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pkgdown updates; bug fix with cbps
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‎DESCRIPTION

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Package: WeightIt
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Type: Package
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Title: Weighting for Covariate Balance in Observational Studies
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Version: 1.4.0.9002
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Version: 1.4.0.9003
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Authors@R: c(
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person("Noah", "Greifer", role=c("aut", "cre"),
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email = "[email protected]",

‎NEWS.md

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@@ -13,6 +13,8 @@ WeightIt News and Updates
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* Fixed a bug in which messages about assuming which treatment level was "treated" were printed many times, including when bootstrapping.
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* Fixed a bug in which CBPS for continuous treatments would not yield correct balance in the presence of some extreme density estimates.
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* Typo fixes in vignettes and documentation.
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# `WeightIt` 1.4.0

‎R/weightit2cbps.R

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@@ -357,16 +357,17 @@ weightit2cbps <- function(covs, treat, s.weights, estimand, focal, subset,
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if (solver == "multiroot") {
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out <- suppressWarnings({
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try(rootSolve::multiroot(f = function(...) colMeans(psi_bal(...)),
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start = par_alpha,
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Xm = mod_covs,
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Xb = bal_covs,
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A = treat,
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SW = s.weights,
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rtol = reltol,
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atol = reltol,
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ctol = reltol),
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silent = TRUE)
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try(verbosely({
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rootSolve::multiroot(f = function(...) colMeans(psi_bal(...)),
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start = par_alpha,
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Xm = mod_covs,
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Xb = bal_covs,
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A = treat,
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SW = s.weights,
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rtol = reltol,
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atol = reltol,
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ctol = reltol)
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}, verbose = FALSE), silent = TRUE)
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})
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if (!null_or_error(out) && utils::hasName(out, "root") &&
@@ -624,16 +625,17 @@ weightit2cbps.multi <- function(covs, treat, s.weights, estimand, focal, subset,
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if (solver == "multiroot") {
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out <- suppressWarnings({
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try(rootSolve::multiroot(f = function(...) colMeans(psi_bal(...)),
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start = par_alpha,
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Xm = mod_covs,
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Xb = bal_covs,
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A = treat,
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SW = s.weights,
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rtol = reltol,
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atol = reltol,
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ctol = reltol),
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silent = TRUE)
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try(verbosely({
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rootSolve::multiroot(f = function(...) colMeans(psi_bal(...)),
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start = par_alpha,
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Xm = mod_covs,
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Xb = bal_covs,
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A = treat,
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SW = s.weights,
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rtol = reltol,
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atol = reltol,
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ctol = reltol)
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}, verbose = FALSE), silent = TRUE)
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})
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if (!null_or_error(out) && utils::hasName(out, "root") &&
@@ -858,7 +860,7 @@ weightit2cbps.multi <- function(covs, treat, s.weights, estimand, focal, subset,
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weightit2cbps.cont <- function(covs, treat, s.weights, subset, missing, moments, int, verbose, ...) {
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covs <- covs[subset, , drop = FALSE]
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treat <- treat[subset]
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treat <- as.numeric(treat[subset])
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s.weights <- s.weights[subset]
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missing <- .process_missing2(missing, covs)
@@ -920,32 +922,25 @@ weightit2cbps.cont <- function(covs, treat, s.weights, subset, missing, moments,
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s.weights <- s.weights / mean_fast(s.weights)
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# dens.num <- dnorm(treat, log = TRUE)
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# un_s2 <- mean((treat - mean(treat)) ^ 2)
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# un_p <- mean(treat)
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squish_tol <- 25
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squish_tol <- 50
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# Balance condition
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psi_bal <- function(B, Xm, Xb = Xm, A, SW) {
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un_s2 <- exp(B[1L])
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un_p <- B[2L]
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log.dens.num <- squish(dnorm(A, un_p, sqrt(un_s2), log = TRUE),
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lo = -squish_tol, hi = squish_tol)
