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Changelog

Source: NEWS.md

sondage 0.7.0

Initial CRAN release.

Sampling dispatchers

Five dispatchers returning S3 design objects with class c("<prob_class>", "<replacement>", "sondage_sample"):

  • equal_prob_wor(N, n, method=) - Simple random sampling, systematic, Bernoulli (random size).
  • equal_prob_wr(N, n, method=) - Simple random sampling with replacement.
  • unequal_prob_wor(pik, method=) - Conditional Poisson / maximum entropy (CPS), Brewer, systematic PPS, Poisson (random size), Sequential Poisson (SPS), Pareto.
  • unequal_prob_wr(hits, method=) - Chromy’s minimum replacement, multinomial PPS.
  • balanced_wor(pik, aux, strata, method=) - Cube method for balanced sampling (Deville & Tille, 2004), with optional stratification (Chauvet & Tille, 2006; Chauvet, 2009).

Design queries

Five generics for variance estimation quantities:

  • inclusion_prob() - Compute from size measures or extract from a WOR design.
  • expected_hits() - Compute from size measures or extract from a WR design.
  • joint_inclusion_prob() - N x N matrix of joint inclusion probabilities (WOR). Exact for CPS, systematic, Poisson, SRS, Bernoulli; high-entropy approximation (Brewer & Donadio, 2003) for Brewer, SPS, Pareto, cube. sampled_only = TRUE returns only the n x n submatrix for sampled units.
  • joint_expected_hits() - N x N matrix of pairwise expectations E(n_i n_j) (WR). Exact analytic for multinomial and SRS and simulation-based for Chromy (nsim parameter). sampled_only = TRUE returns only the submatrix for selected units.
  • sampling_cov() - Sampling covariance matrix. weighted = TRUE returns Sen-Yates-Grundy check quantities. sampled_only = TRUE computes only the submatrix for sampled units.

Sampling methods (13 total)

Method Dispatcher Fixed size PRN Joint probs
srs equal_prob_wor yes no Exact
systematic equal_prob_wor yes no Exact (some = 0)
bernoulli equal_prob_wor no yes Exact (independent)
srs equal_prob_wr yes no Exact
cps unequal_prob_wor yes no Exact
brewer unequal_prob_wor yes no HE approximation
systematic unequal_prob_wor yes no Exact (some = 0)
poisson unequal_prob_wor no yes Exact (independent)
sps unequal_prob_wor yes yes HE approximation
pareto unequal_prob_wor yes yes HE approximation
chromy unequal_prob_wr yes no Simulated
multinomial unequal_prob_wr yes no Exact
cube balanced_wor yes no HE approximation

Features

  • All functions return design objects usable with s$sample, s$pik, s$hits, etc.
  • Batch sampling via nrep argument for Monte Carlo simulations. Fixed-size designs return a matrix; random-size designs return a list.
  • Permanent random numbers (prn) for sample coordination (Bernoulli, Poisson, SPS, Pareto).
  • C implementations for CPS calibration and sampling, Brewer’s draw-by-draw, Chromy’s sequential PPS, systematic PPS joint probabilities, high-entropy joint probabilities, cube flight/landing phases, and inclusion probability capping.
  • CPS batch optimisation: the O(N^2) design matrix is computed once, then each replicate is a cheap sequential draw.
  • Cube batch optimisation: C-level batch entry points reuse the workspace allocation and the A = X/pi matrix across replicates, with precomputed X/pi helpers for stratified designs.
  • Cube auxiliary conditioning: condition_aux = TRUE pre-conditions aux by weighted centering/scaling and QR-pivot rank pruning, improving numerical stability with ill-conditioned or collinear auxiliary variables.
  • Stratified cube: within-stratum size constraints are preserved exactly when per-stratum sum(pik) is close to an integer; otherwise a warning is issued and the design is marked random-size.
  • High-entropy joint inclusion probabilities handle certainty units (pi_k = 1) correctly and clamp values to valid bounds. A warning is issued when the marginal defect exceeds 5% of n.
  • N-size guard: joint_inclusion_prob() and joint_expected_hits() refuse N > 10,000 to prevent accidental allocation of multi-GB dense matrices. sampled_only = TRUE bypasses this limit: all methods compute the n x n submatrix directly without allocating an N x N intermediate.
  • sampling_cov(weighted = TRUE) returns NA (not NaN) for undefined entries (zero joint probabilities or zero selection probabilities), with an informative warning.
  • All long-running C loops call R_CheckUserInterrupt() so Ctrl-C works.
  • CPS calibration warns on non-convergence.
  • Brewer’s denominator is clamped (not zeroed) for numerical safety with extreme inclusion probabilities.