Merge branch 'release/v0.21.0'

Author: DilumAluthge

CONTRIBUTING.md

@@ -222,10 +222,11 @@ your GPG set-up is working.
 Release tags MUST be signed with your GPG key.**
 
 #### Step 1:
-Open a terminal window and `cd` to the directory containing the PredictMD source code:
+Open a terminal window and `cd` to the directory
+containing the PredictMD source code:
 
 ```bash
-cd ~/.julia/v0.6/PredictMD
+cd ~/.julia/dev/PredictMD
 ```
 
 #### Step 2:

Project.toml

@@ -1,10 +1,7 @@
 name = "PredictMD"
 uuid = "3e7d7328-36f8-4388-bd01-4613c92c7370"
-authors = ["Dilum Aluthge <dilum@aluthge.com>"]
-version = "0.20.2"
-
-[compat]
-julia = "^0.7.0"
+authors = ["Dilum Aluthge <dilum@aluthge.com>", "Ishan Sinha <ishan_sinha@brown.edu>", "Paul Stey <paulstey@gmail.com>", "Isabel Restrepo <isabelrestre@gmail.com>", "Elizabeth Chen <liz_chen@brown.edu>", "Neil Sarkar <neil_sarkar@brown.edu>", "Brown Center for Biomedical Informatics <bcbi@brown.edu>"]
+version = "0.21.0"
 
 [deps]
 Adapt = "79e6a3ab-5dfb-504d-930d-738a2a938a0e"
@@ -17,7 +14,6 @@ CUDAapi = "3895d2a7-ec45-59b8-82bb-cfc6a382f9b3"
 Cassette = "7057c7e9-c182-5462-911a-8362d720325c"
 ClassImbalance = "04a18a73-7590-580c-b363-eeca0919eb2a"
 Combinatorics = "861a8166-3701-5b0c-9a16-15d98fcdc6aa"
-Compat = "34da2185-b29b-5c13-b0c7-acf172513d20"
 DataDeps = "124859b0-ceae-595e-8997-d05f6a7a8dfe"
 DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
 DecisionTree = "7806a523-6efd-50cb-b5f6-3fa6f1930dbb"
@@ -67,6 +63,10 @@ ValueHistories = "98cad3c8-aec3-5f06-8e41-884608649ab7"
 WebSockets = "104b5d7c-a370-577a-8038-80a2059c5097"
 Zygote = "e88e6eb3-aa80-5325-afca-941959d7151f"
 
+[compat]
+julia = "^0.7.0"
+Documenter = "~0.19"
+
 [extras]
 Adapt = "79e6a3ab-5dfb-504d-930d-738a2a938a0e"
 Atom = "c52e3926-4ff0-5f6e-af25-54175e0327b1"
@@ -78,7 +78,6 @@ CUDAapi = "3895d2a7-ec45-59b8-82bb-cfc6a382f9b3"
 Cassette = "7057c7e9-c182-5462-911a-8362d720325c"
 ClassImbalance = "04a18a73-7590-580c-b363-eeca0919eb2a"
 Combinatorics = "861a8166-3701-5b0c-9a16-15d98fcdc6aa"
-Compat = "34da2185-b29b-5c13-b0c7-acf172513d20"
 DataDeps = "124859b0-ceae-595e-8997-d05f6a7a8dfe"
 DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
 DecisionTree = "7806a523-6efd-50cb-b5f6-3fa6f1930dbb"
@@ -131,66 +130,4 @@ WebSockets = "104b5d7c-a370-577a-8038-80a2059c5097"
 Zygote = "e88e6eb3-aa80-5325-afca-941959d7151f"
 
