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Changelog

Source: NEWS.md

fieldopt 0.1.0

  • cost_variance_frontier() and everything built on it (routed_unit_cost(), routed_cost_curve(), two_stage_design(), dual_frame_design()) route the simulated samples without the optimality certificate of single tours, which only adds proof time: about 14 times faster on single-tour problems.

  • The local-mean variance estimator (design_variance() for "lpm", "cube" and single-replicate systematic samples) averages over each sampled unit and its neighbours = 3 nearest sampled units, as BalancedSampling::vsb() (engine fieldopt-core 0.10.0). The previous neighbourhood grew until the inclusion probabilities summed to one and overestimated the variance by 60 to 160 percent for spatially smooth variables (experiment E3). design_variance() also reports df, the degrees of freedom of the variance (r - 1 with r replicates): use the t quantile for intervals.

  • routed_cost_curve() measures the routed cost G(n) = c0 + a n + b sqrt(n) of a design: Hansen, Hurwitz and Madow’s travel constant estimated by routing the frame; predict() gives the total, average and marginal cost.

  • two_stage_design() and dual_frame_design() fit the routed cost curve G(n) = c0 + a n + b sqrt(n) from samples routed at a few sizes and allocate with the marginal cost G'(n), which is what Cochran’s and Hartley’s rules need when the cost is not linear; under a budget the sample sizes come from the fitted curve, so the design spends the budget at its real routed cost. The previous loop used the average cost and took 3 to 12 percent too few primary units. Results gain c1_average (or cost_a_average), cost_curve and cost_coef.

  • schedule_fieldwork() solves the assignment of units to bases and the routes jointly, as one multi-depot problem with a route limit per base and the per_route cost in the objective (engine fieldopt-core 0.8.1); route_fieldwork() charges per_route in its objective too. On the 23 Cordeau multi-depot instances the solver reaches or beats the best known solution on 11 and stays within 1.6% on the rest, in 1 to 26 seconds.

  • Pipe-friendly inputs: travel_matrix() takes a sample (its sampled units only) with bases and a speed that turns distances into minutes; route_fieldwork() and schedule_fieldwork() take the sample as units; sampled() returns the selected rows, so a plan reads frame |> select_units(n) |> travel_matrix(bases = towns, speed = 60) |> schedule_fieldwork(units = s, ...).

  • Connectors: as_svydesign() hands a sample to the survey package (a replicate-weight design for replicated systematic samples, Brewer’s or the simple-random-sampling approximation otherwise) for calibration, domains and nonresponse adjustment; frame_overlap() matches the establishments of a list frame to the cells of the area frame (coordinates, polygons or a table of linked pairs) and marks the overlap domain; read_areaframe() reads the cells exported by the area-frame service (Parquet with WKB geometries) or any spatial file.

  • select_units(method = "lpm") and spatial_balance() use a grid spatial index (engine fieldopt-core 0.9.1): a frame of a million cells is sampled in under a second instead of hours. schedule_fieldwork(region = ) solves one region at a time and combines the calendars, the way a national survey is scheduled state by state.

  • schedule_calendar() redistributes the routes of a schedule over teams and days as an integer programme solved with the highs package (in Suggests): team availability by day, routes fixed to a team or a day, precedences, and a daily limit that lets short routes share a team-day; it minimises the last working day.

  • route_fieldwork(certify = ) runs a branch-and-bound on Held-Karp 1-trees that proves single tours optimal (engine fieldopt-core 0.9.0): most tours of up to a hundred units are certified within a few seconds, and optimal is no longer limited to instances of 13 units.

  • The routing solver was rebuilt (engine fieldopt-core 0.5.0): instances of up to 13 units are solved exactly; larger ones by a hybrid genetic search with the optimal split of giant tours and a granular local search with inter-route moves (relocate, swap, 2-opt); the Held-Karp bound replaces the two-edge bound for single tours and optimal reports proven optimality; route_fieldwork() gains demand, capacity, time_limit and engine = "vrpr" (the PyVRP solver through the vrpr package, also in schedule_fieldwork(), where it optimises the assignment to bases jointly with the routes). The built-in solver has feasible and infeasible subpopulations with adaptive penalties, SWAP and intra-route swaps, moves priced from route totals in constant time, population restarts and a time limit (engine 0.7.0): on TSPLIB it is optimal and on Augerat set A it reaches 24 of 27 optima (mean gap 0.01%, largest 0.15%) in about two seconds.

