Merge branch 'nessie' of https://github.com/JuliaSurv/NetSurvival.jl …

…into nessie

src/Nessie.jl

@@ -3,7 +3,7 @@ struct Nessie
     expected_life_time::Float64
     grid::Vector{Float64}
     function Nessie(T, Δ, age, year, rate_preds, ratetable)
-        annual_grid = 0:365.241:maximum(T)
+        annual_grid = 0:RateTables.RT_DAYS_IN_YEAR:maximum(T)
         exp_spl_size = zeros(length(annual_grid))
         exp_life_time = 0.0
         for i in eachindex(age)
@@ -13,7 +13,7 @@ struct Nessie
             end
             exp_life_time += expectation(Lᵢ)
         end
-        return new(exp_spl_size, exp_life_time / 365.241 / length(age), annual_grid)
+        return new(exp_spl_size, exp_life_time / RateTables.RT_DAYS_IN_YEAR / length(age), annual_grid)
     end
 end
 
@@ -38,51 +38,6 @@ function nessie(args...)
     return lt, r
 end
 
-
+# Maybe not necessary ? No need to clutter the interface too much.. 
 expected_life_time(x::Nessie) = x.expected_life_time
 expected_sample_size(x::Nessie) = x.expected_sample_size
-
-
-
-
-# function old_Nessie(formula::FormulaTerm, df::DataFrame, rt::RateTables.AbstractRateTable)
-#     formula_applied = apply_schema(formula,schema(df))
-#     rate_predictors = String.([RateTables.predictors(rt)...])
-
-#     nms = StatsModels.termnames(formula_applied.rhs)
-#     if isa(nms, String)
-#         pred_names = [nms]
-#     else
-#         pred_names = nms
-#     end
-
-#     times = sort(unique(floor.(df.time ./ 365.241)))
-#     times = unique([0.0; times])
-
-#     times_d = times .* 365.241
-
-#     new_df = groupby(df, pred_names)
-#     povp = zeros(nrow(unique(df[!,pred_names])))
-#     sit = zeros(length(times))
-#     num_pop = zeros(nrow(unique(df[!,pred_names])), length(times))
-
-#     for i in 1:nrow(unique(df[!,pred_names]))
-#         for j in 1:nrow(new_df[i])
-#             Tᵢ = searchsortedlast(times_d, new_df[i].time[j])
-#             rate_preds = select(new_df[i],rate_predictors)
-#             rtᵢ = rt[rate_preds[j,:]...]
-#             Λₚ = 0.0
-
-#             for m in 1:Tᵢ
-#                 λₚ          = daily_hazard(rtᵢ, new_df[i].age[j] + times_d[m], new_df[i].year[j] + times_d[m])
-#                 ∂Λₚ         = λₚ * 365.241
-#                 Λₚ         += ∂Λₚ
-#                 Sₚ          = exp(-Λₚ)
-#                 num_pop[i,m] += Sₚ 
-#                 sit[m]     += (1-Sₚ) / λₚ
-#             end
-#         end  
-#         povp[i] = mean(sit ./ 365.241)     
-#     end
-#     return num_pop, povp
-# end