depmixS4 - How to fit HMM usgin multiple sequences of observation
No, depmixS4 supports multiple external variables to be included to forecast underlying time series.   In this case transition matrix is a function of all the other external variables. It is given in depmix S4 vignette also.-

a useful material for start -
https://machinelearningstories.blogspot.com/2017/02/hidden-markov-model-session-1.html &amp;
http://machinelearningstories.blogspot.in/2017_03_01_archive.html

R Code snippets- 

Required library

library(depmixS4)

data loading-

physician_prescrition_data &lt;-c(12,16,45,45,56,67,78,98,120,124,156)

model execution-

HMM_model &lt;- depmixS4::depmix(physician_prescrition_data~1, nstates = 2,ntimes=length(physician_prescrition_data))

model fitting

HMM_fm &lt;- fit(HMM_model)

Transition probabilties-

HMM_fm@transition

posterior states-

posterior(HMM_fm)
plot(ts(posterior(HMM_fm)[,1]))

Emission probabilties-

HMM_fm@response