  • New tools (engine fieldopt-core 0.4.0): route_fieldwork() takes a service_time per unit that counts towards max_length and reports route durations; field_cost_model() gains per_route (the cost of a team-day); schedule_fieldwork() assigns units to bases, routes each base and builds the calendar of teams and days; dual_frame_estimator() combines the area and list samples (Hartley with fixed, screening or estimated theta; Fuller-Burmeister) with design-based variances; select_units(method = "cube", balance = ) draws balanced samples by the cube method and spatial_balance() is the Voronoi measure of Stevens and Olsen; ratio_estimator() uses a known auxiliary total; multivariate_allocation() is the Bethel allocation for several study variables; as_frame() and as_sf() move between sf layers and frames.

  • Review round (engine fieldopt-core 0.3.0): the routing local search prices every move in the direction travelled, so asymmetric road matrices (one-way streets, durations by direction) no longer loop, and the lower bound is valid for asymmetric matrices and for several routes; two_stage_allocation() tries every whole m, accepts a fractional m_secondary and meets its target in small populations; dual_frame_allocation() reads sd as the standard deviation with divisor N - 1 and refuses theta outside [0, 1] and budgets below two units per frame; select_units() refuses size with "srs" (the probabilities were mislabelled), duplicate unit names, n below the number of strata, strata with too few positive sizes and invalid seeds, and leaves the user’s random-number generator untouched; point_estimator() refuses missing y; two_stage_design() and dual_frame_design() report converged = FALSE when the rounds run out; cost_variance_frontier() takes the coordinate columns from a supplied matrix; core_version() reports the engine crate. New: autoplot() for samples, print() for points, tidy()/glance() for routes, glance() for frontiers.

  • The R wrappers are shipped in R/extendr-wrappers.R and no longer regenerated at install time (cargo run --bin document), which failed on the CRAN Windows toolchain.

  • Area-frame and dual-frame design: select_units() gains strata, a spatially ordered systematic method with interpenetrating replicates and simple random sampling; select_points(), expected_hits() and point_estimator() implement point sampling inside segments with the multiplicity estimator; segment_estimator() gives the closed, open and weighted segment estimators; dual_frame_allocation() is Hartley’s allocation with an optimised or fixed mixing weight (screening design); two_stage_allocation() is Cochran’s two-stage allocation with the cost function c1 n + c2 n m. design_variance() chooses the variance estimator that matches the design (local-mean, replicates or SRS). Engine: fieldopt-core 0.2.3.

  • cost_variance_frontier() accepts strata, selection and replicates, a list of per-stratum allocations in n_grid, and reports cost_per_unit.

  • travel_matrix() gains a detour factor for straight-line distances and method = "osrm", road durations or distances from an OSRM server through the osrm package, requested in blocks; the frontier and the routed designs accept the same method.

  • routed_unit_cost(), two_stage_design() and dual_frame_design() iterate the allocation with the cost per area unit measured by routing; point_design_effect() simulates the area design and returns the bias, variance and design effect of point sampling; select_points() gains a systematic layout; tidy() and glance() for the three allocations.

  • Variances are computed within strata and summed ("stratified srs", "stratified local-mean"); route plots of geographic matrices put longitude on the x axis.

  • First version: travel matrices from coordinates or a road network (travel_matrix()), a field cost model (field_cost_model()), GRASP routing of the field work for one or several teams (route_fieldwork()), spatially balanced selection of units with the local pivotal method (select_units()), Horvitz-Thompson estimation with the local-mean variance estimator (design_variance()), cost-aware allocation across strata or frames (frame_allocation()) and the cost-variance frontier of a design by simulation (cost_variance_frontier()). The engine is the Rust crate fieldopt-core.