Cleaning and updates

R/glm_weightit-methods.R

@@ -14,7 +14,7 @@
 #' @param thresholds `logical`; whether to include thresholds in the `summary()` output for `ordinal_weightit` objects. Default is `TRUE`.
 #' @param vcov either a string indicating the method used to compute the variance of the estimated parameters for `object`, a function used to extract the variance, or the variance matrix itself. Default is to use the variance matrix already present in `object`. If a string or function, arguments passed to `...` are supplied to the method or function. (Note: for `vcov()`, can also be supplied as `type`.)
 #' @param complete `logical`; whether the full variance-covariance matrix should be returned also in case of an over-determined system where some coefficients are undefined and `coef(.)` contains `NA`s correspondingly. When `complete = TRUE`, `vcov()` is compatible with `coef()` also in this singular case.
-#' @param formula. changes to the model formula, passed to the `new` argument of [udpdate.formula()].
+#' @param formula. changes to the model formula, passed to the `new` argument of [update.formula()].
 #' @param \dots for `vcov()` or `summary()` or `confint()` with `vcov` supplied, other arguments used to compute the variance matrix depending on the method supplied to `vcov`, e.g., `cluster`, `R`, or `fwb.args`. For `update()`, additional arguments to the call or arguments with changed values. See [glm_weightit()] for details.
 #' @param evaluate whether to evaluate the call (`TRUE`, the default) or just return it.
 #'
@@ -714,6 +714,7 @@ update.glm_weightit <- function(object, formula. = NULL, ..., evaluate = TRUE) {
   object
 }
 
+#Note: the following need to be fun.foo <- fun.bar for environment reasons
 #' @exportS3Method stats::update multinom_weightit
 update.multinom_weightit <- update.glm_weightit
 

R/multinom_weightit.R

@@ -83,8 +83,8 @@
                   maxit = 1e3,
                   reltol = 1e-12)
 
-  control <- modifyList(control,
-                        dots[intersect(names(dots), c("trace", "maxit", "reltol", "ndeps", "REPORT"))])
+  control <- utils::modifyList(control,
+                               dots[intersect(names(dots), c("trace", "maxit", "reltol", "ndeps", "REPORT"))])
 
   out <- optim(par = start[!aliased_B],
                ll,

R/ordinal_weightit.R

@@ -136,8 +136,8 @@
                   maxit = 1e3,
                   reltol = 1e-12)
 
-  control <- modifyList(control,
-                        dots[intersect(names(dots), c("trace", "maxit", "reltol", "ndeps", "REPORT"))])
+  control <- utils::modifyList(control,
+                               dots[intersect(names(dots), c("trace", "maxit", "reltol", "ndeps", "REPORT"))])
 
   # Estimate using cumsum parameterization to get estimates
   out0 <- optim(par = start,

README.Rmd

@@ -17,12 +17,9 @@ knitr::opts_chunk$set(
 ```
 # WeightIt: Weighting for Covariate Balance in Observational Studies <img src="man/figures/logo.png" align="right" width="150"/>
 
-<!-- badges: start -->
 [![CRAN status](https://www.r-pkg.org/badges/version/WeightIt?color=00622B)](https://CRAN.R-project.org/package=WeightIt)
 [![CRAN_Downloads_Badge](https://cranlogs.r-pkg.org/badges/WeightIt?color=00622B)](https://cran.r-project.org/package=WeightIt)
-<!-- badges: end -->
 
-------
 ### Overview
 
 *WeightIt* is a one-stop package to generate balancing weights for point and longitudinal treatments in observational studies. Support is included for binary, multi-category, and continuous treatments, a variety of estimands including the ATE, ATT, ATC, ATO, and others, and support for a wide variety of weighting methods, including those that rely on parametric modeling, machine learning, or optimization. *WeightIt* also provides functionality for fitting regression models in weighted samples that account for estimation of the weights in quantifying uncertainty. *WeightIt* uses a familiar formula interface and is meant to complement `MatchIt` as a package that provides a unified interface to basic and advanced weighting methods.
@@ -33,10 +30,10 @@ To install and load *WeightIt* , use the code below:
 
