Cvtmle

.github/workflows/CI.yml

@@ -12,7 +12,6 @@ jobs:
         version:
           - '1.6'
           - '1'
-          - 'nightly'
         os:
           - ubuntu-latest
           - macOS-latest

.gitignore

@@ -6,4 +6,5 @@
 build/
 sandbox.jl
 /Manifest.toml
-/test/Manifest.toml
+/test/Manifest.toml
+/docs/Manifest.toml

Project.toml

@@ -1,45 +1,62 @@
 name = "TMLE"
 uuid = "8afdd2fb-6e73-43df-8b62-b1650cd9c8cf"
 authors = ["Olivier Labayle"]
-version = "0.11.4"
+version = "0.12.0"
 
 [deps]
 AbstractDifferentiation = "c29ec348-61ec-40c8-8164-b8c60e9d9f3d"
 CategoricalArrays = "324d7699-5711-5eae-9e2f-1d82baa6b597"
-Configurations = "5218b696-f38b-4ac9-8b61-a12ec717816d"
+Combinatorics = "861a8166-3701-5b0c-9a16-15d98fcdc6aa"
 Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"
 GLM = "38e38edf-8417-5370-95a0-9cbb8c7f171a"
+Graphs = "86223c79-3864-5bf0-83f7-82e725a168b6"
 HypothesisTests = "09f84164-cd44-5f33-b23f-e6b0d136a0d5"
+JSON = "682c06a0-de6a-54ab-a142-c8b1cf79cde6"
 LogExpFunctions = "2ab3a3ac-af41-5b50-aa03-7779005ae688"
 MLJBase = "a7f614a8-145f-11e9-1d2a-a57a1082229d"
 MLJGLMInterface = "caf8df21-4939-456d-ac9c-5fefbfb04c0c"
 MLJModels = "d491faf4-2d78-11e9-2867-c94bc002c0b7"
+MetaGraphsNext = "fa8bd995-216d-47f1-8a91-f3b68fbeb377"
 Missings = "e1d29d7a-bbdc-5cf2-9ac0-f12de2c33e28"
 PrecompileTools = "aea7be01-6a6a-4083-8856-8a6e6704d82a"
 PrettyTables = "08abe8d2-0d0c-5749-adfa-8a2ac140af0d"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
 TableOperations = "ab02a1b2-a7df-11e8-156e-fb1833f50b87"
 Tables = "bd369af6-aec1-5ad0-b16a-f7cc5008161c"
-YAML = "ddb6d928-2868-570f-bddf-ab3f9cf99eb6"
 Zygote = "e88e6eb3-aa80-5325-afca-941959d7151f"
 
+[weakdeps]
+JSON = "682c06a0-de6a-54ab-a142-c8b1cf79cde6"
+YAML = "ddb6d928-2868-570f-bddf-ab3f9cf99eb6"
+
+[extensions]
+JSONExt = "JSON"
+YAMLExt = "YAML"
+
 [compat]
-AbstractDifferentiation = "0.4, 0.5"
+AbstractDifferentiation = "0.6.0"
 CategoricalArrays = "0.10"
-Configurations = "0.17"
+Combinatorics = "1.0.2"
 Distributions = "0.25"
 GLM = "1.8.2"
-HypothesisTests = "0.10"
+Graphs = "1.8"
+HypothesisTests = "0.10, 0.11"
+JSON = "0.21.4"
 LogExpFunctions = "0.3"
-MLJBase = "0.19, 0.20, 0.21"
+MLJBase = "1.0.1"
 MLJGLMInterface = "0.3.4"
 MLJModels = "0.15, 0.16"
+MetaGraphsNext = "0.7"
 Missings = "1.0"
+PrecompileTools = "1.1.1"
 PrettyTables = "2.2"
 TableOperations = "1.2"
 Tables = "1.6"
-YAML = "0.4"
+YAML = "0.4.9"
 Zygote = "0.6"
-PrecompileTools = "1.1.1"
 julia = "1.6, 1.7, 1"
+
+[extras]
+JSON = "682c06a0-de6a-54ab-a142-c8b1cf79cde6"
+YAML = "ddb6d928-2868-570f-bddf-ab3f9cf99eb6"

README.md

@@ -5,7 +5,6 @@
 ![Codecov](https://img.shields.io/codecov/c/github/TARGENE/TMLE.jl/main)
 ![GitHub release (latest SemVer)](https://img.shields.io/github/v/release/TARGENE/TMLE.jl)
 
-Targeted Minimum Loss-Based Estimation (TMLE) is a framework for efficient estimation of pathwise differentiable parameters. The goal of this package is to provide an implementation on top of the [MLJ](https://alan-turing-institute.github.io/MLJ.jl/dev/) framework.
+This package enables the estimation of various Causal Inference related estimands using Targeted Minimum Loss-Based Estimation (TMLE). TMLE.jl is based on top of [MLJ](https://alan-turing-institute.github.io/MLJ.jl/dev/), which means any MLJ compliant machine-learning model can be used here.
 
-
-**New to TMLE.jl?** The API design is still work in progress but you can already [get started now](https://targene.github.io/TMLE.jl/stable/)
+**New to TMLE.jl?** [Get started now](https://targene.github.io/TMLE.jl/stable/)