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lo = -Inf, hi = squish_tol)
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s2 <- exp(B[3L])
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p <- drop(Xm %*% B[-(1:3)])
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log.dens.denom <- squish(dnorm(A, p, sqrt(s2), log = TRUE),
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lo = -squish_tol, hi = squish_tol)
937+
lo = -squish_tol, hi = Inf)
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w <- exp(log.dens.num - log.dens.denom)
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cbind(SW * (A - un_p)^2 - un_s2,
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SW * (A - un_p),
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SW * (A - p)^2 - s2,
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# SW * (A - p),
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# SW * w * Xb,
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SW * w * A * Xb)
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}
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@@ -965,16 +960,17 @@ weightit2cbps.cont <- function(covs, treat, s.weights, subset, missing, moments,
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if (solver == "multiroot") {
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out <- suppressWarnings({
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try(rootSolve::multiroot(f = function(...) colMeans(psi_bal(...)),
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start = par_alpha,
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Xm = mod_covs,
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Xb = bal_covs,
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A = treat,
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SW = s.weights,
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rtol = reltol,
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atol = reltol,
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ctol = reltol),
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silent = TRUE)
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try(verbosely({
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rootSolve::multiroot(f = function(...) colMeans(psi_bal(...)),
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start = par_alpha,
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Xm = mod_covs,
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Xb = bal_covs,
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A = treat,
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SW = s.weights,
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rtol = reltol,
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atol = reltol,
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ctol = reltol)
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}, verbose = FALSE), silent = TRUE)
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})
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if (!null_or_error(out) && utils::hasName(out, "root") &&
@@ -1009,10 +1005,7 @@ weightit2cbps.cont <- function(covs, treat, s.weights, subset, missing, moments,
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p <- drop(Xm %*% B[-(1:3)])
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cbind(#SW * (A - un_p)^2 - un_s2,
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#SW * (A - un_p),
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#SW * (A - p)^2 - s2,
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SW * (A - p) * Xm)
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cbind(SW * (A - p) * Xm)
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}
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# Combine LR and balance
@@ -1070,11 +1063,13 @@ weightit2cbps.cont <- function(covs, treat, s.weights, subset, missing, moments,
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un_s2 <- exp(par_out[1L])
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un_p <- par_out[2L]
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log.dens.num <- dnorm(treat, un_p, sqrt(un_s2), log = TRUE)
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log.dens.num <- squish(dnorm(treat, un_p, sqrt(un_s2), log = TRUE),
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lo = -Inf, hi = squish_tol)
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s2 <- exp(par_out[3L])
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p <- drop(mod_covs %*% par_out[-(1:3)])
1077-
log.dens.denom <- dnorm(treat, p, sqrt(s2), log = TRUE)
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log.dens.denom <- squish(dnorm(treat, p, sqrt(s2), log = TRUE),
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lo = -squish_tol, hi = Inf)
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w <- exp(log.dens.num - log.dens.denom)
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@@ -1197,7 +1192,7 @@ weightitMSM2cbps <- function(covs.list, treat.list, s.weights, subset, missing,
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})
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})
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squish_tol <- 25
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squish_tol <- 50
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get_w <- lapply(seq_along(treat.list), function(i) {
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switch(treat.types[i],
@@ -1296,15 +1291,16 @@ weightitMSM2cbps <- function(covs.list, treat.list, s.weights, subset, missing,
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if (solver == "multiroot") {
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out <- suppressWarnings({
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try(rootSolve::multiroot(f = function(...) colMeans(psi_bal(...)),
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start = par_alpha,
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X.list = covs.list,
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A.list = treat.list,
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SW = s.weights,
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rtol = reltol,
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atol = reltol,
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ctol = reltol),
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silent = TRUE)
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try(verbosely({
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rootSolve::multiroot(f = function(...) colMeans(psi_bal(...)),
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start = par_alpha,
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X.list = covs.list,
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A.list = treat.list,
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SW = s.weights,
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rtol = reltol,
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atol = reltol,
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ctol = reltol)
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}, verbose = FALSE), silent = TRUE)
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})
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if (!null_or_error(out) && out$estim.precis < 1e-5) {

