to define CTMC with the biochemical network.

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.

First bricks of the automaton_abc algorithm.

slight improvement in allocation and time in benchmark but too small.

Add several convenient methods for parameters of model.

Next step: implementation of simulate with resize!.

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 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.

Add of several useful functions for access values of model, lha and
trajectory objects.

Fix of a major issue in the simulation: sometimes the number of states
wasn't consistent. Add of test to check the consistency of the
Trajectory structure (unit/check_trajectory_consistency).

Implementation of the lp distance for trajectories.
Add of unit tests for lp distance.

- Add of benchmark scripts that compares perf wrt pygmalion.
=> As expected it is bad in terms of reading cost (because of row by row
matrix read) but it is also not good in terms of simulation cost.
hcat seems to perform badly, should investigate.

SIR model.

- Evolution of the ContinuousTimeModel type
- simulate, easy access to Trajectory fields
- fix of several errors

run the tests
Function load_module in core + test