implemetend in the package.
A lot of files has been modified because some of the internal syntax has
changed. But the top level methods should still work.

Two tests about the plots of synchronized oscillatory simulations have
been added.

All tests passed.

This commit groups the change operated to the creation of models and
simulate function of a ContinuousTimeModel.
The general idea is to create a concrete type and a simulate function
 per model creation by metaprogramming.
- Now, ContinuousTimeModel is an abstract type. Each creation of a model
defines a concrete type T <: ContinuousTimeModel by meta programming.
- f! and isabsorbing ContinuousTimeModel fields are Symbols.
- simulate(::ContinuousTimeModel) is run by multiple dispatch, according
to the type of the model.

Can't run the whole tests for now but unit/simulate_available_models.jl
runs properly (i've updated the list of models in this commit), and I've
manually checked in the repl that simulations run correctly (distributed
/ plots).

integrated poisson model wasn't updated
improvement of plots
fix of synchronized simulation when state is absorbing
All tests passed

now the package has a better structure for distributed simulations by better definition of functions over workers + test

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).

simulation tests for sir_tauleap and poisson.

call of isabsorbing() during the simulation.

Now the package is a bit more efficient than DiffEqJump.jl with the
Catalyst.jl interface which is encouraging, see bench/pkg/catalyst.jl.

to define CTMC with the biochemical network.

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

small.

Fix of a misbehaviour of isbounded() for trajectories.

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 a new type of Trajectory: SynchronizedTrajectory which is created
by the simulation of a synchronized model with an LHA.

Simulate function for SynchronizedModel executes and is tested by two
simple models.

Next step : Cosmos based tests.

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

Major fix of type unstabilities which improved performances.
For now on, we observe for pygmalion benchmarks:
+2x speed simulation compared to simulation of models.
100x speed in terms of reading values (no allocations).

Add of bench for @view macro.

can be set by the user. Changes of SIR and ER.

previously loaded model). The package warns when this happen.

Several benchmarks for the data structure. Now we chose the row buffer
method according to results (mainly because the trajectory access with
far way faster, but strangly not that much differences in other cases).

- a script to run the different categories
- now the execution of simulation/ scripts is checked
- several minor changes and add of tests

New directory : models/_bench_perf_tests.  Models for performance benchmarking purposes.

In fine I give the Abstract and Static arrays types up.

- Real => Float64 for better performance (Real is an abstract type)
- Should not create a type inside the model because the compiler fails
at estimating the type of the output of the f function (detected with
@code_warntype)
- Intentionaly ER is not as well improved as SIR in order to check the
differences of performance.

- 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.

Files should have the same name as the exported model variable (<:Model)