Update docs and tests

NAMESPACE

@@ -17,6 +17,7 @@ S3method(is_unit_truncated,default)
 S3method(is_unit_truncated,ps_trunc)
 S3method(is_unit_truncated,psw)
 S3method(print,ipw)
+S3method(summary,psw)
 S3method(vec_arith,ps_trim)
 S3method(vec_arith,ps_trunc)
 S3method(vec_arith,psw)

R/psw.R

@@ -292,3 +292,8 @@ vec_cast.psw.integer <- function(x, to, estimand = NULL, ...)
 vec_cast.integer.psw <- function(x, to, ...) {
   vec_cast(vec_data(x), integer(), x_arg = "psw")
 }
+
+#' @export
+summary.psw <- function(object, ...) {
+  summary(as.numeric(object), ...)
+}

R/weights.R

@@ -22,37 +22,73 @@
 #'   weights have been stabilized, trimmed, or truncated.
 #'
 #' @details
+#' ## Theoretical Background
+#'
+#' Propensity score weighting is a method for estimating causal effects by
+#' creating a pseudo-population where the exposure is independent of measured
+#' confounders. The propensity score, \eqn{e(X)}, is the probability of receiving
+#' treatment given observed covariates \eqn{X}. By weighting observations inversely
+#' proportional to their propensity scores, we can balance the distribution of
+#' covariates between treatment groups. Other weights allow for different target populations.
+#'
+#' ## Mathematical Formulas
+#'
+#' ### Binary Exposures
+#'
+#' For binary treatments (\eqn{A = 0} or \eqn{1}), the weights are:
+#'
+#' - **ATE**: \eqn{w = \frac{A}{e(X)} + \frac{1-A}{1-e(X)}}
+#' - **ATT**: \eqn{w = A + \frac{(1-A) \cdot e(X)}{1-e(X)}}
+#' - **ATU**: \eqn{w = \frac{A \cdot (1-e(X))}{e(X)} + (1-A)}
+#' - **ATM**: \eqn{w = \frac{\min(e(X), 1-e(X))}{A \cdot e(X) + (1-A) \cdot (1-e(X))}}
+#' - **ATO**: \eqn{w = A \cdot (1-e(X)) + (1-A) \cdot e(X)}
+#' - **Entropy**: \eqn{w = \frac{h(e(X))}{A \cdot e(X) + (1-A) \cdot (1-e(X))}}, where \eqn{h(e) = -[e \cdot \log(e) + (1-e) \cdot \log(1-e)]}
+#'
+#' ### Continuous Exposures
+#'
+#' For continuous treatments, weights use the density ratio:
+#' \eqn{w = \frac{f_A(A)}{f_{A|X}(A|X)}}, where \eqn{f_A} is the marginal density of \eqn{A}
+#' and \eqn{f_{A|X}} is the conditional density given \eqn{X}.
+#'
 #' ## Exposure Types
 #'
-#'   The functions support different types of exposures:
+#' The functions support different types of exposures:
 #'
 #' - **`binary`**: For dichotomous treatments (e.g. 0/1).
 #' - **`continuous`**: For numeric exposures. Here, weights are calculated via the normal density using
 #'   `dnorm()`.
 #' - **`categorical`**: Currently not supported (an error will be raised).
 #' - **`auto`**: Automatically detects the exposure type based on `.exposure`.
 #'
-#'   ## Stabilization
+#' ## Stabilization
+#'
+#' For ATE weights, stabilization can improve the performance of the estimator
+#' by reducing variance. When `stabilize` is `TRUE` and no
+#' `stabilization_score` is provided, the weights are multiplied by the mean
+#' of `.exposure`. Alternatively, if a `stabilization_score` is provided, it
+#' is used as the multiplier. Stabilized weights have the form:
+#' \eqn{w_s = f_A(A) \times w}, where \eqn{f_A(A)} is the marginal probability or density.
 #'
-#'   For ATE weights, stabilization can improve the performance of the estimator
-#'   by reducing variance. When `stabilize` is `TRUE` and no
-#'   `stabilization_score` is provided, the weights are multiplied by the mean
-#'   of `.exposure`. Alternatively, if a `stabilization_score` is provided, it
-#'   is used as the multiplier.
+#' ## Weight Properties and Diagnostics
 #'
-#'   ## Trimmed and Truncated Weights
+#' Extreme weights can indicate:
+#' - Positivity violations (near 0 or 1 propensity scores)
+#' - Poor model specification
+#' - Lack of overlap between treatment groups
+#' 
+#' See the halfmoon package for tools to diagnose and visualize weights.
+#' 
+#' You can address extreme weights in several ways. The first is to modify the target population:
+#' use trimming, truncation, or alternative estimands (ATM, ATO, entropy). 
+#' Another technique that can help is stabilization, which reduces variance of the weights.
 #'
-#'   In addition to the standard weight functions, versions exist for trimmed
-#'   and truncated propensity score weights created by [ps_trim()],
-#'   [ps_trunc()], and [ps_refit()]. These variants calculate the weights using
-#'   modified propensity scores (trimmed or truncated) and update the estimand
-#'   attribute accordingly.
+#' ## Trimmed and Truncated Weights
 #'
-#'   The main functions (`wt_ate`, `wt_att`, `wt_atu`, `wt_atm`, and `wt_ato`)
-#'   dispatch on the class of `.propensity`. For binary exposures, the weights
-#'   are computed using inverse probability formulas. For continuous exposures
-#'   (supported only for ATE), weights are computed as the inverse of the
-#'   density function evaluated at the observed exposure.
