sondage

Fast survey sampling algorithms for R. All functions return indices for easy subsetting.

Installation

# From GitLab
remotes::install_gitlab("dickoa/sondage")

Usage

library(sondage)

# Use built-in US state data
data(state)
states <- as.data.frame(state.x77)

# Simple random sample: 10 states
idx <- srs(10, nrow(states))
states[idx, ]

# Unequal probability sampling: probability proportional to population
pik <- inclusion_prob(states$Population, n = 10)
idx <- up_maxent(pik)
states[idx, ]

# Batch sampling for simulations (returns n × nrep matrix)
sim_cps <- up_maxent(pik, nrep = 10000)

Functions

Equal probability:

• srs() - Simple random sampling
• systematic() - Systematic sampling
• bernoulli() - Bernoulli sampling

Unequal probability:

• up_maxent() - Maximum entropy / Conditional Poisson
• up_brewer() - Brewer’s method
• up_systematic() - Systematic PPS
• up_poisson() - Poisson sampling
• up_multinomial() - PPS with replacement
• up_chromy() - PPS with minimum replacement

Joint inclusion probabilities :

• up_maxent_jip() - Exact CPS joint probabilities (Aires’ formula)
• up_brewer_jip() - Brewer’s approximation (equation 18)
• up_systematic_jip() - Exact systematic joint probabilities
• up_poisson_jip() - Independent selections (π_i × π_j)

Utilities:

• inclusion_prob() - Compute π from measure of size

Method comparison

Method	Fixed n	Exact π	All π_kl > 0
up_maxent	✓	✓	✓
up_brewer	✓	✓	✓
up_systematic	✓	✓	✗
up_poisson	✗	✓	✓
up_multinomial	✓	—	✓ (with replacement)
up_chromy	✓	✓	✓

References

Brewer, K.R.W. and Donadio, M.E. (2003). The High Entropy Variance of the Horvitz-Thompson Estimator. Survey Methodology, 29(2), 189-196.

Tillé, Y. (2006). Sampling Algorithms. Springer.