 ```{r, eval = FALSE}
 #CRAN version
-install.packages("WeightIt")
+pak::pkg_install("WeightIt")
 
 #Development version
-remotes::install_github("ngreifer/WeightIt")
+pak::pkg_install("ngreifer/WeightIt")
 
 library("WeightIt")
 ```
@@ -75,8 +72,8 @@ Desirable qualities include small coefficients of variation close to 0 and large
 Finally, we can estimate the effect of the treatment using a weighted outcome model, accounting for estimation of the weights in the standard error of the effect estimate:
 
 ```{r}
-fit <- lm_weightit(re78 ~ treat, data = lalonde,
-                   weightit = W)
+fit <- glm_weightit(re78 ~ treat, data = lalonde,
+                    weightit = W)
 
 summary(fit, ci = TRUE)
 ```

README.md

@@ -3,14 +3,9 @@
 
 # WeightIt: Weighting for Covariate Balance in Observational Studies <img src="man/figures/logo.png" align="right" width="150"/>
 
-<!-- badges: start -->
-
 [![CRAN
 status](https://www.r-pkg.org/badges/version/WeightIt?color=00622B)](https://CRAN.R-project.org/package=WeightIt)
 [![CRAN_Downloads_Badge](https://cranlogs.r-pkg.org/badges/WeightIt?color=00622B)](https://cran.r-project.org/package=WeightIt)
-<!-- badges: end -->
-
-------------------------------------------------------------------------
 
 ### Overview
 
@@ -34,10 +29,10 @@ To install and load *WeightIt* , use the code below:
 
 ``` r
 #CRAN version
-install.packages("WeightIt")
+pak::pkg_install("WeightIt")
 
 #Development version
-remotes::install_github("ngreifer/WeightIt")
+pak::pkg_install("ngreifer/WeightIt")
 
