shapleydecomp 1.0.0

• First CRAN release.

• The package is renamed from decomposer to shapleydecomp. CRAN already hosts DecomposeR, and CRAN package names are case-insensitive. Replace library(decomposer) / decomposer:: with shapleydecomp. The other API changes are listed below.

• decompose() is renamed shapley_decompose(), so it no longer masks stats::decompose().

• All exported functions are documented with examples. There are two new vignettes, Get started and Methods, and a pkgdown site.

• New group_vars argument to shapley_decompose() (and group_vars in run_decomp() params) names the columns, such as a location, that identify independent groups. It replaces the hard-coded ihme_loc_id grouping.

• The warm-start methods are documented together under ?warm_start, with a runnable example. The documentation now describes the actual contract: run_from_warm_start() receives the observed simulation and warm_start_year.

• n_cores > 1 on Windows falls back to sequential with a warning instead of failing.

• The Shapley algebra is now a single weight-matrix product over all groups, years and measures (#8). With 200 draws as groups, a full shapley_decompose() run is 5-7x faster and the decomposition step alone about 130x faster, with results identical to rounding.

• The decomp_scalar argument is removed from shapley_decompose() and make_run_table() (#1). Factor levels came from decomp_scalar:1, so any value other than 0 either collapsed the design (0.5:1 is 0.5) or added a level beyond “off” (-1:1), and the algebra gave wrong results for both. Every known caller used 0. For a counterfactual between on and off, define it in the model’s build_counterfactual() (see the Methods vignette).

• The future_policies argument is removed. Sequential Shapley with future policies at their counterfactual, the former default "off", is the only method. "on" was kept only to reproduce runs from before version 0.2.0: its same-year attributions equal "off"’s, and its extended rows cannot be summed. Earlier runs remain reproducible with decomposer 0.2.0. result$params no longer has a future_policies element.

• shapley_decompose() does less work for the same result (#9):

  • all counterfactual runs, across all decomp years, go through one parallel map, so a decomposition is no longer limited to 2^n cores at a time or held up by the slowest run of each year;
  • the all-off run at step y is reused from the all-on run at step y - 1 rather than simulated again (one run fewer per contiguous year);
  • the “every factor off from y + 1” parameters are built once per year (per worker) rather than once per run. Outputs are identical to the previous version.

• Factors whose counterfactual leaves a step’s parameters unchanged, such as a programme that has not started yet, are detected as null players. Their effect at that step is 0, and coalitions that differ only in null factors are simulated once (#10). A run is reused only after confirming its parameters are identical(), so a model whose counterfactuals interact loses the saving, never correctness. The new run_summary output reports, per decomp year, the runs simulated and reused and the null factors.

• A failed simulation inside n_cores > 1 now stops with the decomp year and run that failed, instead of a later, unrelated-looking error.

• summarize_results() may return any data.frame. It is converted to a data.table on the way in (#6).

• run_decomp(), decomp_algebra() and decomp_pair() match rows to on/off combinations by their factor values, so row order no longer matters (#7). Incomplete or duplicated combinations, or factor values other than 0/1, are now an error instead of a silently wrong result.

• decomp_pair() returns a data.table with measure first, like decomp_algebra() (#4).

• make_run_table() now prints when called at top level.

• shapley_decompose() reports progress with message() instead of cat().

decomposer 0.2.0

• decompose() gains future_policies, defaulting to "off": sequential Shapley, where each step varies only that year’s policy and every later policy is held at its counterfactual. "on" reproduces earlier runs.
• New sequential_totals() sums sequential steps into each factor’s total impact at every year.
• Decomposition is unified on the Shapley value. method = "das_gupta" is a deprecated alias that gives identical results.

decomposer 0.1.0

• Initial version: model-agnostic factorial decomposition through an S3 interface, with "change_since" and "total_impact" counterfactuals and optional warm starts.

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