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ebalance() is no longer an S3 generic. The formula
vs. matrix dispatch is now handled inside the function via
inherits(Treatment, "formula"). The user-facing API is
unchanged: ebalance(treat ~ x1 + x2, data = df) and
ebalance(Treatment = t, X = X) both work exactly as before.
This change was made to satisfy CRAN’s auto-checker, which flagged the
long-standing top-level function ebalance.trim() as an
“apparent method” of the ebalance generic and refused the
0.2.0 submission.Authors@R field replaces the older Author
/ Maintainer pair (current CRAN style).Depends: methods moved to
Imports: graphics, methods, stats so the package no longer
attaches its dependencies onto the user’s search path.tests/testthat/ suite with 54 assertions covering
the matrix and formula interfaces, the ebalance.trim graceful-fallback
behavior, the new S3 methods, and the helper functions
(matrixmaker, getsquares).README.md with quick-start examples for both the matrix
and formula interfaces and a section on trimming.R-CMD-check.yaml) runs
against R release on macOS / Windows / Ubuntu, R devel on Ubuntu, and
one prior R release on Ubuntu.Formula interface.
ebalance(treat ~ x1 + x2, data = df) now works, in addition
to the original ebalance(Treatment = t, X = X) matrix
interface. Both produce identical numerical results. The formula
interface uses model.frame() and
model.matrix() and drops the (Intercept)
column automatically.
weights() method.
weights(fit) returns a length- vector aligned to the
original Treatment: treated units get weight 1, control
units get their entropy-balancing weight. Drop-in for
lm(..., weights = w) or
survey::svyglm().
print() and summary()
methods. print(fit) shows a one-screen overview
(counts, moments, convergence status, and for trimmed objects whether
the trim target was feasible). summary(fit) returns a
balance table comparing treated and control means (and standardized
differences) before and after weighting.
plot() method. Base-graphics Love
plot of standardized differences before vs. after weighting, one row per
covariate. No ggplot2 or other graphics dependency.
New return fields on ebalance and
ebalance.trim objects: Treatment (the
original treatment indicator) and X (the original covariate
matrix). Existing fields are unchanged. These enable the
summary(), plot(), and weights()
methods without requiring the user to pass the original data back
in.
ebalance is now an S3 generic with
ebalance.default (the workhorse with the matrix interface)
and ebalance.formula (formula method) registered.
ebalance.trim is intentionally NOT registered as a method;
it remains a top-level function callable as
ebalance.trim(fit) exactly as before. R CMD check emits an
informational NOTE about the legacy name and we accept it.ebalance() and ebalance.trim() no
longer crash with "missing value where TRUE/FALSE needed"
when the optimizer is pushed into a regime where
exp(co.x %*% coefs) would overflow IEEE double precision.
Both call sites now route through an internal .safe_exp()
helper that caps the linear predictor at 700 before exponentiating. The
cap is inactive (no observable effect) for well-conditioned problems; it
activates when an aggressive max.weight target in
ebalance.trim() forces the algorithm to explore very large
coefficients, returning a large-but-finite weight so the line search and
gradient check can keep navigating.
ebalance.trim() now fails gracefully when the inner
Newton solve becomes numerically singular during the
explicit-max.weight branch (for example, because the
requested target is infeasible). It emits a clear warning, returns the
most recent feasible fit, and sets the new trim.feasible
field to FALSE. Previously it crashed with
"system is computationally singular" (or, before the
overflow guard, the older NaN error). The
automatic-minimization branch was already wrapped in try()
and is unchanged.
New return field trim.feasible (logical) on
ebalance.trim objects. TRUE when an explicit
target was met or when automatic minimization completed;
FALSE when an explicit target proved infeasible. Existing
fields are unchanged; reading any other field by name continues to work
as before.
ebalance() uses colSums() instead of
apply(X, 2, sum) to compute treatment-group target margins.
Output is identical; trivially faster and avoids an unnecessary
as.matrix() wrap.
H = X' diag(w) X is symmetric positive
semidefinite, so a Cholesky-based solve would be the textbook choice. We
considered switching from solve(H, gradient) to
chol2inv(chol(H)) but kept solve() because (a)
the Hessian is small in this package (covariates × covariates, typically
< 50 × 50), so the speed difference is negligible, and (b) Cholesky
and LU agree only at machine precision, which would introduce last-bit
drift versus the 0.1-8 baseline that we have no compelling reason to
accept.ebalance.trim() now correctly populates the
target.margins field of the returned object. Previously it
referenced a non-existent ebalanceobj$tr.total and silently
returned NULL.ebalance() now uses drop = FALSE when
subsetting the control rows of X, eliminating a latent
dimension-drop issue that was masked by cbind() in the next
line.print.level = 0, neither
ebalance() nor ebalance.trim() emits
per-iteration progress messages or a “Converged within tolerance” line.
Set print.level = 1 to restore the previous chatter;
2 and 3 give increasing detail.eb(): removed a redundant call to
solve(hessian, gradient) after the line search; the Newton
direction is now reused.getsquares(): replaced
dim(table(x)) > 2 with
length(unique(x)) > 2; same predicate, faster on large
vectors.matrixmaker(): dropped an unused “dummy” column that
was allocated and then discarded before return.zzz.r: removed unused this.year
computation; switched startup message URL to https.ebalance.trim.Rd: \seealso now correctly
cross-references ebalance instead of itself.print.level documentation in ebalance.Rd
and ebalance.trim.Rd now lists all four levels (0, 1, 2, 3)
the code actually supports.These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.