 [targets]
-test = [
-    "Adapt",
-    "Atom",
-    "AutoGrad",
-    "BSON",
-    "CSV",
-    "CSVFiles",
-    "CUDAapi",
-    "Cassette",
-    "ClassImbalance",
-    "Combinatorics",
-    "Compat",
-    "DataDeps",
-    "DataFrames",
-    "DecisionTree",
-    "DefaultApplication",
-    "Documenter",
-    "FileIO",
-    "Flux",
-    "GLM",
-    "GPUArrays",
-    "GZip",
-    "HTTP",
-    "HTTPClient",
-    "HttpServer",
-    "InteractiveUtils",
-    "IterableTables",
-    "JLD2",
-    "Knet",
-    "LIBSVM",
-    "LaTeXStrings",
-    "LinearAlgebra",
-    "Literate",
-    "MLBase",
-    "MNIST",
-    "MbedTLS",
-    "Metalhead",
-    "Mux",
-    "NNlib",
-    "NumericalIntegration",
-    "OnlineStats",
-    "PGFPlotsX",
-    "PackageCompiler",
-    "Pkg",
-    "ProgressMeter",
-    "Query",
-    "RData",
-    "RDatasets",
-    "ROCAnalysis",
-    "Random",
-    "Requires",
-    "Revise",
-    "Statistics",
-    "StatsBase",
-    "StatsModels",
-    "Test",
-    "TikzPictures",
-    "TimeSeries",
-    "UnicodePlots",
-    "ValueHistories",
-    "WebSockets",
-    "Zygote",
-]
+test = ["Adapt", "Atom", "AutoGrad", "BSON", "CSV", "CSVFiles", "CUDAapi", "Cassette", "ClassImbalance", "Combinatorics", "DataDeps", "DataFrames", "DecisionTree", "DefaultApplication", "Documenter", "FileIO", "Flux", "GLM", "GPUArrays", "GZip", "HTTP", "HTTPClient", "HttpServer", "InteractiveUtils", "IterableTables", "JLD2", "Knet", "LIBSVM", "LaTeXStrings", "LinearAlgebra", "Literate", "MLBase", "MNIST", "MbedTLS", "Metalhead", "Mux", "NNlib", "NumericalIntegration", "OnlineStats", "PGFPlotsX", "PackageCompiler", "Pkg", "ProgressMeter", "Query", "RData", "RDatasets", "ROCAnalysis", "Random", "Requires", "Revise", "Statistics", "StatsBase", "StatsModels", "Test", "TikzPictures", "TimeSeries", "UnicodePlots", "ValueHistories", "WebSockets", "Zygote"]

README.md

@@ -8,18 +8,29 @@
 
 [PredictMD](https://www.predictmd.net) is a free and open-source Julia package that provides a uniform interface for machine learning.
 
-### Table of Contents
-- [Installation](#installation)
-- [Documentation](#documentation)
-- [Citing](#citing)
-- [Contributing](#contributing)
-- [Stronghold](#stronghold)
-- [Repository Status](#repository-status)
-- [CI/CD](#cicd)
+To install PredictMD, open Julia
+(e.g. open a terminal, type `julia`, and press enter)
+and run the following command:
+```julia
+Pkg.clone("https://github.com/bcbi/PredictMD.jl")
+```
+
+Please submit any issues related to PredictMD to the issue tracker: [https://github.com/bcbi/PredictMD.jl/issues](https://github.com/bcbi/PredictMD.jl/issues)
+
+| Table of Contents |
+| ----------------- |
+| [1. Installation](#installation) |
+| [2. Documentation](#documentation) |
+| [3. Citing](#citing) |
+| [4. Related Repositories](#related-repositories) |
+| [5. Contributing](#contributing) |
+| [6. Stronghold](#stronghold) |
+| [7. Repository Status](#repository-status) |
+| [8. CI/CD](#cicd) |
 
 ## Installation
 
-PredictMD requires Julia version 0.6.
+PredictMD requires Julia version 0.7.
 
 To install PredictMD, open Julia
 (e.g. open a terminal, type `julia`, and press enter)
@@ -46,6 +57,12 @@ PredictMD's internals.
 
 If you use PredictMD in research, please cite the software using the following DOI: <a href="https://zenodo.org/badge/latestdoi/109460252"> <img src="https://zenodo.org/badge/109460252.svg"/></a>
 
+## Related Repositories
+
+- [bcbi/PredictMDFull.jl](https://github.com/bcbi/PredictMDFull.jl) - Install PredictMD and all of its Julia dependencies
+- [bcbi/PredictMDExtra.jl](https://github.com/bcbi/PredictMDExtra.jl) - Install all of the Julia dependencies of PredictMD (but does not install PredictMD)
+- [bcbi/predictmd-conda-recipes](https://github.com/bcbi/predictmd-conda-recipes) - Conda recipes for building the binary dependencies that are required for plotting in PredictMD
+
 ## Contributing
 
 If you would like to contribute to the PredictMD source code, please read the instructions in [CONTRIBUTING.md](CONTRIBUTING.md).

ready.yml

@@ -0,0 +1,17 @@
+maintainers:
+    - name: Dilum Aluthge
+      github_user: DilumAluthge
+
+docs_url:         https://www.predictmd.net/stable
+
+doi:              "10.5281/zenodo.1291209"
+
+repo:             PredictMD.jl
+
+repo_url:         https://github.com/bcbi/PredictMD.jl
+
+site_description: Uniform interface for machine learning in Julia
+
+site_name:        PredictMD.jl
+
+type:             software

templates/examples/cpu/boston_housing/01_preprocess_data.jl

@@ -51,12 +51,6 @@ import StatsBase
 
 Random.seed!(999)
 
-# df = CSV.read(
-#     ,
-#     DataFrames.DataFrame;
-#     rows_for_type_detect = 100,
-#     )
-
 df = DataFrames.DataFrame(
     CSVFiles.load(
         CSVFiles.Stream(