Added docs annotations, related to #9

src/ABCalgorithm.jl

@@ -1,4 +1,8 @@
+"""
+  runabc(ABCsetup::ABCtype, targetdata; progress = false)
 
+Run the ABC algorithm specified by ABCsetup and return and ABCresults type containing posterior distribution samples. If progress is set to true, will print out a ProgressMeter bar.
+"""
 function runabc(ABCsetup::ABCRejection, targetdata; progress = false)
 
   #initalize array of particles
@@ -42,7 +46,6 @@ function runabc(ABCsetup::ABCRejection, targetdata; progress = false)
 
 end
 
-
 function runabc(ABCsetup::ABCRejectionModel, targetdata; progress = false)
 
   ABCsetup.nmodels > 1 || error("Only 1 model specified, use ABCRejection method to estimate parameters for a single model")
@@ -80,8 +83,6 @@ function runabc(ABCsetup::ABCRejectionModel, targetdata; progress = false)
         next!(p)
       end
     end
-
-
   end
 
   i > ABCsetup.Models[1].nparticles || error("Only accepted $(i-1) particles with ϵ < $(ABCsetup.Models[1].ϵ). \n\tDecrease ϵ or increase maxiterations ")
@@ -192,15 +193,10 @@ function runabc(ABCsetup::ABCSMC, targetdata; verbose = false, progress = false)
       flush(STDOUT)
       flush(STDERR)
     end
-
     popnum = popnum + 1
-
   end
 
-  out = ABCSMCresults(particles, numsims, ABCsetup, ϵvec)
-
-  return out
-
+  return ABCSMCresults(particles, numsims, ABCsetup, ϵvec)
 end
 
 
@@ -237,7 +233,6 @@ function runabc(ABCsetup::ABCSMCModel, targetdata; verbose = false, progress = f
   end
 
   popnum = 1
-
   finalpop = false
 
   if verbose == true
@@ -338,12 +333,14 @@ function runabc(ABCsetup::ABCSMCModel, targetdata; verbose = false, progress = f
 
   end
 
-  out = ABCSMCmodelresults(particles, numsims, ABCsetup, ϵvec)
-
-  return out
-
+  return ABCSMCmodelresults(particles, numsims, ABCsetup, ϵvec)
 end
 
+"""
+  runabcCancer(ABCsetup::ABCtype, targetdata; progress = false)
+
+Specific implementation of the runabc function to be applied with CancerSeqSim.jl. Only lets a sample be considered true if it returns a the correct clonal structure. If progress is set to true, will print out a ProgressMeter bar.
+"""
 function runabcCancer(ABCsetup::ABCSMCModel, targetdata; verbose = false, progress = false)
 
   ABCsetup.nmodels > 1 || error("Only 1 model specified, use ABCSMC method to estimate parameters for a single model")
@@ -487,14 +484,12 @@ function runabcCancer(ABCsetup::ABCSMCModel, targetdata; verbose = false, progre
     if verbose == true
       show(ABCSMCmodelresults(oldparticles, numsims, ABCsetup, ϵvec))
     end
-
   end
 
-  out = ABCSMCmodelresults(particles, numsims, ABCsetup, ϵvec)
+  return ABCSMCmodelresults(particles, numsims, ABCsetup, ϵvec)
+end
 
-  return out
 
-end
 
 function runabcCancer(ABCsetup::ABCSMC, targetdata; verbose = false, progress = false)
 

src/types.jl

@@ -45,6 +45,17 @@ type ParticleSMCModel <: Particle
 
 end
 
+"""
+  ABCrejection(sim_func::Function, nparams::Int64, ϵ::Float64, prior::Prior, <keyword arguments>)
+Construct an ABCrejection type with simulation function `sim_func`, `nparams` parameters, `ϵ` distance threshold and prior distribution over parameters.
+
+...
+## Arguments
+- `maxiterations = 10^3`: Maximum number of iterations before algorithm terminates
+- `constants = [1.0]`: Any constants required in `sim_func` function
+- `nparticles = 100`: Number of posterior samples
+...
+"""
 type ABCRejection <: ABCtype
 
   simfunc::Function
@@ -64,6 +75,21 @@ type ABCRejection <: ABCtype
 
 end
 
+"""
+  ABCSMC(sim_func::Function, nparams::Int64, ϵT::Float64, prior::Prior, <keyword arguments>)
+Construct an ABCSMC type with simulation function `sim_func`, `nparams` parameters, `ϵT` target distance threshold and `prior` distribution over parameters.
+
+...
+## Arguments
+- `maxiterations = 10^3`: Maximum number of iterations before algorithm terminates
+- `constants = [1.0]`: Any constants required in `sim_func` function
+- `nparticles = 100`: Number of posterior samples
+- `α = 0.3`: SMC adaptive parameter, next population takes αth quantile of previous population distance
+- `ϵ1 = 10^5`: ϵ of first population which is ABCrejection
+- `convergence = 0.05`: Algorithm terminates if distance decreases < convergence
+- `scalefactor = 2`: Parameter perturbation kernel scalefactor, larger numbers means perturbation are smaller
+...
+"""
 type ABCSMC <: ABCtype
 
   simfunc::Function
@@ -91,6 +117,16 @@ type ABCSMC <: ABCtype
 
 end
 
+"""
+  ABCrejectionModel(sim_func::Array{Function, 1}, nparams::Array{Int64, 1}, ϵ::Float64, prior::Array{Prior, 1}, <keyword arguments>)
+Construct an ABCrejection Model type with simulation functions `sim_func`, `nparams` parameters, `ϵT` target distance threshold and `prior` distributions over parameters.
+
+## Arguments
+- `maxiterations = 10^3`: Maximum number of iterations before algorithm terminates
+- `constants = [1.0]`: Any constants required in `sim_func` function
+- `nparticles = 100`: Number of posterior samples
+...
+"""
 type ABCRejectionModel <: ABCtype
 
   Models::Array{ABCRejection, 1}
@@ -105,6 +141,23 @@ type ABCRejectionModel <: ABCtype
 
 end
 
+
+"""
+  ABCSMCmodel(sim_func::Array{Function, 1}, nparams::Array{Int64, 1}, ϵT::Float64, prior::Array{Prior, 1}, <keyword arguments>)
+Construct an ABCSMCModel type with simulation functions `sim_func`, `nparams` parameters, `ϵT` target distance threshold and `prior` distributions over parameters. All arrays should be the same size.
+
+...
+## Arguments
+- `maxiterations = 10^3`: Maximum number of iterations before algorithm terminates
+- `constants = [1.0]`: Any constants required in `sim_func` function
+- `nparticles = 100`: Number of posterior samples
+- `α = 0.3`: SMC adaptive parameter, next population takes αth quantile of previous population distance
+- `modelkern = 0.7`: Probability model stays the same following perturbation
+- `ϵ1 = 10^5`: ϵ of first population which is ABCrejection
+- `convergence = 0.05`: Algorithm terminates if distance decreases < convergence
+- `scalefactor = 2`: Parameter perturbation kernel scalefactor, larger numbers means perturbation are smaller
+...
+"""
 type ABCSMCModel <: ABCtype
 
   Models::Array{ABCSMC, 1}