Estimate arm-specific variance confidence intervals from pilot data and return the shared-control multi-arm CMR assignment.
Usage
cmr_multiarm(
y,
arm,
alpha = 0.05,
method = c("auto", "bounded", "bernoulli", "maurer_pontil", "mp", "bernoulli_exact",
"martinez_taboada_ramdas", "mtr"),
beta = NULL,
control_arm = 0,
normalize = FALSE,
lower = NULL,
upper = NULL,
na.rm = TRUE,
tol = 1e-11,
solver_control = list(),
max_vertices = 65536L
)Arguments
- y
Pilot outcomes.
- arm
Pilot arm labels. The control arm is identified by
control_armand internally standardized to"0".- alpha
Target joint error level.
- method
Confidence-set method.
"auto"chooses exact Bernoulli bounds for 0/1 outcomes and bounded Maurer–Pontil bounds otherwise.- beta
Optional endpoint error allocation. If
NULL, Bonferroni error is split across all lower and upper arm endpoints.- control_arm
Label identifying the control arm in
arm.- normalize
If
TRUE, normalize bounded outcomes to[0, 1]before computing variances. For guarantee-bearing bounded CMR on a non-unit scale, provide knownlowerandupperbounds.- lower, upper
Optional lower and upper outcome bounds used when
normalize = TRUE. If either is omitted, the pilot minimum and/or maximum is used with a warning; that data-dependent normalization is exploratory and does not carry the finite-sample bounded-outcome CMR guarantee.- na.rm
If
TRUE, drop rows with missingyorarm.- tol
Numerical tolerance for exact Bernoulli bound inversion.
- solver_control
Optional list of solver controls for the general vertex epigraph solver.
- max_vertices
Maximum number of hyperrectangle vertices to enumerate.
Value
A list of class cmr_multiarm with named assignment shares pi over all
arms, CMR certificate U_CMR, confidence set, pilot summaries, endpoint
error allocation, and diagnostics.