_pkgdown.yml

url: https://blasbenito.github.io/collinear/

template:
  bootstrap: 5
  fonts:
    heading: Montserrat
    body: Montserrat
  includes:
    in_header: |
      <link rel="stylesheet" href="extra.css">


authors:
  Blas M. Benito:
    href: https://blasbenito.com

articles:

- title: "Tutorials"
  navbar: "Tutorials"
  contents:
  - articles/how_it_works

- title: "Blog Posts"
  navbar: "Blog Posts"
  contents:
  - multicollinearity_blog_post
  - vif_blog_post
  - target_encoding_blog_post

external-articles:

  - name: multicollinearity_blog_post
    title: "Multicollinearity Hinders Model Interpretability"
    description: "This post delves into the intricacies of model interpretation under the influence of multicollinearity, and use R and a toy data set to demonstrate how this phenomenon impacts both linear and machine learning models."
    href: https://www.blasbenito.com/post/multicollinearity-model-interpretability/

  - name: vif_blog_post
    title: "Everything About Variance Inflation Factors"
    description: "This post dives deep Variance Inflation Factors (VIF) and their crucial role in identifying multicollinearity within linear models. It uses R code extensively to illustrate various concepts, making it a practical guide for those well-versed in R."
    href: https://www.blasbenito.com/post/variance-inflation-factor/

  - name: target_encoding_blog_post
    title: "Mapping Categorical Predictors to Numeric"
    description: "This post focuses on the complexities of dealing with categorical predictors using methods such as one-hot encoding (please don’t) or target encoding, and provides insights into its mechanisms and quirks."
    href: https://www.blasbenito.com/post/target-encoding/


reference:

- title: Automated Multicollinearity Management
  desc: Tools to automatically select sets of variables with a low multicollinearity.
- contents:
  - has_concept("automated_multicollinearity_analysis")

- title: Variance Inflation Factors
  desc: Functions implementing VIF-based methods for multicollinearity filtering.
- contents:
  - has_concept("vif")

- title: Pairwise Correlation
  desc: Functions implementing pairwise correlation-based methods for multicollinearity filtering.
- contents:
  - has_concept("pairwise_correlation")

- title: Target Encoding
  desc: Tools to transform categorical variables into numeric.
- contents:
  - has_concept("target_encoding")
  - has_concept("target_encoding_tools")

- title: Preference Order
  desc: Rank predictors by their association to a response to preserve important ones during multicollinearity filtering.
- contents:
  - has_concept("preference_order")
  - has_concept("preference_order_tools")
  - has_concept("preference_order_functions")

- title: Modelling Tools
  desc: Tools to evaluate models, weight cases, and generate model formulas.
- contents:
  - has_concept("modelling_tools")

- title: Example Data
  desc: Real and synthetic datasets used throughout the package examples.
- contents:
  - has_concept("example_data")

- title: Data Preparation
  desc: Internal functions for data preparation and validation.
- contents:
  - has_concept("data_validation")
  - has_concept("data_types")
  - has_concept("data_preparation")