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manydist

Distance-Based Learning for Mixed-Type Data

manydist provides tools for constructing, computing, and using distance measures for numerical, categorical, and mixed-type data.

Installation

# install.packages("palmerpenguins")
install.packages("manydist")

You can also install the development version from GitHub:

# install.packages("remotes")
remotes::install_github("alfonsoIodiceDE/manydist_package", subdir = "manydist")

Initialization

library(manydist)
library(palmerpenguins)
library(dplyr)
library(tidyr)

Getting started

The main function in manydist is mdist(). It computes dissimilarities for numerical, categorical, and mixed-type data.

penguins_small <- penguins |>
  select(-year) |> drop_na()

A mixed-type dissimilarity can be computed with a preset. For example, preset = "gower" computes a Gower-type dissimilarity.

D <- penguins_small |> select(-species) |> 
  mdist(preset = "gower")

as.matrix(D$distance)[1:5,1:5] |> round(digits=2)

##      1    2    3    4    5
## 1 0.00 0.21 0.25 0.24 0.07
## 2 0.21 0.00 0.07 0.09 0.25
## 3 0.25 0.07 0.00 0.06 0.26
## 4 0.24 0.09 0.06 0.00 0.23
## 5 0.07 0.25 0.26 0.23 0.00

Some presets can use a response variable. When a response is supplied, it is used for response-aware distance construction and is not treated as an ordinary predictor.

D_resp <- penguins_small |> mdist(
  response = species,
  preset = "u_dep"
)

D_resp

## MDist object
##   preset : u_dep 
##   number of observations : 333 
##   number of continuous variables   : 4 
##   number of categorical variables   : 2 
##   parameters:
##     - categorical method: tvd
##     - numerical preprocessing: pc_scores
##     - commensurability adjustment: TRUE

Custom specifications can be defined by combining a categorical method, a numerical preprocessing method, and a commensurability rule.

D_custom <- mdist(penguins_small |> select(-species),
                  preset = "custom",
                  method_cat = "matching",
                  method_num = "std",
                  commensurable = TRUE
)

D_custom

## MDist object
##   preset : custom 
##   number of observations : 333 
##   number of continuous variables   : 4 
##   number of categorical variables   : 2 
##   parameters:
##     - categorical method: matching
##     - numerical preprocessing: std
##     - commensurability adjustment: TRUE
##     - number of principal components: 6
##     - inertia threshold: NULL

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.