+#' In addition to the standard weight functions, versions exist for trimmed
+#' and truncated propensity score weights created by [ps_trim()],
+#' [ps_trunc()], and [ps_refit()]. These variants calculate the weights using
+#' modified propensity scores (trimmed or truncated) and update the estimand
+#' attribute accordingly.
 #'
 #' @param .propensity Either a numeric vector of predicted probabilities or a
 #'   `data.frame` where each column corresponds to a level of the exposure.
@@ -83,31 +119,94 @@
 #'   - **truncated**: A logical flag indicating if the weights are based on truncated propensity scores.
 #'
 #' @examples
-#' ## ATE Weights with a Binary Exposure
+#' ## Basic Usage with Binary Exposures
+#'
+#' # Simulate a simple dataset
+#' set.seed(123)
+#' n <- 100
+#' propensity_scores <- runif(n, 0.1, 0.9)
+#' treatment <- rbinom(n, 1, propensity_scores)
+#'
+#' # Calculate different weight types
+#' weights_ate <- wt_ate(propensity_scores, treatment)
+#' weights_att <- wt_att(propensity_scores, treatment)
+#' weights_atu <- wt_atu(propensity_scores, treatment)
+#' weights_atm <- wt_atm(propensity_scores, treatment)
+#' weights_ato <- wt_ato(propensity_scores, treatment)
+#' weights_entropy <- wt_entropy(propensity_scores, treatment)
+#'
+#' # Compare weight distributions
+#' summary(weights_ate)
+#' summary(weights_ato)  # Often more stable than ATE
+#'
+#' ## Stabilized Weights
+#'
+#' # Stabilization reduces variance
+#' weights_ate_stab <- wt_ate(propensity_scores, treatment, stabilize = TRUE)
+#'
+#' # Compare coefficient of variation
+#' sd(weights_ate) / mean(weights_ate)      # Unstabilized
+#' sd(weights_ate_stab) / mean(weights_ate_stab)  # Stabilized (lower is better)
+#'
+#' ## Handling Extreme Propensity Scores
+#'
+#' # Create data with positivity violations
+#' ps_extreme <- c(0.01, 0.02, 0.98, 0.99, rep(0.5, 4))
+#' trt_extreme <- c(0, 0, 1, 1, 0, 1, 0, 1)
+#'
+#' # Standard ATE weights can be extreme
+#' wt_extreme <- wt_ate(ps_extreme, trt_extreme)
+#' # Very large!
+#' max(wt_extreme)  
+#'
+#' # ATO weights are bounded
+#' wt_extreme_atm <- wt_ato(ps_extreme, trt_extreme)
+#' # Much more reasonable
+#' max(wt_extreme_atm)  
+#' # but they target a different population
+#' estimand(wt_extreme_atm) # "ato"
+#'
+#' @references
+#' 
+#' For detailed guidance on causal inference in R, see [*Causal Inference in R*](https://www.r-causal.org/) 
+#' by Malcolm Barrett, Lucy D'Agostino McGowan, and Travis Gerke.
+#' 
+#' ## Foundational Papers
+#'
+#' Rosenbaum, P. R., & Rubin, D. B. (1983). The central role of the propensity
+#' score in observational studies for causal effects. *Biometrika*, 70(1), 41-55.
+#'
+#' ## Estimand-Specific Methods
+#'
+#' Li, L., & Greene, T. (2013). A weighting analogue to pair matching in
+#' propensity score analysis. *The International Journal of Biostatistics*, 9(2),
+#' 215-234. (ATM weights)
 #'
-#' # Simulate a binary treatment and corresponding propensity scores
-#' propensity_scores <- c(0.2, 0.7, 0.5, 0.8)
-#' treatment <- c(0, 1, 0, 1)
+#' Li, F., Morgan, K. L., & Zaslavsky, A. M. (2018). Balancing covariates via
+#' propensity score weighting. *Journal of the American Statistical Association*,
+#' 113(521), 390-400. (ATO weights)
 #'
-#' # Compute ATE weights (unstabilized)
-#' weights_ate <- wt_ate(propensity_scores, .exposure = treatment)
-#' weights_ate
+#' Zhou, Y., Matsouaka, R. A., & Thomas, L. (2020). Propensity score weighting
+#' under limited overlap and model misspecification. *Statistical Methods in
+#' Medical Research*, 29(12), 3721-3756. (Entropy weights)
 #'
-#' # Compute ATE weights with stabilization using the mean of the exposure
-#' weights_ate_stab <- wt_ate(propensity_scores, .exposure = treatment, stabilize = TRUE)
-#' weights_ate_stab
+#' ## Continuous Exposures
 #'
-#' ## ATT Weights for a Binary Exposure
+#' Hirano, K., & Imbens, G. W. (2004). The propensity score with continuous
+#' treatments. *Applied Bayesian Modeling and Causal Inference from
+#' Incomplete-Data Perspectives*, 226164, 73-84.
 #'
-#' propensity_scores <- c(0.3, 0.6, 0.4, 0.7)
-#' treatment <- c(1, 1, 0, 0)
+#' ## Practical Guidance
 #'
-#' # Compute ATT weights
-#' weights_att <- wt_att(propensity_scores, .exposure = treatment)
-#' weights_att
+#' Austin, P. C., & Stuart, E. A. (2015). Moving towards best practice when
+#' using inverse probability of treatment weighting (IPTW) using the propensity
+#' score to estimate causal treatment effects in observational studies.
+#' *Statistics in Medicine*, 34(28), 3661-3679.
 #'
 #' @seealso
 #' - [psw()] for details on the structure of the returned weight objects.
+#' - [ps_trim()], [ps_trunc()], and [ps_refit()] for handling extreme weights.
+#' - [ps_calibrate()] for calibrating weights.
 #'
 #' @export
 wt_ate <- function(