All automata were readapted and are in accordance with the last description of the thesis. Tests passed

Replaced access of fields in models with getfield. Improvement of distribute_mean_lha_value: number of accepts are computed simultaneously.3

models. It's a better semantic and improve performance as well as
readability of the code. All the tests passes (except the remark in the
last commit).

variableautomaton. It improves performance.

Tests of Cosmos doesn't pass on the bounds (with a parameter equal to
zero). It's a problem of design (there are issues for cosmos too).

of automata: it wasn't an issue about the simulation code of LHA but an
issue of LHA designs.

Improvement of a Cosmos utils method.

Now A.constants is a NamedTuple.

distributed_mean_value_lha now takes several variables.

Fix about behavior of automata when the initial state is absorbing or
the parameters are equal to zero.

Add of supplementary unit tests.

The two Cosmos tests checks the statistical correctness of estimated
distance value for R1,R2,R3,R5 experiments. Also, they test that all
trajectories are accepted.

A bunch of errors were fixed in automata files (Cosmos .lha and julia
./automata files) and simulation function that introduced statistical
bias, i.e. approximated values that were closed but not in the
confidence interval. The general structure of simulation and was
reworked in order to suits well te behavior of Cosmos.

copyto! is implemented for LHA states.

Automaton G works almost well, a bias in the distance is identified
compared to Cosmos when one of the model parameters is zero.

Implementation of volatile_simulate, which simulates SynchronizedModel
without saving values: it only returns the last LHA state of the
simulation.

Implementation of distributed computations of variable automata with
volatile_simulate.

Tests passed.

Automaton G is implemented and runs OK. Not tested with Cosmos call.

The next step of development is the restruction of data collection for
trajectories.

From now on: Trajectory.values::Matrix{Float64}, it will be
Trajectory.values::Vector{Vector{Float64}}.

According to small performance tests I've made, it will increase the
performance of the package.

Add of set_time_bound!
Add of a first notebook example to explain the package.

Add of a major test based on Cosmos: tests/cosmos/distance_F/ER_1D.jl

The LHA distance F of this package is tested with Cosmos.
For several parameters in R1, R2, R3 experiments (see Bentriou,
Ballarini, Cournede 2018), Cosmos is called to
compute an average distance. Then we collect the number of simulations
and the estimated value computed by Cosmos.
We compute an average distance by the package methods with the same
number of simulations done by Cosmos

Then the two average distances are compared to each others with
regard to the width of the confidence interval set in Cosmos.
Test passed.