rm IIF dependency and examples from docs until DFG v1

docs/Project.toml

@@ -4,7 +4,6 @@ Compose = "a81c6b42-2e10-5240-aca2-a61377ecd94b"
 DistributedFactorGraphs = "b5cc3c7e-6572-11e9-2517-99fb8daf2f04"
 Documenter = "e30172f5-a6a5-5a46-863b-614d45cd2de4"
 GraphPlot = "a2cc645c-3eea-5389-862e-a155d0052231"
-IncrementalInference = "904591bb-b899-562f-9e6f-b8df64c7d480"
 
 [compat]
 Documenter = "1"

docs/src/BuildingGraphs.md

@@ -2,7 +2,7 @@
 
 In this section constructing DFG graphs will be discussed. To start, bring DistributedFactorGraphs into your workspace:
 
-```@example buildingGraphs; continued = true
+```julia
 using DistributedFactorGraphs
 ```
 
@@ -14,7 +14,7 @@ So for the following examples, IncrementalInference will be used to create the v
 using Pkg
 Pkg.add("IncrementalInference")
 ```
-```@example buildingGraphs; continued = true
+```julia
 using IncrementalInference
 ```
 
@@ -30,7 +30,7 @@ To continue the example, run one of the following to create a DFG driver:
 
 ### Creating a GraphsDFG Graph
 
-```@example buildingGraphs; continued = true
+```julia
 # Create a DFG with default solver parameters using the Graphs.jl driver.
 dfg = GraphsDFG{SolverParams}(solverParams=SolverParams())
 ```
@@ -54,7 +54,7 @@ In addition, the following optional parameters are provided:
 
 Three variables are added:
 
-```@example buildingGraphs; continued = true
+```julia
 v1 = addVariable!(dfg, :x0, ContinuousScalar, tags = [:POSE], solvable=1)
 v2 = addVariable!(dfg, :x1, ContinuousScalar, tags = [:POSE], solvable=1)
 v3 = addVariable!(dfg, :l0, ContinuousScalar, tags = [:LANDMARK], solvable=1)
@@ -73,7 +73,7 @@ Additionally, the solvable flag is also set to indicate that the factor can be u
 
 Four factors are added: a prior, a linear conditional relationship with a normal distribution between x0 and x1, and a pair of linear conditional relationships between each pose and the landmark.
 
-```@example buildingGraphs; continued = true
+```julia
 prior = addFactor!(dfg, [:x0], Prior(Normal(0,1)))
 f1 = addFactor!(dfg, [:x0; :x1], LinearRelative(Normal(50.0,2.0)), solvable=1)
 f1 = addFactor!(dfg, [:l0; :x0], LinearRelative(Normal(40.0,5.0)), solvable=1)
@@ -97,12 +97,12 @@ variable and factor labels can be retrieved with the [`ls`](@ref)/[`listVariable
 [`lsf`](@ref)/[`listFactors`](@ref) functions:
 
 For example listing the variables in the graph we created above:
-```@example buildingGraphs
+```julia
 ls(dfg)
 ```
 
 Or listing the factors:
-```@example buildingGraphs
+```julia
 lsf(dfg)
 ```
 

docs/src/DrawingGraphs.md

@@ -13,14 +13,14 @@ Pkg.add("GraphPlot")
 
 Then bring `GraphPlot` in before DFG:
 
-```@example plots; continued = true
+```julia
 using GraphPlot
 using DistributedFactorGraphs
 ```
 
 Any factor graph can then be drawn by calling [`plotDFG`](@ref):
 
-```@example plots
+```julia
 using Cairo # hide
 # Construct graph using IIF
 using IncrementalInference
@@ -42,7 +42,7 @@ plotDFG(dfg)
 
 The graph can be rendered to PDF, SVG or JPG in the following way by including compose:
 
-```@example plots
+```julia
 using Compose
 # lets add another variable and factor and plot it
 dfg.solverParams.graphinit = false # hide
@@ -52,7 +52,7 @@ addFactor!(dfg, [:x1; :x2], LinearRelative(Normal(50.0,2.0)));
 draw(SVG("graph.svg", 10cm, 10cm), plotDFG(dfg));
 nothing # hide
 ```
-![](graph.svg)
+FIXME show graph.svg
 
 
 ### More Information
@@ -67,7 +67,7 @@ and can be drawn by either:
 - Calling [`toDot`](@ref) on any graph to produce a string of the graph
 - Calling [`toDotFile`](@ref) on any graph to save it directly to a dotfile
 
-```@example
+```julia
 using DistributedFactorGraphs
 # Construct graph using IIF
 using IncrementalInference