Miscellaneous style and docs improvements

src/contexts.jl

@@ -188,7 +188,7 @@ getsampler(::IsParent, context::AbstractContext) = getsampler(childcontext(conte
 """
     struct DefaultContext <: AbstractContext end
 
-The `DefaultContext` is used by default to compute log the joint probability of the data 
+The `DefaultContext` is used by default to compute the log joint probability of the data
 and parameters when running the model.
 """
 struct DefaultContext <: AbstractContext end
@@ -199,7 +199,7 @@ NodeTrait(context::DefaultContext) = IsLeaf()
         vars::Tvars
     end
 
-The `PriorContext` enables the computation of the log prior of the parameters `vars` when 
+The `PriorContext` enables the computation of the log prior of the parameters `vars` when
 running the model.
 """
 struct PriorContext{Tvars} <: AbstractContext
@@ -213,8 +213,8 @@ NodeTrait(context::PriorContext) = IsLeaf()
         vars::Tvars
     end
 
-The `LikelihoodContext` enables the computation of the log likelihood of the parameters when 
-running the model. `vars` can be used to evaluate the log likelihood for specific values 
+The `LikelihoodContext` enables the computation of the log likelihood of the parameters when
+running the model. `vars` can be used to evaluate the log likelihood for specific values
 of the model's parameters. If `vars` is `nothing`, the parameter values inside the `VarInfo` will be used by default.
 """
 struct LikelihoodContext{Tvars} <: AbstractContext
@@ -229,10 +229,10 @@ NodeTrait(context::LikelihoodContext) = IsLeaf()
         loglike_scalar::T
     end
 
-The `MiniBatchContext` enables the computation of 
-`log(prior) + s * log(likelihood of a batch)` when running the model, where `s` is the 
-`loglike_scalar` field, typically equal to `the number of data points / batch size`. 
-This is useful in batch-based stochastic gradient descent algorithms to be optimizing 
+The `MiniBatchContext` enables the computation of
+`log(prior) + s * log(likelihood of a batch)` when running the model, where `s` is the
+`loglike_scalar` field, typically equal to `the number of data points / batch size`.
+This is useful in batch-based stochastic gradient descent algorithms to be optimizing
 `log(prior) + log(likelihood of all the data points)` in the expectation.
 """
 struct MiniBatchContext{Tctx,T} <: AbstractContext

src/model.jl

@@ -1,15 +1,17 @@
 """
-    struct Model{F,argnames,defaultnames,missings,Targs,Tdefaults}
+    struct Model{F,argnames,defaultnames,missings,Targs,Tdefaults,Ctx<:AbstactContext}
         f::F
         args::NamedTuple{argnames,Targs}
         defaults::NamedTuple{defaultnames,Tdefaults}
+        context::Ctx=DefaultContext()
     end
 
 A `Model` struct with model evaluation function of type `F`, arguments of names `argnames`
-types `Targs`, default arguments of names `defaultnames` with types `Tdefaults`, and missing
-arguments `missings`.
+types `Targs`, default arguments of names `defaultnames` with types `Tdefaults`, missing
+arguments `missings`, and evaluation context of type `Ctx`.
 
 Here `argnames`, `defaultargnames`, and `missings` are tuples of symbols, e.g. `(:a, :b)`.
+`context` is by default `DefaultContext()`.
 
 An argument with a type of `Missing` will be in `missings` by default. However, in
 non-traditional use-cases `missings` can be defined differently. All variables in `missings`
@@ -1077,7 +1079,7 @@ end
 Return an array of log joint probabilities evaluated at each sample in an MCMC `chain`.
 
 # Examples
-	
+
 ```jldoctest
 julia> using MCMCChains, Distributions
 
@@ -1093,7 +1095,7 @@ julia> # construct a chain of samples using MCMCChains
        chain = Chains(rand(10, 2, 3), [:s, :m]);
 
 julia> logjoint(demo_model([1., 2.]), chain);
-```   
+```
 """
 function logjoint(model::Model, chain::AbstractMCMC.AbstractChains)
     var_info = VarInfo(model) # extract variables info from the model
@@ -1124,7 +1126,7 @@ end
 Return an array of log prior probabilities evaluated at each sample in an MCMC `chain`.
 
 # Examples
-	
+
 ```jldoctest
 julia> using MCMCChains, Distributions
 
@@ -1140,7 +1142,7 @@ julia> # construct a chain of samples using MCMCChains
        chain = Chains(rand(10, 2, 3), [:s, :m]);
 
 julia> logprior(demo_model([1., 2.]), chain);
-```   
+```
 """
 function logprior(model::Model, chain::AbstractMCMC.AbstractChains)
     var_info = VarInfo(model) # extract variables info from the model
@@ -1171,7 +1173,7 @@ end
 Return an array of log likelihoods evaluated at each sample in an MCMC `chain`.
 
 # Examples
-	
+
 ```jldoctest
 julia> using MCMCChains, Distributions
 
@@ -1187,7 +1189,7 @@ julia> # construct a chain of samples using MCMCChains
        chain = Chains(rand(10, 2, 3), [:s, :m]);
 
 julia> loglikelihood(demo_model([1., 2.]), chain);
-```  
+```
 """
 function Distributions.loglikelihood(model::Model, chain::AbstractMCMC.AbstractChains)
     var_info = VarInfo(model) # extract variables info from the model

src/transforming.jl

@@ -1,3 +1,14 @@
+"""
+    struct DynamicTransformationContext{isinverse} <: AbstractContext
+
+When a model is evaluated with this context, transform the accompanying `AbstractVarInfo` to
+constrained space if `isinverse` or unconstrained if `!isinverse`.
+
+Note that some `AbstractVarInfo` types, must notably `VarInfo`, override the
+`DynamicTransformationContext` methods with more efficient implementations.
+`DynamicTransformationContext` is a fallback for when we need to evaluate the model to know
+how to do the transformation, used by e.g. `SimpleVarInfo`.
+"""
 struct DynamicTransformationContext{isinverse} <: AbstractContext end
 NodeTrait(::DynamicTransformationContext) = IsLeaf()