Error in check_pars(allpars, pars)

Modeling
,
1

I found the “footBayes” package for R and decided to run the code from the manual to see how it works: https://cran.r-project.org/web/packages/footBayes/footBayes.pdf

if(requireNamespace("engsoccerdata")){
  require(engsoccerdata)
  require(tidyverse)
  require(dplyr)
  ### Use Italian Serie A from 2000 to 2002
  italy <- as_tibble(italy)
  italy_2000_2002<- italy %>%
    dplyr::select(Season, home, visitor, hgoal,vgoal) %>%
    dplyr::filter(Season=="2000" | Season=="2001"| Season=="2002")
  ### Fit Stan models
  ## no dynamics, no predictions
  fit1 <- stan_foot(data = italy_2000_2002,
                    model="double_pois") # double poisson
  print(fit1, pars =c("home", "sigma_att",
                      "sigma_def"))
  fit2 <- stan_foot(data = italy_2000_2002,
                    model="biv_pois") # bivariate poisson
  print(fit2, pars =c("home", "rho",
                      "sigma_att", "sigma_def"))
  fit3 <- stan_foot(data = italy_2000_2002,
                    model="skellam") # skellam
  print(fit3, pars =c("home", "sigma_att",
                      "sigma_def"))
  fit4 <- stan_foot(data = italy_2000_2002,
                    model="student_t") # student_t
  print(fit4, pars =c("home", "beta"))
  ## seasonal dynamics, no prediction
  fit5 <- stan_foot(data = italy_2000_2002,
                    model="double_pois",
                    dynamic_type ="seasonal") # double poisson
  print(fit5, pars =c("home", "Sigma_att",
                      "Sigma_def"))
  ## seasonal dynamics, prediction for the last season
  fit6 <- stan_foot(data = italy_2000_2002,
                    model="double_pois",
                    dynamic_type ="seasonal",
                    predict = 306) # double poisson
  print(fit6, pars =c("home", "Sigma_att",
                      "Sigma_def"))
  ## other priors' options
  fit_p <- stan_foot(data = italy_2000_2002,
                     model="double_pois",
                     priors = student_t (4, 0, NULL),
                     prior_sd = laplace(0,1)) # double poisson with
  # student_t priors for teams abilities
  # and laplace prior for the hyper sds
  print(fit_p, pars = c("home", "sigma_att",
                        "sigma_def"))
}

But as a result I encountered the error:

SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 4).
Chain 4: 
Chain 4: Gradient evaluation took 4.9e-05 seconds
Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.49 seconds.
Chain 4: Adjust your expectations accordingly!
Chain 4: 
Chain 4: 
Chain 4: Iteration:    1 / 2000 [  0%]  (Warmup)
Chain 4: Iteration:  200 / 2000 [ 10%]  (Warmup)
Chain 4: Iteration:  400 / 2000 [ 20%]  (Warmup)
Chain 4: Iteration:  600 / 2000 [ 30%]  (Warmup)
Chain 4: Iteration:  800 / 2000 [ 40%]  (Warmup)
Chain 4: Iteration: 1000 / 2000 [ 50%]  (Warmup)
Chain 4: Iteration: 1001 / 2000 [ 50%]  (Sampling)
Chain 4: Iteration: 1200 / 2000 [ 60%]  (Sampling)
Chain 4: Iteration: 1400 / 2000 [ 70%]  (Sampling)
Chain 4: Iteration: 1600 / 2000 [ 80%]  (Sampling)
Chain 4: Iteration: 1800 / 2000 [ 90%]  (Sampling)
Chain 4: Iteration: 2000 / 2000 [100%]  (Sampling)
Chain 4: 
Chain 4:  Elapsed Time: 25.842 seconds (Warm-up)
Chain 4:                24.925 seconds (Sampling)
Chain 4:                50.767 seconds (Total)
Chain 4: 
Error in check_pars(allpars, pars) : no parameter home
In addition: Warning messages:
1: The largest R-hat is 2.29, indicating chains have not mixed.
Running the chains for more iterations may help. See
https://mc-stan.org/misc/warnings.html#r-hat 
2: Bulk Effective Samples Size (ESS) is too low, indicating posterior means and medians may be unreliable.
Running the chains for more iterations may help. See
https://mc-stan.org/misc/warnings.html#bulk-ess 
3: Tail Effective Samples Size (ESS) is too low, indicating posterior variances and tail quantiles may be unreliable.
Running the chains for more iterations may help. See
https://mc-stan.org/misc/warnings.html#tail-ess 
4: There were 78 divergent transitions after warmup. See
https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
to find out why this is a problem and how to eliminate them. 
5: There were 4 chains where the estimated Bayesian Fraction of Missing Information was low. See
https://mc-stan.org/misc/warnings.html#bfmi-low 
6: Examine the pairs() plot to diagnose sampling problems
 
7: The largest R-hat is 1.28, indicating chains have not mixed.
Running the chains for more iterations may help. See
https://mc-stan.org/misc/warnings.html#r-hat 
8: Bulk Effective Samples Size (ESS) is too low, indicating posterior means and medians may be unreliable.
Running the chains for more iterations may help. See
https://mc-stan.org/misc/warnings.html#bulk-ess 
9: Tail Effective Samples Size (ESS) is too low, indicating posterior variances and tail quantiles may be unreliable.
Running the chains for more iterations may help. See
https://mc-stan.org/misc/warnings.html#tail-ess

Please help!