rank_candidates: Rank candidates based on voter preferences

In fastVoteR: Efficient Voting Methods for Committee Selection

Rank candidates based on voter preferences

Description

Calculates a ranking of candidates based on voters' preferences. Approval-Based Committe (ABC) rules are based on Lackner et al. (2023). For an example use in Machine Learning for ensemble feature selection, see Zobolas et al. (2026).

Usage

rank_candidates(
  voters,
  candidates,
  weights = NULL,
  committee_size = NULL,
  method = "av",
  borda_score = TRUE,
  shuffle_candidates = TRUE,
  check = FALSE
)

Arguments

voters	(list()) A list of subsets (character vectors), where each subset contains the candidates approved or selected by a voter.
candidates	(character()) A vector of all candidates to be ranked.
weights	(numeric()|NULL) A numeric vector of non-negative weights representing each voter's influence. Larger weight, higher influence. Must have the same length as voters. If NULL (default), all voters are assigned equal weights of 1, representing equal influence.
committee_size	(integer(1)|NULL) Number of top-ranked candidates to return. Default (NULL) returns all candidates.
method	(character(1)) The ranking voting method to use. Must be one of: "av", "sav", "seq_pav", "seq_phragmen". See Details.
borda_score	(logical(1)) Whether to include a borda_score column in the output, which provides a normalized score based on the candidate's rank. If TRUE (default), the borda_score is calculated as (p - i) / (p - 1), where p is the total number of candidates and i is the candidate's rank.
shuffle_candidates	(logical(1)) Whether to randomly shuffle candidates before ranking. This provides random tie-breaking and avoids deterministic bias when scores are equal. Default is TRUE.
check	(logical(1)) Whether to run additional voter-integrity checks. When TRUE, each voter must approve at least one candidate, approvals must be unique per voter, and all approved candidates must appear in candidates. Use FALSE to skip these checks when inputs are known to be valid.

Details

We implement several consensus-based ranking methods, where voters express preferences for candidates. The framework has three components:

• Voters: A list where each element is the set of approved candidates for a single voter.

• Candidates: A character vector of all possible candidates. This vector can be shuffled to randomize tie-breaking across methods.

• Weights: A numeric vector giving each voter’s influence. Equal weights mean equal influence; differing weights reflect varying importance.

This function is a thin wrapper that dispatches to method-specific implementations. Supported methods include:

1. Approval Voting (method = "av"), calls av()

2. Satisfaction Approval Voting (method = "sav"), calls sav()

3. Sequential Proportional Approval Voting (method = "seq_pav"), calls seq_pav()

4. Sequential Phragmen’s Rule (method = "seq_phragmen"), calls seq_phragmen()

All methods support voter weights.

For method-agnostic comparisons, we compute borda scores, which map each candidate’s rank to a normalized scale across methods that may otherwise return different score scales or only ordinal rankings.

For sequential methods such as "seq_pav" and "seq_phragmen", the committee_size parameter can speed up computation by selecting only the top N candidates instead of producing a full ranking. For non-sequential methods (e.g., "sav" or "av"), it simply truncates the final ranking to the top N candidates.

Value

A data.frame with columns:

• "candidate": Candidate names.

• "score": Scores assigned to each candidate based on the selected method (if applicable).

• "norm_score": Normalized scores (if applicable), scaled to the range [0,1], which can be loosely interpreted as selection probabilities (see Meinshausen et al. (2010) for an example in Machine Learning research where the goal is to perform stable feature selection).

• "borda_score": Borda scores for method-agnostic comparison, ranging in [0,1], where the top candidate receives a score of 1 and the lowest-ranked candidate receives a score of 0.

Candidates are ordered by decreasing "score", or by "borda_score" if the method returns only rankings.

References

Lackner M, Skowron P (2023). Multi-Winner Voting with Approval Preferences. Springer Nature, 121 p. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1007/978-3-031-09016-5")}.

Zobolas J, George A, Lopez A, Fischer S, Becker M, Aittokallio T (2026). "Prognostic biomarker discovery in pancreatic cancer through hybrid ensemble feature selection and multi-omics data." BioData Mining. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1186/s13040-026-00546-0")}.

Meinshausen N, Buhlmann P (2010). "Stability Selection." Journal of the Royal Statistical Society Series B: Statistical Methodology, 72(4), 417-473. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1111/J.1467-9868.2010.00740.X")}.

Examples

# 5 candidates
candidates = paste0("V", seq_len(5))

# 4 voters
voters = list(
  c("V3", "V1", "V4"),
  c("V3", "V1"),
  c("V3", "V2"),
  c("V2", "V4")
)

# voter weights
weights = c(1.1, 2.5, 0.8, 0.9)

# Approval voting (all voters equal)
rank_candidates(voters, candidates)

# Approval voting (voters unequal)
rank_candidates(voters, candidates, weights)

# Satisfaction Approval voting (voters unequal, no borda score)
rank_candidates(voters, candidates, weights, method = "sav", borda_score = FALSE)

# Sequential Proportional Approval voting (voters equal, no borda score)
rank_candidates(voters, candidates, method = "seq_pav", borda_score = FALSE)

# Sequential Phragmen's Rule (voters equal)
rank_candidates(voters, candidates, method = "seq_phragmen", borda_score = FALSE)

fastVoteR documentation built on May 16, 2026, 1:08 a.m.