The hardware and bandwidth for this mirror is donated by METANET, the Webhosting and Full Service-Cloud Provider.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]metanet.ch.

samesies

samesies compares lists of texts, factors, or numerical values to measure their similarity. The motivating use case is evaluating the similarity of large language model (LLM) responses across models, providers, or prompts—a strategy often referred to as LLM-as-a-judge .

Installation

You can install samesies from CRAN with:

install.packages("samesies")

Basic Usage

samesies provides three main functions for measuring similarity:

same_text()

Compare similarity between multiple lists of character strings.

library(samesies)

r1 <- list("R is a statistical computing software", 
           "R enables grammar of graphics using ggplot2", 
           "R supports advanced statistical models")
r2 <- list("R is a full-stack programming language",
           "R enables advanced data visualizations", 
           "R supports machine learning algorithms")

tex <- same_text(r1, r2)

Methods available via stringdist (e.g., method = "osa"):

• Transformational Algorithms

  • osa: Adjacent transposition edits
  • lv: Basic edit distance
  • dl: Comprehensive edit distance with transpositions

• Structural Comparison

  • hamming: Position-wise character differences
  • lcs: Longest shared subsequence
  • qgram: Subsequence matching
  • cosine: Vector-space string similarity
  • jaccard: Set-based string comparison

• Linguistic Matching

  • jw: Prefix-weighted string matching
  • soundex: Phonetic encoding

same_factor()

Compare similarity between multiple lists of categorical data.

cats1 <- list("R", "R", "Python")
cats2 <- list("R", "Python", "R")

fct <- same_factor(cats1, cats2, 
                   levels = c("R", "Python"))

Methods available (e.g., method = "exact"):

• exact: Exact matching
• order: Distances across ordered factor levels

same_number()

Compare similarity between multiple lists of numeric values.

n1 <- list(1, 2, 3)
n2 <- list(1, 2.1, 3.2)

num <- same_number(n1, n2)

Methods available (e.g., method = "exact"):

• exact: Exact matching
• raw: Absolute difference
• exp: Exponential decay on the absolute difference
• pct_diff: Percentage difference
• normalized: Normalized difference (max_diff is computed automatically by default)

num <- same_number(n1, n2, 
                   method = "normalized", 
                   max_diff = 2.2)

• fuzzy: Fuzzy matching with dual tolerance system:
  • Uses both relative and absolute tolerance thresholds
  • The fuzzy matching method calculates numeric similarity by using both a relative (default 2%) and absolute (default 0.05) tolerance
  • Values within the maximum of these two epsilon thresholds are considered exact matches (score of 1)
  • Similarity scores gradually decrease as the difference grows beyond the threshold

num <- same_number(n1, n2, 
                   method = "fuzzy", 
                   epsilon = 0.05,
                   epsilon_pct = 0.02)

More Lists

When you input more than two lists, compute pairwise comparisons across lists.

Nested Lists

Nested lists are supported as long as they share the same names and lengths.

Methods

All three functions return similar objects that support the following methods:

• print(x)
• summary(x)
• average_similarity(x, method = NULL)
• pair_averages(x, method = NULL)

Accessing Object Data

The package uses S3 objects, allowing access to the underlying data:

• $scores: A list of similarity scores for each method and comparison pair
• $summary: A list of statistical summaries for each method and comparison pair
• $methods: The similarity methods used in the analysis
• $list_names: Names of the input lists
• $raw_values: The original input values
• $digits: Number of decimal places for rounding results in output

Credits

The Spiderman image in the hex logo is fan art created by the Reddit user WistlerR15.

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.