Apply JuliaFormatter (#4)

.JuliaFormatter.toml

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

00-model_fits.jl

@@ -9,13 +9,13 @@ pkdata = dataset("nlme_sample")
 
 pop = read_pumas(
  pkdata;
- id=:ID,
- time=:TIME,
- amt=:AMT,
- observations=[:DV],
- cmt=:CMT,
- evid=:EVID,
- rate=:RATE
+ id = :ID,
+ time = :TIME,
+ amt = :AMT,
+ observations = [:DV],
+ cmt = :CMT,
+ evid = :EVID,
+ rate = :RATE,
 )
 
 # 1-compartment model
@@ -26,19 +26,27 @@ model_1cmt = @model begin
  end
 
  @param begin
- "Clearance (L/hr)"
- tvcl ∈ RealDomain(; lower=0)
- "Volume Central Compartment (L)"
- tvvc ∈ RealDomain(; lower=0)
  """
- - ΩCL
- - ΩVc
+ Clearance (L/hr)
+ """
+ tvcl ∈ RealDomain(; lower = 0)
+ """
+ Volume Central Compartment (L)
+ """
+ tvvc ∈ RealDomain(; lower = 0)
+ """
+ - ΩC
+ - ΩV
  """
  Ω ∈ PDiagDomain(2)
- "Additive RUV"
- σ_add ∈ RealDomain(; lower=0)
- "Proportional RUV"
- σ_prop ∈ RealDomain(; lower=0)
+ """
+ Additive RUV
+ """
+ σ_add ∈ RealDomain(; lower = 0)
+ """
+ Proportional RUV
+ """
+ σ_prop ∈ RealDomain(; lower = 0)
  end
 
  @random begin
@@ -61,13 +69,8 @@ model_1cmt = @model begin
  end
 end
 
-params_1cmt = (;
- tvvc=5,
- tvcl=0.2,
- Ω=Diagonal([0.01, 0.01]),
- σ_add=0.1,
- σ_prop=0.1
-)
+params_1cmt =
+ (; tvvc = 5, tvcl = 0.2, Ω = Diagonal([0.01, 0.01]), σ_add = 0.1, σ_prop = 0.1)
 
 fit_1cmt = fit(model_1cmt, pop, params_1cmt, FOCE())
 
@@ -79,23 +82,35 @@ model_2cmt = @model begin
  end
 
  @param begin
- "Clearance (L/hr)"
- tvcl ∈ RealDomain(; lower=0)
- "Volume Central Compartment (L)"
- tvvc ∈ RealDomain(; lower=0)
- "Intercompartmental Clearance (L/hr)"
- tvq ∈ RealDomain(; lower=0)
- "Volume Peripheral Compartment (L)"
- tvvp ∈ RealDomain(; lower=0)
- """
- - ΩCL
- - ΩVc
+ """
+ Clearance (L/hr)
+ """
+ tvcl ∈ RealDomain(; lower = 0)
+ """
+ Volume Central Compartment (L)
+ """
+ tvvc ∈ RealDomain(; lower = 0)
+ """
+ Intercompartmental Clearance (L/hr)
+ """
+ tvq ∈ RealDomain(; lower = 0)
+ """
+ Volume Peripheral Compartment (L)
+ """
+ tvvp ∈ RealDomain(; lower = 0)
+ """
+ - ΩC
+ - ΩV
  """
  Ω ∈ PDiagDomain(2)
- "Additive RUV"
- σ_add ∈ RealDomain(; lower=0)
- "Proportional RUV"
- σ_prop ∈ RealDomain(; lower=0)
+ """
+ Additive RUV
+ """
+ σ_add ∈ RealDomain(; lower = 0)
+ """
+ Proportional RUV
+ """
+ σ_prop ∈ RealDomain(; lower = 0)
  end
 
  @random begin
@@ -118,13 +133,13 @@ model_2cmt = @model begin
 end
 
 params_2cmt = (;
- tvvc=5,
- tvcl=0.02,
- tvq=0.01,
- tvvp=10,
- Ω=Diagonal([0.01, 0.01]),
- σ_add=0.1,
- σ_prop=0.1
+ tvvc = 5,
+ tvcl = 0.02,
+ tvq = 0.01,
+ tvvp = 10,
+ Ω = Diagonal([0.01, 0.01]),
+ σ_add = 0.1,
+ σ_prop = 0.1,
 )
 
 fit_2cmt = fit(model_2cmt, pop, params_2cmt, FOCE())

01-inspect.jl

@@ -12,11 +12,11 @@ inspect_1cmt = inspect(fit_1cmt)
 # You can also have NPDEs if you pass an extra optional keyword
 # argument nsim with the number of times each subject is simulated
 # for npde computation
-inspect_1cmt_npde = inspect(fit_1cmt; nsim=200)
+inspect_1cmt_npde = inspect(fit_1cmt; nsim = 200)
 
 # Let's also call the inspect function on our 2-compartment model
 inspect_2cmt = inspect(fit_2cmt)
-inspect_2cmt_npde = inspect(fit_2cmt; nsim=200)
+inspect_2cmt_npde = inspect(fit_2cmt; nsim = 200)
 
 # You can convert any inspect object to a NM-TRAN-formatted DataFrame
 inspect_1cmt_df = DataFrame(inspect_1cmt)

02-predict.jl

@@ -14,4 +14,4 @@ predict_1cmt = DataFrame(predict(model_1cmt, pop, coef(fit_1cmt)))
 # Suppose you want a more richer/denser pred/ipred time profile
 # You can do that with the keyword argument obstimes
 # it will "extend" the original observation profile to encompass the desired obstimes
-predict_1cmt_custom = DataFrame(predict(fit_1cmt; obstimes=168:172))
+predict_1cmt_custom = DataFrame(predict(fit_1cmt; obstimes = 168:172))

03-metrics.jl

@@ -17,16 +17,16 @@ bic(fit_1cmt)
 # It can compute a loglikelihood for any model given any population, parameter values, and estimation method
 # This is helpful for model conversions from other software/tools
 loglikelihood(
-  model_1cmt,
-  pop,
-  (; # NamedTuple of parameter values
-  tvcl=0.2,
-  tvvc=5,
-  Ω=Diagonal([0.1, 0.1]),
-  σ_add=0.01,
-  σ_prop=0.05
-  ),
-  FOCE(),
+ model_1cmt,
+ pop,
+ (; # NamedTuple of parameter values
+ tvcl = 0.2,
+ tvvc = 5,
+ Ω = Diagonal([0.1, 0.1]),
+ σ_add = 0.01,
+ σ_prop = 0.05,
+ ),
+ FOCE(),
 )
 
 # There are several plotting functions available in Pumas

04-vpc.jl

@@ -10,7 +10,7 @@ vpc_plot(vpc_1cmt)
 vpc_plot(vpc_2cmt)
 
 # You can also use a prediction-corrected VPC with the keyword argument prediction_correction
-vpc_1cmt_pc = vpc(fit_1cmt; prediction_correction=true)
-vpc_2cmt_pc = vpc(fit_2cmt; prediction_correction=true)
+vpc_1cmt_pc = vpc(fit_1cmt; prediction_correction = true)
+vpc_2cmt_pc = vpc(fit_2cmt; prediction_correction = true)
 vpc_plot(vpc_1cmt_pc)
 vpc_plot(vpc_2cmt_pc)