Converting MATlab Loop to R - Cell Structures - r

Question

I am having trouble to convert a Matlab loop into R.
For every {t,tau}, calculate the o indexed with a value that refers to the value of [t,tau]
from within the loop. In the matlab code it is done via a cell structure that applies from outside the loop.
o{1,1} = [1 0]';
o{1,2} = [0 0]';
o{2,1} = [1 0]';
o{2,2} = [0 1]';
Within the loop something like this is calculated:
nat_log(A'*o{t,tau})
How can I manage the loop to use the values of t and tau that I set within the loop in order to adress certain "o" within a list?
First my attempt:
Timesteps = 2 # total timesteps/iterations
Tau = 2 # amounts of observations o within 1 timestep; at t=1 and tau-1 = o = [0 0], above denoted {1,2}
for(t in 1:length(Timesteps)){
qs[,t] = matrix(c(.5,.5)) # This is the posterior distribution to be updated,
for (t in 1:length(Timesteps)){ # but that's not that important
for (tau in 1:length(Timesteps)){
****some more math****
y = x * o[[t,tau]] # THIS is the important part!
etc. .... ..... .... ....
I tried something like
o<- vector("list", length = 2 * 2)
dim(o) <- matrix(c(2, 2))
o[[1,1]] <- matrix(c(1, 0), nrow = 2, ncol =1, byrow = TRUE)
o[[1,2]] <- matrix(c(0, 0), nrow = 2, ncol =1, byrow = TRUE)
o[[2,1]] <- matrix(c(1, 0), nrow = 2, ncol =1, byrow = TRUE)
o[[2,2]] <- matrix(c(0, 1), nrow = 2, ncol =1, byrow = TRUE)
The orginial matlab code that I am trying to reproduce can be found here:
https://github.com/rssmith33/Active-Inference-Tutorial-Scripts/blob/main/Pencil_and_paper_exercise_solutions.m
Line: 46-103 (it is essentially Bayes inference with Markov property...)
If someone has an idea, I would be very thankful. I am just a med student interested in math and would love to see this available for R as well.