nt-williams

Papers using lmtp

• Evaluating shifts in mobility and COVID-19 case rates in U.S. counties: A demonstration of modified treatment policies for causal inference with continuous exposures
• When effects cannot be estimated: redefining estimands to understand the effects of naloxone access laws
• [Primary Care Provider Density and Elective Total Joint Replacement

nt-williams

SL.lightgbm <- function(Y, X, newX, family, obsWeights, id, nrounds = 1000, verbose = -1, 
                        learning_rate = 0.1, min_data_in_leaf = 10, max_depth = -1, ...) {
  if(!requireNamespace("lightgbm", quietly = FALSE)) {
    stop("loading required package (lightgbm) failed", call. = FALSE)
  }
  
  if (family$family == "gaussian") {
    objective <- "regression"
    evalu <- ""
  }

nt-williams

lmtp_type <- \(x) structure(x, class = class(x[[1]]))

rubins_rules <- \(x, ...) UseMethod("rubins_rules", lmtp_type(x))

rubins_rules.lmtp <- function(lmtps, label, alpha = 0.05) {
  thetas <- purrr::map_dbl(lmtps, "theta")
  vw <- mean(purrr::map_dbl(lmtps, \(x) x$standard_error^2))
  vb <- var(thetas)
  theta <- mean(thetas)
  se <- pooled_se(vw, vb, length(thetas))

nt-williams

glmnet3 <- function(X, y, family = c("gaussian", "binomial"), id = NULL) {
  if (!is.null(id)) {
    folds <- origami::make_folds(
      nrow(X), fold_fun = origami::folds_vfold,
      cluster_ids = id, V = 10
    )

    foldid <- vector("numeric", nrow(X))
    for (i in 1:nrow(X)) {
      for (v in 1:10) {

nt-williams

read_zip_rds <- function(tar) {
  files <- unzip(tar, list = TRUE)$Name
  p <- progressr::progressor(along = 1:length(files))
  purrr::map(files, function(file) {
    p()
    con <- gzcon(unz(tar, file))
    x <- readRDS(con)
    close(con)
    x
  })

nt-williams

tmle_mlr3 <- function(data, 
											trt, 
											covar_trt, 
											covar_outcome, 
											covar_cens,
											outcome, 
											id = NULL,
											g_learners = "glm", 
											Q_learners = "glm", 
											c_learners = "glm",