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Changelog

Source: NEWS.md

samplyr 0.5.9999

Initial release.

Core grammar

Sampling methods

  • 13 methods in three families:
    • Equal probability: srswor, srswr, systematic, bernoulli.
    • PPS without replacement: pps_systematic, pps_brewer, pps_cps (maximum entropy), pps_poisson, pps_sps, pps_pareto.
    • PPS with replacement / PMR: pps_multinomial, pps_chromy.
  • Balanced sampling via the cube method (method = "balanced") with optional auxiliary balancing variables and measure of size. Stratified designs use the stratified cube algorithm. Supported for up to 2 stages.
  • Permanent random numbers (PRN) for sample coordination: bernoulli, pps_poisson, pps_sps, pps_pareto.
  • Random-size methods (bernoulli, pps_poisson) accept n (expected size) or frac (sampling fraction).

Stratification and allocation

  • Five allocation methods via stratify_by(..., alloc =): proportional, equal, Neyman, optimal, and power.
  • Custom allocation via named vectors or data frames.
  • Minimum and maximum sample size constraints per stratum (min_n, max_n).

Multi-stage and multi-phase

  • Multi-stage sampling with add_stage(). Weights compound automatically across stages.
  • Partial execution via execute(..., stages = 1) for operational workflows.
  • Two-phase sampling by piping a tbl_sample into execute().

Certainty selection

  • PPS WOR methods support certainty selection via absolute (certainty_size) or proportional (certainty_prop) thresholds, including iterative identification for proportional thresholds.
  • certainty_overflow = "allow" returns all certainty units when they exceed n.
  • Stratum-specific thresholds via data frames.

Panel partitioning

  • execute(..., panels = k) assigns units to k panels via systematic within-stratum interleaving.
  • Multi-stage designs assign panels at PSU level and propagate to all units.

Control sorting

  • control = c(var1, var2) for nested sorting.
  • control = serp(var1, var2) for serpentine (alternating direction) sorting.

Survey export

  • as_svydesign() converts tbl_sample to survey::svydesign() with correct strata, cluster IDs, weights, and finite population corrections. Handles PPS WOR (Brewer approximation or exact ppsmat), WR/PMR (Inf FPC, Hansen-Hurwitz), certainty strata, balanced sampling, and two-phase designs.
  • as_svrepdesign() converts to replicate-weight designs. For PPS and balanced designs, "subbootstrap" and "mrbbootstrap" are supported.
  • as_survey_design() and as_survey_rep() for direct conversion to srvyr tbl_svy objects.
  • joint_expectation() computes pairwise joint inclusion probabilities (WOR) or joint expected hits (WR/PMR) for exact variance estimation.

Survey planning

  • design_effect() and effective_n() with tbl_sample methods. Five methods: Kish, Henry, Spencer, Chen-Rust, and cluster planning. Auto-extraction of strata, clusters, and selection probabilities from the stored design.
  • draw() accepts svyplan sample size objects (svyplan_n, svyplan_power, svyplan_cluster) directly.
  • Precision analysis (prec_prop(), prec_mean(), prec_cluster(), prec_multi()), sensitivity analysis (predict()), response rate adjustment (resp_rate), and confidence intervals (confint()) on all planning objects.

Diagnostics

  • summary() shows per-stage stratum allocation tables with N_h, n_h, f_h, and weight diagnostics (Kish DEFF, n_eff, CV).
  • validate_frame() checks for missing variables, NA values in key columns, and MOS/PRN/auxiliary variable issues before execution.

Datasets

  • bfa_eas: 14,900 enumeration areas from Burkina Faso (LSMS/HBS style). Companion tables bfa_eas_variance and bfa_eas_cost for Neyman and optimal allocation.
  • zwe_eas and zwe_households: DHS-style two-stage cluster frame from Zimbabwe (22,600 EAs and 379,326 households).
  • ken_enterprises: 6,823 establishments from Kenya for enterprise surveys, panel partitioning, and PRN coordination examples.

Vignettes

  • Introduction: full tutorial covering SRS through multi-stage PPS designs.
  • Design semantics: assumptions, weight formulas, and method properties.
  • Survey analysis: export to survey/srvyr, joint probabilities, two-phase.
  • Sampling coordination: PRN workflows, positive/negative coordination.
  • Survey planning: svyplan integration, sample size, precision, design effects.
  • Validation: deterministic invariants and Monte Carlo coverage checks on synthetic populations.