Apply JuliaFormatter (#3)

.JuliaFormatter.toml

@@ -0,0 +1,2 @@
+indent = 2
+format_docstrings = true

01-single_tte.jl

@@ -12,146 +12,142 @@ tte_single = dataset("tte_single")
 
 # Pumas Modeling
 pop_single = read_pumas(
-  tte_single;
-  observations=[:DV],
-  covariates=[:DOSE],
-  id=:ID,
-  time=:TIME,
-  amt=:AMT,
-  evid=:EVID
+ tte_single;
+ observations = [:DV],
+ covariates = [:DOSE],
+ id = :ID,
+ time = :TIME,
+ amt = :AMT,
+ evid = :EVID,
 )
 
 # Exponential
 tte_single_exp_model = @model begin
-  @param begin
-  λ₁ ∈ RealDomain(; lower=0, init=0.001) # basal hazard
-  β ∈ RealDomain(; init=0.001) # fixed effect DOSE
-  ω ∈ RealDomain(; lower=0) # inter-subject variability
-  end
-
-  @random begin
-  η ~ Normal(0.0, ω)
-  end
-
-  @covariates DOSE
-
-  @pre begin
-  _λ₁ = λ₁ * exp(η) # basal hazard with inter-subject variability
-  _λ₀ = _λ₁ * exp(β * DOSE) # total hazard
-  end
-
-  @vars begin
-  # exponential
-  λ = _λ₀
-  end
-
-  @dynamics begin
-  # the derivative of cumulative hazard is equal to hazard
-  Λ' = λ
-  end
-
-  @derived begin
-  DV ~ @. TimeToEvent(λ, Λ) # special type of distribution
-  end
+ @param begin
+ λ₁ ∈ RealDomain(; lower = 0, init = 0.001) # basal hazard
+ β ∈ RealDomain(; init = 0.001) # fixed effect DOSE
+ ω ∈ RealDomain(; lower = 0) # inter-subject variability
+ end
+
+ @random begin
+ η ~ Normal(0.0, ω)
+ end
+
+ @covariates DOSE
+
+ @pre begin
+ _λ₁ = λ₁ * exp(η) # basal hazard with inter-subject variability
+ _λ₀ = _λ₁ * exp(β * DOSE) # total hazard
+ end
+
+ @vars begin
+ # exponential
+ λ = _λ₀
+ end
+
+ @dynamics begin
+ # the derivative of cumulative hazard is equal to hazard
+ Λ' = λ
+ end
+
+ @derived begin
+ DV ~ @. TimeToEvent(λ, Λ) # special type of distribution
+ end
 end
 
-tte_single_exp_fit = fit(
- tte_single_exp_model,
- pop_single,
- init_params(tte_single_exp_model),
- LaplaceI(),
-)
+tte_single_exp_fit =
+ fit(tte_single_exp_model, pop_single, init_params(tte_single_exp_model), LaplaceI())
 
 # Weibull
 tte_single_weibull_model = @model begin
-  @param begin
-  λ₁ ∈ RealDomain(; lower=0, init=0.001) # basal hazard
-  β ∈ RealDomain(; init=0.001) # fixed effect DOSE
-  ω ∈ RealDomain(; lower=0) # inter-subject variability
-  Κ ∈ RealDomain(; lower=0, init=0.001) # Weibull shape
-  end
-
-  @random begin
-  η ~ Normal(0.0, ω)
-  end
-
-  @covariates DOSE
-
-  @pre begin
-  _Κ = Κ # shape parameter
-  _λ₁ = λ₁ * exp(η) # basal hazard with inter-subject variability
-  _λ₀ = _λ₁ * exp(β * DOSE) # total hazard
-  end
-
-  @vars begin
-  # Weibull
-  # 1e-10 for model numerical stability
-  λ = _λ₀ * _Κ * (_λ₀ * t + 1e-10)^(_Κ - 1)
-  end
-
-  @dynamics begin
-  # the derivative of cumulative hazard is equal to hazard
-  Λ' = λ
-  end
-
-  @derived begin
-  DV ~ @. TimeToEvent(λ, Λ) # special type of distribution
-  end
+ @param begin
+ λ₁ ∈ RealDomain(; lower = 0, init = 0.001) # basal hazard
+ β ∈ RealDomain(; init = 0.001) # fixed effect DOSE
+ ω ∈ RealDomain(; lower = 0) # inter-subject variability
+ Κ ∈ RealDomain(; lower = 0, init = 0.001) # Weibull shape
+ end
+
+ @random begin
+ η ~ Normal(0.0, ω)
+ end
+
+ @covariates DOSE
+
+ @pre begin
+ _Κ = Κ # shape parameter
+ _λ₁ = λ₁ * exp(η) # basal hazard with inter-subject variability
+ _λ₀ = _λ₁ * exp(β * DOSE) # total hazard
+ end
+
+ @vars begin
+ # Weibull
+ # 1e-10 for model numerical stability
+ λ = _λ₀ * _Κ * (_λ₀ * t + 1e-10)^(_Κ - 1)
+ end
+
+ @dynamics begin
+ # the derivative of cumulative hazard is equal to hazard
+ Λ' = λ
+ end
+
+ @derived begin
+ DV ~ @. TimeToEvent(λ, Λ) # special type of distribution
+ end
 end
 
