API Refactor about lifecontingencies.jl HOT 2 CLOSED

mt = UltimateMortality([0.5,0.5])

# whole life insurance
ins = Insurance(
        SingleLife(mort = mt,issue_age = 0),
        Yields.Constant(0.05)
) 

@test survival(ins) == [1.0,0.5]
@test discount(ins) == [1.0 / 1.05, 1 / 1.05^2]
@test benefit(ins) == [1.0,1.0]
@test probability(ins) == [0.5,0.25]
@test cashflows(ins) == [0.5,0.25]
@test cashflows(ins) == benefit(ins) .* probability(ins)
@test timepoints(ins) == [1.0,2.0]
@test present_value(ins)  ≈ 0.5 / 1.05 + 0.5 * 0.5 / 1.05 ^ 2

ins = LifeContingency(
    SingleLife(
        mort = mt,
        issue_age = 0
    ),
    Yields.Constant(0.05)
)

@test insurance(ins) ≈ 0.5 / 1.05 + 0.5 * 0.5 / 1.05 ^ 2

julia> @benchmark present_value(Insurance($ins   ))		#new
BenchmarkTools.Trial: 
  memory estimate:  111.30 KiB
  allocs estimate:  1579
  --------------
  minimum time:     81.200 μs (0.00% GC)
  median time:      83.301 μs (0.00% GC)
  mean time:        92.890 μs (6.73% GC)
  maximum time:     3.461 ms (96.72% GC)
  --------------
  samples:          10000
  evals/sample:     1

julia> @benchmark insurance($ins   )              		#old
BenchmarkTools.Trial: 
  memory estimate:  83.88 KiB
  allocs estimate:  740
  --------------
  minimum time:     35.000 μs (0.00% GC)
  median time:      48.000 μs (0.00% GC)
  mean time:        48.172 μs (6.57% GC)
  maximum time:     2.160 ms (96.04% GC)
  --------------
  samples:          10000**