[R-sig-ME] Error using MCMCglmm

[Kamal Atmeh]

Dear list,

I am trying to run bayesian phylogenetic mixed models using MCMCglmm but 
I keep getting the following error:

    " Error in MCMCglmm(ltau ~ x * x2 + lbmM + age + lmean_ndvi + :
   no slot of name "i" for this object of class "ddiMatrix" "

This is not the first time I use MCMCglmm and it usually works 
flawlessly. I thought that there may be a conflict with the "tidyverse" 
package since some functions of "Matrix" are masked, but I tried to run 
the model without loading the "tidyverse" package and still received the 
same error. I was not able to find answers online and am thus turning to 
this list for answers if you can help please.

I am running the following model:

 >>>  prior1 <-list (G = list(G1 = list(V = 1, nu = 0.02)
                             ,G2 = list(V = 1, nu = 0.02)
                             ,G3 = list(V = 1, nu = 0.02)
                             ,G4 = list(V = 1, nu = 0.02)),
                      R = list(V = 1, nu = 0.02)
                      )

 >>> mod_tau_mc <- MCMCglmm(ltau ~ x * x2+    # x and x2 are categorical 
variables
                                  lbmM +      # continuous variable
                                  age +         # categorical
                                  lmean_ndvi +         # continuous
                                  lrange_ndvi +         # continuous
                                  lnb.loc +         # continuous
                                  lduration         # continuous
                        , random = ~sp_phylo+species2+phylo_pop+phylo_pop_id
                        , ginverse = list(sp_phylo = inv.phylo$Ainv)     
     # include a custom matrix for argument sp_phylo
                        , family = "gaussian"
                        , prior = prior1
                        , data = dt
                        , nitt = 22e+03         # number of iteration 
after burnin
                        , burnin = 2000         # number of iteration 
before beginning sample
                        , thin = 100         # nb of iteration between 
sample
                        , pr = TRUE)         #save random posterior 
distribution

I would greatly appreciate your help and happy to provide further 
information if needed!

Thank you in advance!

Kamal