 tte_single_weibull_fit = fit(
-  tte_single_weibull_model,
-  pop_single,
-  init_params(tte_single_weibull_model),
-  LaplaceI(),
+ tte_single_weibull_model,
+ pop_single,
+ init_params(tte_single_weibull_model),
+ LaplaceI(),
 )
 
 # Gompertz
 tte_single_gompertz_model = @model begin
-  @param begin
-  λ₁ ∈ RealDomain(; lower=0, init=0.001) # basal hazard
-  β ∈ RealDomain(; init=0.001) # fixed effect DOSE
-  ω ∈ RealDomain(; lower=0) # inter-subject variability
-  Κ ∈ RealDomain(; lower=0, init=0.001) # Gompertz shape
-  end
-
-  @random begin
-  η ~ Normal(0.0, ω)
-  end
-
-  @covariates DOSE
-
-  @pre begin
-  _Κ = Κ # shape parameter
-  _λ₁ = λ₁ * exp(η) # basal hazard with inter-subject variability
-  _λ₀ = _λ₁ * exp(β * DOSE) # total hazard
-  end
-
-  @vars begin
-  # Gompertz
-  λ = _λ₀ * exp(_Κ * t)
-  end
-
-  @dynamics begin
-  # the derivative of cumulative hazard is equal to hazard
-  Λ' = λ
-  end
-
-  @derived begin
-  DV ~ @. TimeToEvent(λ, Λ) # special type of distribution
-  end
+ @param begin
+ λ₁ ∈ RealDomain(; lower = 0, init = 0.001) # basal hazard
+ β ∈ RealDomain(; init = 0.001) # fixed effect DOSE
+ ω ∈ RealDomain(; lower = 0) # inter-subject variability
+ Κ ∈ RealDomain(; lower = 0, init = 0.001) # Gompertz shape
+ end
+
+ @random begin
+ η ~ Normal(0.0, ω)
+ end
+
+ @covariates DOSE
+
+ @pre begin
+ _Κ = Κ # shape parameter
+ _λ₁ = λ₁ * exp(η) # basal hazard with inter-subject variability
+ _λ₀ = _λ₁ * exp(β * DOSE) # total hazard
+ end
+
+ @vars begin
+ # Gompertz
+ λ = _λ₀ * exp(_Κ * t)
+ end
+
+ @dynamics begin
+ # the derivative of cumulative hazard is equal to hazard
+ Λ' = λ
+ end
+
+ @derived begin
+ DV ~ @. TimeToEvent(λ, Λ) # special type of distribution
+ end
 end
 
 tte_single_gompertz_fit = fit(
-  tte_single_gompertz_model,
-  pop_single,
-  init_params(tte_single_gompertz_model),
-  LaplaceI(),
+ tte_single_gompertz_model,
+ pop_single,
+ init_params(tte_single_gompertz_model),
+ LaplaceI(),
 )
 
 # compare MLE estimates
 compare_estimates(;
-  Exponential=tte_single_exp_fit,
-  Weibull=tte_single_weibull_fit,
-  Gompertz=tte_single_gompertz_fit
-)
+ Exponential = tte_single_exp_fit,
+ Weibull = tte_single_weibull_fit,
+ Gompertz = tte_single_gompertz_fit,
+)