 library("WeightIt")
 ```
@@ -149,9 +144,9 @@ summary(fit, ci = TRUE)
     #> lm_weightit(formula = re78 ~ treat, data = lalonde, weightit = W)
     #> 
     #> Coefficients:
-    #>             Estimate Std. Error z value  Pr(>|z|)  2.5 % 97.5 %
-    #> (Intercept)     5135      583.8   8.797 1.411e-18 3990.9   6279
-    #> treat           1214      798.2   1.521 1.282e-01 -350.3   2778
+    #>             Estimate Std. Error z value Pr(>|z|)  2.5 % 97.5 %    
+    #> (Intercept)   5135.1      583.8   8.797   <1e-06 3990.9 6279.2 ***
+    #> treat         1214.1      798.2   1.521    0.128 -350.3 2778.4    
     #> Standard error: HC0 robust (adjusted for estimation of weights)
 
 The tables below contains the available methods in *WeightIt* for
@@ -162,47 +157,47 @@ install packages that are used.
 
 #### Binary Treatments
 
-| Method                                | `method`                                                                             |
-|---------------------------------------|--------------------------------------------------------------------------------------|
-| Binary regression PS                  | [`"glm"`](https://ngreifer.github.io/WeightIt/reference/method_glm.html)             |
-| Generalized boosted modeling PS       | [`"gbm"`](https://ngreifer.github.io/WeightIt/reference/method_gbm.html)             |
-| Covariate balancing PS                | [`"cbps"`](https://ngreifer.github.io/WeightIt/reference/method_cbps.html)           |
-| Non-Parametric covariate balancing PS | [`"npcbps"`](https://ngreifer.github.io/WeightIt/reference/method_npcbps.html)       |
-| Entropy balancing                     | [`"ebal"`](https://ngreifer.github.io/WeightIt/reference/method_ebal.html)           |
-| Inverse probability tilting           | [`"ipt"`](https://ngreifer.github.io/WeightIt/reference/method_ipt.html)             |
-| Stable balancing weights              | [`"optweight"`](https://ngreifer.github.io/WeightIt/reference/method_optweight.html) |
-| SuperLearner PS                       | [`"super"`](https://ngreifer.github.io/WeightIt/reference/method_super.html)         |
-| Bayesian additive regression trees PS | [`"bart"`](https://ngreifer.github.io/WeightIt/reference/method_bart.html)           |
-| Energy balancing                      | [`"energy"`](https://ngreifer.github.io/WeightIt/reference/method_energy.html)       |
+| Method | `method` |
+|----|----|
+| Binary regression PS | [`"glm"`](https://ngreifer.github.io/WeightIt/reference/method_glm.html) |
+| Generalized boosted modeling PS | [`"gbm"`](https://ngreifer.github.io/WeightIt/reference/method_gbm.html) |
+| Covariate balancing PS | [`"cbps"`](https://ngreifer.github.io/WeightIt/reference/method_cbps.html) |
+| Non-Parametric covariate balancing PS | [`"npcbps"`](https://ngreifer.github.io/WeightIt/reference/method_npcbps.html) |
+| Entropy balancing | [`"ebal"`](https://ngreifer.github.io/WeightIt/reference/method_ebal.html) |
+| Inverse probability tilting | [`"ipt"`](https://ngreifer.github.io/WeightIt/reference/method_ipt.html) |
+| Stable balancing weights | [`"optweight"`](https://ngreifer.github.io/WeightIt/reference/method_optweight.html) |
+| SuperLearner PS | [`"super"`](https://ngreifer.github.io/WeightIt/reference/method_super.html) |
+| Bayesian additive regression trees PS | [`"bart"`](https://ngreifer.github.io/WeightIt/reference/method_bart.html) |
+| Energy balancing | [`"energy"`](https://ngreifer.github.io/WeightIt/reference/method_energy.html) |
 
 #### Multi-Category Treatments
 
-| Method                                | `method`                                                                             |
-|---------------------------------------|--------------------------------------------------------------------------------------|
-| Multinomial regression PS             | [`"glm"`](https://ngreifer.github.io/WeightIt/reference/method_glm.html)             |
-| Generalized boosted modeling PS       | [`"gbm"`](https://ngreifer.github.io/WeightIt/reference/method_gbm.html)             |
-| Covariate balancing PS                | [`"cbps"`](https://ngreifer.github.io/WeightIt/reference/method_cbps.html)           |
-| Non-Parametric covariate balancing PS | [`"npcbps"`](https://ngreifer.github.io/WeightIt/reference/method_npcbps.html)       |
-| Entropy balancing                     | [`"ebal"`](https://ngreifer.github.io/WeightIt/reference/method_ebal.html)           |
-| Inverse probability tilting           | [`"ipt"`](https://ngreifer.github.io/WeightIt/reference/method_ipt.html)             |