‎R/weightit2ebal.R

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@@ -241,13 +241,14 @@ weightit2ebal <- function(covs, treat, s.weights, subset, estimand, focal,
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if (solver == "multiroot") {
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out <- suppressWarnings({
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try(rootSolve::multiroot(f = gradient.EB,
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start = coef_start,
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S = s.weights_t, C = C, Q = Q,
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rtol = reltol,
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atol = reltol,
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ctol = reltol),
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silent = TRUE)
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try(verbosely({
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rootSolve::multiroot(f = gradient.EB,
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start = coef_start,
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S = s.weights_t, C = C, Q = Q,
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rtol = reltol,
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atol = reltol,
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ctol = reltol)
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}, verbose = FALSE), silent = TRUE)
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})
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if (!null_or_error(out) && utils::hasName(out, "root") &&
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if (solver == "multiroot") {
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out <- suppressWarnings({
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try(rootSolve::multiroot(f = gradient.EB,
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start = coef_start,
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S = s.weights, C = C, Q = Q,
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maxiter = 20),
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silent = TRUE)
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try(verbosely({
532+
rootSolve::multiroot(f = gradient.EB,
533+
start = coef_start,
534+
S = s.weights, C = C, Q = Q,
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maxiter = 20)
536+
}, verbose = FALSE), silent = TRUE)
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})
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if (!null_or_error(out) && utils::hasName(out, "root") &&
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#
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# if (solver == "multiroot") {
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# out <- suppressWarnings({
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# try(rootSolve::multiroot(f = gradient.EB,
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# try(verbosely({
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# rootSolve::multiroot(f = gradient.EB,
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# start = coef_start,
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# S = s.weights, C = C, Q = Q,
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# maxiter = 20),
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# silent = TRUE)
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# maxiter = 20)
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# }, verbose = FALSE), silent = TRUE)
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# })
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#
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# if (!null_or_error(out) && utils::hasName(out, "root") &&

‎_pkgdown.yml

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template:
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bootstrap: 5
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math-rendering: mathjax
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bslib:
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primary: "#00612E"
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url: https://ngreifer.github.io/WeightIt/
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reference:
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- title: Estimate weights

‎pkgdown/favicon/favicon.svg

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Loading

‎vignettes/installing-packages.Rmd

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*WeightIt* is a wrapper for several other packages that aid in estimating balancing weights. In many ways, this is *WeightIt*'s strength, because it is easy to try out several weighting methods without having to learn a completely new syntax for each one. One weakness of this is that when one of these packages is not available (e.g., on CRAN), the method that relies on that package cannot be used.
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This document explains how to install each package *WeightIt* uses. You do not need to install every single one; you only need the one you want to use. For example, the *miseam* package provides support for logistic regression with missing data, but if you have no missing data or you don't want to use the approach implemented in *miseam*, you don't need to install it. *WeightIt* strongly depends on a few packages, which are automatically installed along with *WeightIt*, so you don't need to worry about installing them separately; these are not listed here.
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This document explains how to install each package *WeightIt* uses. You do not need to install every single one; you only need the one you want to use. For example, the *misaem* package provides support for logistic regression with missing data, but if you have no missing data or you don't want to use the approach implemented in *misaem*, you don't need to install it. *WeightIt* strongly depends on a few packages, which are automatically installed along with *WeightIt*, so you don't need to worry about installing them separately; these are not listed here.
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Below we note each method (by name and by the input to the `method` argument of `weightit()`) and how to install the required packages either from CRAN or otherwise when the CRAN version is not available. In many cases, this involves installing the package from the author's GitHub repository, which requires the *pak* package, which contains the function `pkg_install()`.
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