
  Q10 ~~  Q10 0.65 0.083 < 0.0001  10  10  35
> 
> # Remove non-sig latent edge:
> lnm <- lnm %>% prune(alpha = 0.05)
> 
> # Compare to the original CFA model:
> compare(cfa = mod1, lnm = lnm)
 model DF     AIC     BIC RMSEA Chisq Chisq_diff DF_diff p_value
   cfa 30 8322.70 8448.78 0.024 34.58                           
   lnm 31 8321.02 8443.49 0.022 34.89       0.31       1    0.58

Note: Chi-square difference test assumes models are nested.> 
> # Plot network:
> library("qgraph")
> qgraph(lnm@modelmatrices[[1]]$omega_zeta, labels = latents,
+        theme = "colorblind", vsize = 10)
> 
> # A wrapper for the latent network model is the lnm function:
> lnm2 <- lnm(StarWars, lambda = Lambda, vars = obsvars,
+             latents = latents, identification = "variance")
> lnm2 <- lnm2 %>% runmodel %>% prune(alpha = 0.05)
> compare(lnm, lnm2) # Is the same as the model before.
   model DF     AIC     BIC RMSEA Chisq Chisq_diff DF_diff p_value
 Model 1 31 8321.02 8443.49 0.022 34.89                           
 Model 2 31 8321.02 8443.49 0.022 34.89        ~ 0       0       1

Note: Chi-square difference test assumes models are nested.> 
> # I could also estimate a "residual network model", which adds partial correlations to 
> # the residual level:
> # This can be done using lvm(..., residal = "ggm") or with rnm(...)
> rnm <- rnm(StarWars, lambda = Lambda, vars = obsvars,
+            latents = latents, identification = "variance")
> # Stepup search:
> rnm <- rnm %>% stepup
> 
> # It will estimate the same model (with link Q10 - Q4) as above. In the case of only one 
> # partial correlation, There is no difference between residual covariances (SEM) or 
> # residual partial correlations (RNM).
> 
> 
> # For more information on latent and residual network models, see:
> # Epskamp, S., Rhemtulla, M.T., & Borsboom, D. Generalized Network Psychometrics: 
> # Combining Network and Latent Variable Models 
> # (2017). Psychometrika. doi:10.1007/s11336-017-9557-x
> 
> ### Gaussian graphical models ###
> 
> # All psychonetrics functions (e.g., lvm, lnm, rnm...) allow input via a covariance 
> # matrix, with the "covs" and "nobs" arguments.
> # The following fits a baseline GGM network with no edges:
> S <- (nrow(StarWars) - 1)/ (nrow(StarWars)) * cov(StarWars[,1:10])
> ggmmod <- ggm(covs = S, nobs = nrow(StarWars))
Assuming denominator n was used in covariance computation (covtype = 'ML').
> 
> # Run model with stepup search and pruning:
> ggmmod <- ggmmod%>% prune  %>% modelsearch
> 
> # Fit measures:
> ggmmod %>% fit
           Measure    Value
              logl -4131.43
 unrestricted.logl -4109.06
     baseline.logl -4322.24
              nvar       10
              nobs       55
              npar       23
                df       32
         objective    12.11
             chisq    44.74
            pvalue    0.067
    baseline.chisq   426.35
     baseline.npar       10
       baseline.df       45
   baseline.pvalue      ~ 0
               nfi     0.90
              pnfi     0.64
               tli     0.95
              nnfi     0.95
               rfi     0.85
               ifi     0.97
               rni     0.97
               cfi     0.97
             rmsea    0.038
    rmsea.ci.lower      ~ 0
    rmsea.ci.upper    0.063
      rmsea.pvalue     0.76
            aic.ll  8308.87
           aic.ll2  8313.34
             aic.x   -19.26
            aic.x2    90.74
               bic  8391.72
              bic2  8318.79
           ebic.25  8444.68
            ebic.5  8497.63
           ebic.75  8540.00
             ebic1  8603.55
> 
> # Plot network:
> nodeNames <- c(
+ "I am a huge Star Wars\nfan! (star what?)",
+ "I would trust this person\nwith my democracy.",
+ "I enjoyed the story of\nAnakin's early life.",
+ "The special effects in\nthis scene are awful (Battle of\nGeonosis).",
+ "I would trust this person\nwith my life.",
+ "I found Darth Vader's big\nreveal in 'Empire' one of the greatest
+ moments in movie history.",
+ "The special effects in\nthis scene are amazing (Death Star\nExplosion).",
+ "If possible, I would\ndefinitely buy this\ndroid.",
+ "The story in the Star\nWars sequels is an improvement to\nthe previous movies.",
+ "The special effects in\nthis scene are marvellous (Starkiller\nBase Firing)."
+ )
> library("qgraph")
> qgraph(as.matrix(ggmmod@modelmatrices[[1]]$omega), nodeNames = nodeNames, 
+     legend.cex = 0.25,  theme = "colorblind", layout = "spring")
> 
> # We can actually compare this model statistically (note they are not nested) to the 
> # latent variable model:
> compare(original_cfa = mod1, adjusted_cfa = mod2, exploratory_ggm = ggmmod)
           model DF     AIC     BIC RMSEA Chisq Chisq_diff DF_diff p_value
    adjusted_cfa 29 8315.19 8444.87   ~ 0 25.07                           
    original_cfa 30 8322.70 8448.78 0.024 34.58       9.51       1  0.0020
 exploratory_ggm 32 8308.87 8391.72 0.038 44.74      10.16       2  0.0062

Note: Chi-square difference test assumes models are nested.> 
> 
> ### Meausrement invariance ###
> # Let's say we are interested in seeing if people >= 30 like the original trilogy better 
> # than people < 30.
> # First we can make a grouping variable:
> StarWars$agegroup <- ifelse(StarWars$Q12 < 30, "young", "less young")
> 
> # Let's look at the distribution:
> table(StarWars$agegroup) # Pretty even...

less young      young 
       118        153 
> 
> # Observed variables:
> obsvars <- paste0("Q",1:10)
> 
> # Let's look at the mean scores:
> StarWars %>% group_by(agegroup) %>% summarize_each_(funs(mean),vars = obsvars)
Error:
! `summarise_each_()` was deprecated in dplyr 0.7.0 and is now defunct.
ℹ Please use `across()` instead.
Backtrace:
    ▆
 1. ├─StarWars %>% group_by(agegroup) %>% ...
 2. └─dplyr::summarize_each_(., funs(mean), vars = obsvars)
 3.   └─dplyr:::each_defunct("summarise_each_()")
 4.     └─lifecycle::deprecate_stop(when = "0.7.0", what = fun, with = "across()")
 5.       └─lifecycle:::deprecate_stop0(msg)
 6.         └─rlang::cnd_signal(...)
Execution halted
* checking PDF version of manual ... OK
* checking for non-standard things in the check directory ... OK
* checking for detritus in the temp directory ... OK
* checking for new files in some other directories ... OK
* DONE

Status: 1 ERROR
See
  ‘/data/blackswan/ripley/R/packages/tests-devel/psychonetrics.Rcheck/00check.log’
for details.

Command exited with non-zero status 1
Time 13:22.04, 915.78 + 94.88