-| Stable balancing weights              | [`"optweight"`](https://ngreifer.github.io/WeightIt/reference/method_optweight.html) |
-| SuperLearner PS                       | [`"super"`](https://ngreifer.github.io/WeightIt/reference/method_super.html)         |
-| Bayesian additive regression trees PS | [`"bart"`](https://ngreifer.github.io/WeightIt/reference/method_bart.html)           |
-| Energy balancing                      | [`"energy"`](https://ngreifer.github.io/WeightIt/reference/method_energy.html)       |
+| Method | `method` |
+|----|----|
+| Multinomial regression PS | [`"glm"`](https://ngreifer.github.io/WeightIt/reference/method_glm.html) |
+| Generalized boosted modeling PS | [`"gbm"`](https://ngreifer.github.io/WeightIt/reference/method_gbm.html) |
+| Covariate balancing PS | [`"cbps"`](https://ngreifer.github.io/WeightIt/reference/method_cbps.html) |
+| Non-Parametric covariate balancing PS | [`"npcbps"`](https://ngreifer.github.io/WeightIt/reference/method_npcbps.html) |
+| Entropy balancing | [`"ebal"`](https://ngreifer.github.io/WeightIt/reference/method_ebal.html) |
+| Inverse probability tilting | [`"ipt"`](https://ngreifer.github.io/WeightIt/reference/method_ipt.html) |
+| Stable balancing weights | [`"optweight"`](https://ngreifer.github.io/WeightIt/reference/method_optweight.html) |
+| SuperLearner PS | [`"super"`](https://ngreifer.github.io/WeightIt/reference/method_super.html) |
+| Bayesian additive regression trees PS | [`"bart"`](https://ngreifer.github.io/WeightIt/reference/method_bart.html) |
+| Energy balancing | [`"energy"`](https://ngreifer.github.io/WeightIt/reference/method_energy.html) |
 
 #### Continuous Treatments
 
-| Method                                 | `method`                                                                             |
-|----------------------------------------|--------------------------------------------------------------------------------------|
-| Generalized linear model GPS           | [`"glm"`](https://ngreifer.github.io/WeightIt/reference/method_glm.html)             |
-| Generalized boosted modeling GPS       | [`"gbm"`](https://ngreifer.github.io/WeightIt/reference/method_gbm.html)             |
-| Covariate balancing GPS                | [`"cbps"`](https://ngreifer.github.io/WeightIt/reference/method_cbps.html)           |
-| Non-Parametric covariate balancing GPS | [`"npcbps"`](https://ngreifer.github.io/WeightIt/reference/method_npcbps.html)       |
-| Entropy balancing                      | [`"ebal"`](https://ngreifer.github.io/WeightIt/reference/method_ebal.html)           |
-| Stable balancing weights               | [`"optweight"`](https://ngreifer.github.io/WeightIt/reference/method_optweight.html) |
-| SuperLearner GPS                       | [`"super"`](https://ngreifer.github.io/WeightIt/reference/method_super.html)         |
-| Bayesian additive regression trees GPS | [`"bart"`](https://ngreifer.github.io/WeightIt/reference/method_bart.html)           |
-| Distance covariance optimal weighting  | [`"energy"`](https://ngreifer.github.io/WeightIt/reference/method_energy.html)       |
+| Method | `method` |
+|----|----|
+| Generalized linear model GPS | [`"glm"`](https://ngreifer.github.io/WeightIt/reference/method_glm.html) |
+| Generalized boosted modeling GPS | [`"gbm"`](https://ngreifer.github.io/WeightIt/reference/method_gbm.html) |
+| Covariate balancing GPS | [`"cbps"`](https://ngreifer.github.io/WeightIt/reference/method_cbps.html) |
+| Non-Parametric covariate balancing GPS | [`"npcbps"`](https://ngreifer.github.io/WeightIt/reference/method_npcbps.html) |
+| Entropy balancing | [`"ebal"`](https://ngreifer.github.io/WeightIt/reference/method_ebal.html) |
+| Stable balancing weights | [`"optweight"`](https://ngreifer.github.io/WeightIt/reference/method_optweight.html) |
+| SuperLearner GPS | [`"super"`](https://ngreifer.github.io/WeightIt/reference/method_super.html) |
+| Bayesian additive regression trees GPS | [`"bart"`](https://ngreifer.github.io/WeightIt/reference/method_bart.html) |
+| Distance covariance optimal weighting | [`"energy"`](https://ngreifer.github.io/WeightIt/reference/method_energy.html) |
 
 In addition, *WeightIt* implements the subgroup balancing propensity
 score using the function `sbps()`. Several other tools and utilities are

_pkgdown.yml

@@ -13,6 +13,7 @@ reference:
 - contents:
   - glm_weightit
   - predict.glm_weightit
+  - anova.glm_weightit
   - glm_weightit-methods
 - title: Modify weights
 - contents: