diff --git a/NAMESPACE b/NAMESPACE
index f28ec3e..b4c6af8 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -1,12 +1,15 @@
 # Generated by roxygen2: do not edit by hand
 
 S3method(augment,feglm)
+S3method(augment,felm)
 S3method(coef,apes)
 S3method(coef,feglm)
 S3method(coef,felm)
 S3method(coef,summary.apes)
 S3method(coef,summary.feglm)
 S3method(coef,summary.felm)
+S3method(confint,feglm)
+S3method(confint,felm)
 S3method(fitted,feglm)
 S3method(fitted,felm)
 S3method(glance,feglm)
@@ -69,6 +72,7 @@ importFrom(stats,pnorm)
 importFrom(stats,poisson)
 importFrom(stats,predict)
 importFrom(stats,printCoefmat)
+importFrom(stats,qnorm)
 importFrom(stats,terms)
 importFrom(stats,vcov)
 importFrom(utils,combn)
diff --git a/R/broom.R b/R/broom.R
new file mode 100644
index 0000000..30b175d
--- /dev/null
+++ b/R/broom.R
@@ -0,0 +1,76 @@
+#' Augment method for fepoisson (Broom)
+#'
+#' @param x A fitted model object.
+#' @param newdata Optional argument to use data different from the data used to fit
+#'  the model.
+#' @param ... Additional arguments passed to the method.
+#'
+#' @return A tibble with the input data and additional columns for the fitted
+#' values and residuals.
+#'
+#' @rdname broom
+#'
+#' @examples
+#' if (require("broom")) {
+#'  set.seed(123)
+#'  trade_2006 <- trade_panel[trade_panel$year == 2006, ]
+#'  trade_2006 <- trade_2006[sample(nrow(trade_2006), 1000), ]
+#'
+#'  mod <- fepoisson(
+#'   trade ~ log_dist + lang + cntg + clny | exp_year + imp_year,
+#'   trade_2006
+#'  )
+#'
+#'  broom::augment(mod)
+#' }
+
+#' Glance method for fepoisson (Broom)
+#'
+#' @param x A fitted model object.
+#' @param ... Additional arguments passed to the method.
+#'
+#' @return A tibble with the deviance, null deviance, and the number of
+#' observations (full rows, missing values, and perfectly classified).
+#'
+#' @rdname broom
+#'
+#' @examples
+#' if (require("broom")) {
+#'   set.seed(123)
+#'   trade_2006 <- trade_panel[trade_panel$year == 2006, ]
+#'   trade_2006 <- trade_2006[sample(nrow(trade_2006), 1000), ]
+#'
+#'   mod <- fepoisson(
+#'     trade ~ log_dist + lang + cntg + clny | exp_year + imp_year,
+#'     trade_2006
+#'   )
+#'
+#'   broom::glance(mod)
+#' }
+
+#' Tidy method for fepoisson (Broom)
+#'
+#' @param x A fitted model object.
+#' @param conf.int Logical indicating whether to include the confidence interval.
+#' @param conf.level The confidence level for the confidence interval.
+#' @param ... Additional arguments passed to the method.
+#'
+#' @return A tibble with the estimated coefficients, standard errors, test
+#' statistics, p-values, and optionally the lower and upper bounds of the
+#' confidence interval.
+#'
+#' @rdname broom
+#'
+#' @examples
+#' if (require("broom")) {
+#'   set.seed(123)
+#'   trade_2006 <- trade_panel[trade_panel$year == 2006, ]
+#'   trade_2006 <- trade_2006[sample(nrow(trade_2006), 1000), ]
+#'
+#'   mod <- fepoisson(
+#'     trade ~ log_dist + lang + cntg + clny | exp_year + imp_year,
+#'     trade_2006
+#'   )
+#'
+#'   broom::tidy(mod)
+#' }
diff --git a/R/capybara-package.R b/R/capybara-package.R
index b96fb65..2628e6b 100644
--- a/R/capybara-package.R
+++ b/R/capybara-package.R
@@ -19,7 +19,7 @@
 #' @importFrom MASS negative.binomial theta.ml
 #' @importFrom rlang sym :=
 #' @importFrom stats as.formula fitted.values gaussian model.matrix na.omit
-#'  pnorm poisson predict printCoefmat terms vcov
+#'  pnorm poisson predict printCoefmat qnorm terms vcov
 #' @importFrom utils combn
 #' @useDynLib capybara, .registration = TRUE
 "_PACKAGE"
diff --git a/R/generics_confint.R b/R/generics_confint.R
index 8b19abb..ff5ff07 100644
--- a/R/generics_confint.R
+++ b/R/generics_confint.R
@@ -15,9 +15,15 @@ confint.feglm <- function(object, parm, level = 0.95, ...) {
     conf.high = res[, "estimate"] + z * res[, "std.error"]
   )
 
-  colnames(conf.int) <- paste(100 * (c(0, 1) + c(1, -1) * (1 - conf.level) / 2),
+  colnames(conf.int) <- paste(100 * (c(0, 1) + c(1, -1) * (1 - level) / 2),
     "%")
 
   # Return the confidence intervals
   conf.int
 }
+
+#' @export
+#' @noRd
+confint.felm <- function(object, parm, level = 0.95, ...) {
+  confint.feglm(object, parm, level, ...)
+}
diff --git a/R/srr-stats-standards.R b/R/srr-stats-standards.R
index 3dbbd8f..7fac180 100644
--- a/R/srr-stats-standards.R
+++ b/R/srr-stats-standards.R
@@ -87,9 +87,8 @@
 #' @srrstats {RE3.2} *Ensure that convergence thresholds have sensible default values, demonstrated through explicit documentation.*
 #' @srrstats {RE3.3} *Allow explicit setting of convergence thresholds, unless reasons against doing so are explicitly documented.*
 #' @srrstats {RE4.0} *Regression Software should return some form of "model" object, generally through using or modifying existing class structures for model objects (such as `lm`, `glm`, or model objects from other packages), or creating a new class of model objects.*
-#' @srrstatsTODO {RE4.1} *Regression Software may enable an ability to generate a model object without actually fitting values. This may be useful for controlling batch processing of computationally intensive fitting algorithms.*
 #' @srrstats {RE4.2} *Model coefficients (via `coef()` / `coefficients()`)*
-#' @srrstatsTODO {RE4.3} *Confidence intervals on those coefficients (via `confint()`)*
+#' @srrstats {RE4.3} *Confidence intervals on those coefficients (via `confint()`)*
 #' @srrstats {RE4.4} *The specification of the model, generally as a formula (via `formula()`)*
 #' @srrstats {RE4.5} *Numbers of observations submitted to model (via `nobs()`)*
 #' @srrstats {RE4.6} *The variance-covariance matrix of the model parameters (via `vcov()`)*
@@ -102,10 +101,6 @@
 #' @srrstats {RE4.13} *Predictor variables, and associated "metadata" where applicable.*
 #' @srrstats {RE4.18} *Regression Software may also implement `summary` methods for model objects, and in particular should implement distinct `summary` methods for any cases in which calculation of summary statistics is computationally non-trivial (for example, for bootstrapped estimates of confidence intervals).*
 #' @srrstats {RE5.0} *Scaling relationships between sizes of input data (numbers of observations, with potential extension to numbers of variables/columns) and speed of algorithm.*
-#' @srrstatsTODO {RE6.0} *Model objects returned by Regression Software (see* **RE4***) should have default `plot` methods, either through explicit implementation, extension of methods for existing model objects, or through ensuring default methods work appropriately.*
-#' @srrstatsTODO {RE6.1} *Where the default `plot` method is **NOT** a generic `plot` method dispatched on the class of return objects (that is, through an S3-type `plot.<myclass>` function or equivalent), that method dispatch (or equivalent) should nevertheless exist in order to explicitly direct users to the appropriate function.*
-#' @srrstatsTODO {RE6.2} *The default `plot` method should produce a plot of the `fitted` values of the model, with optional visualisation of confidence intervals or equivalent.*
-#' @srrstatsTODO {RE6.3} *Where a model object is used to generate a forecast (for example, through a `predict()` method), the default `plot` method should provide clear visual distinction between modelled (interpolated) and forecast (extrapolated) values.*
 #' @srrstats {RE7.0} *Tests with noiseless, exact relationships between predictor (independent) data.*
 #' @srrstats {RE7.0a} In particular, these tests should confirm ability to reject perfectly noiseless input data.
 #' @srrstats {RE7.1} *Tests with noiseless, exact relationships between predictor (independent) and response (dependent) data.*
@@ -130,9 +125,14 @@ NULL
 #' @srrstatsNA {G5.9b} *Running under different random seeds or initial conditions does not meaningfully change results*
 #' @srrstatsNA {G5.11a} *When any downloads of additional data necessary for extended tests fail, the tests themselves should not fail, rather be skipped and implicitly succeed with an appropriate diagnostic message.*
 #' @srrstatsNA {RE2.2} *Regression Software should provide different options for processing missing values in predictor and response data. For example, it should be possible to fit a model with no missing predictor data in order to generate values for all associated response points, even where submitted response values may be missing.*
+#' @srrstatsNA {RE4.1} *Regression Software may enable an ability to generate a model object without actually fitting values. This may be useful for controlling batch processing of computationally intensive fitting algorithms.*
 #' @srrstatsNA {RE4.16} *Regression Software which models distinct responses for different categorical groups should include the ability to submit new groups to `predict()` methods.*
 #' @srrstatsNA {RE4.14} *Where possible, values should also be provided for extrapolation or forecast *errors*.*
 #' @srrstatsNA {RE4.15} *Sufficient documentation and/or testing should be provided to demonstrate that forecast errors, confidence intervals, or equivalent values increase with forecast horizons.*
 #' @srrstatsNA {RE4.17} *Model objects returned by Regression Software should implement or appropriately extend a default `print` method which provides an on-screen summary of model (input) parameters and (output) coefficients.*
+#' @srrstatsNA {RE6.0} *Model objects returned by Regression Software (see* **RE4***) should have default `plot` methods, either through explicit implementation, extension of methods for existing model objects, or through ensuring default methods work appropriately.*
+#' @srrstatsNA {RE6.1} *Where the default `plot` method is **NOT** a generic `plot` method dispatched on the class of return objects (that is, through an S3-type `plot.<myclass>` function or equivalent), that method dispatch (or equivalent) should nevertheless exist in order to explicitly direct users to the appropriate function.*
+#' @srrstatsNA {RE6.2} *The default `plot` method should produce a plot of the `fitted` values of the model, with optional visualisation of confidence intervals or equivalent.*
+#' @srrstatsNA {RE6.3} *Where a model object is used to generate a forecast (for example, through a `predict()` method), the default `plot` method should provide clear visual distinction between modelled (interpolated) and forecast (extrapolated) values.*
 #' @noRd
 NULL