diff --git a/DESCRIPTION b/DESCRIPTION
index 181fe6de3..b642a6bc5 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -573,7 +573,6 @@ Suggests:
     Lahman,
     lavaan,
     leaps,
-    lfe,
     lm.beta,
     lme4,
     lmodel2,
@@ -662,7 +661,6 @@ Collate:
     'ks-tidiers.R'
     'lavaan-tidiers.R'
     'leaps.R'
-    'lfe-tidiers.R'
     'list-irlba.R'
     'list-optim-tidiers.R'
     'list-svd-tidiers.R'
diff --git a/NAMESPACE b/NAMESPACE
index d7266bc3e..d784d4338 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -12,7 +12,6 @@ S3method(augment,decomposed.ts)
 S3method(augment,default)
 S3method(augment,drc)
 S3method(augment,factanal)
-S3method(augment,felm)
 S3method(augment,fixest)
 S3method(augment,gam)
 S3method(augment,glm)
@@ -75,7 +74,6 @@ S3method(glance,drc)
 S3method(glance,durbinWatsonTest)
 S3method(glance,ergm)
 S3method(glance,factanal)
-S3method(glance,felm)
 S3method(glance,fitdistr)
 S3method(glance,fixest)
 S3method(glance,gam)
@@ -182,7 +180,6 @@ S3method(tidy,emmGrid)
 S3method(tidy,epi.2by2)
 S3method(tidy,ergm)
 S3method(tidy,factanal)
-S3method(tidy,felm)
 S3method(tidy,fitdistr)
 S3method(tidy,fixest)
 S3method(tidy,ftable)
diff --git a/NEWS.md b/NEWS.md
index 368241ff5..9d0e5f10a 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -3,6 +3,7 @@
 To be release as broom 0.7.9.
 
 * Fixed confidence intervals in `tidy.crr()`, which were previously exponentiated when `exponentiate = FALSE` (`#1023` by `@leejasme`)
+* Deprecate tidiers for `felm` objects from the `lfe` package, which was again archived from CRAN.
 
 # broom 0.7.8
 
diff --git a/R/lfe-tidiers.R b/R/lfe-tidiers.R
deleted file mode 100644
index af5ecc1c3..000000000
--- a/R/lfe-tidiers.R
+++ /dev/null
@@ -1,243 +0,0 @@
-#' @templateVar class felm
-#' @template title_desc_tidy
-#'
-#' @param x A `felm` object returned from [lfe::felm()].
-#' @template param_confint
-#' @param fe Logical indicating whether or not to include estimates of
-#'   fixed effects. Defaults to `FALSE`.
-#' @param se.type Character indicating the type of standard errors. Defaults to
-#'   using those of the underlying felm() model object, e.g. clustered errors
-#'   for models that were provided a cluster specification. Users can override
-#'   these defaults by specifying an appropriate alternative: "iid" (for 
-#'   homoskedastic errors), "robust" (for Eicker-Huber-White robust errors), or
-#'   "cluster" (for clustered standard errors; if the model object supports it).
-#' @template param_unused_dots
-#'
-#' @evalRd return_tidy(regression = TRUE)
-#'
-#' @examples
-#'
-#' library(lfe)
-#'
-#' # Use built-in "airquality" dataset
-#' head(airquality)
-#'
-#' # No FEs; same as lm()
-#' est0 <- felm(Ozone ~ Temp + Wind + Solar.R, airquality)
-#' tidy(est0)
-#' augment(est0)
-#' 
-#' # Add month fixed effects
-#' est1 <- felm(Ozone ~ Temp + Wind + Solar.R  | Month, airquality)
-#' tidy(est1)
-#' tidy(est1, fe = TRUE)
-#' augment(est1)
-#' glance(est1)
-#'
-#' # The "se.type" argument can be used to switch out different standard errors 
-#' # types on the fly. In turn, this can be useful exploring the effect of 
-#' # different error structures on model inference.
-#' tidy(est1, se.type = "iid")
-#' tidy(est1, se.type = "robust")
-#' 
-#' # Add clustered SEs (also by month)
-#' est2 <- felm(Ozone ~ Temp + Wind + Solar.R  | Month | 0 | Month, airquality)
-#' tidy(est2, conf.int = TRUE) 
-#' tidy(est2, conf.int = TRUE, se.type = "cluster")
-#' tidy(est2, conf.int = TRUE, se.type = "robust")
-#' tidy(est2, conf.int = TRUE, se.type = "iid")
-#' @export
-#' @aliases felm_tidiers lfe_tidiers
-#' @family felm tidiers
-#' @seealso [tidy()], [lfe::felm()]
-tidy.felm <- function(x, conf.int = FALSE, conf.level = .95, fe = FALSE, se.type = c("default", "iid", "robust", "cluster"), ...) {
-  has_multi_response <- length(x$lhs) > 1
-  
-  # warn users about deprecated "robust" argument
-  dots <- list(...)
-  if (!is.null(dots$robust)) {
-    warning('\nThe "robust" argument has been deprecated in tidy.felm and will be ignored. Please use the "se.type" argument instead.\n')
-  }
-  
-  # match SE args
-  se.type <- match.arg(se.type)
-  if (se.type == "default") {
-    se.type <- NULL
-  }
-  
-  # get "robust" logical to pass on to summary.lfe
-  if (is.null(se.type)) {
-    robust <- !is.null(x$clustervar) 
-  } else if (se.type == 'iid') {
-    robust <- FALSE
-  } else {
-    # catch potential user error, asking for clusters where none exist
-    if (se.type == "cluster" && is.null(x$clustervar)) {
-       warning("Clustered SEs requested, but weren't calculated in underlying model object. Reverting to default SEs.\n")
-       se.type <- NULL
-    }
-    
-    robust <- TRUE
-  }
-  
-  nn <- c("estimate", "std.error", "statistic", "p.value")
-  if (has_multi_response) {
-    ret <- map_df(x$lhs, function(y) {
-      stats::coef(summary(x, lhs = y, robust = robust)) %>%
-        as_tidy_tibble(new_names = nn) %>%
-        mutate(response = y)
-    }) %>%
-      select(response, dplyr::everything())
-  } else {
-    ret <- as_tidy_tibble(
-      stats::coef(summary(x, robust = robust)),
-      new_names = nn
-    )
-  }
-  
-  # Catch edge case where users specify "robust" SEs on felm() object that
-  # contains clusters. Reason: Somewhat confusingly, summary.felm(robust = TRUE) 
-  # reports clustered SEs even though robust SEs are available. In contrast,
-  # confint.felm distinguishes between robust and clustered SEs regardless
-  # of the underlying model. See also: https://github.com/sgaure/lfe/pull/17/files
-  if (!is.null(se.type)) {
-    if (se.type == "robust" && !is.null(x$clustervar)) {
-      ret$std.error <- x$rse
-      ret$statistic <- x$rtval
-      ret$p.value <- x$rpval
-    }
-  }
-  
-  
-  if (conf.int) {
-    if (has_multi_response) {
-      ci <- map_df(x$lhs, function(y) {
-        broom_confint_terms(x, level = conf.level, type = NULL, lhs = y) %>% 
-          mutate(response=y)
-      })
-      ret <- dplyr::left_join(ret, ci, by = c("response", "term"))
-    } else {
-      ci <- broom_confint_terms(x, level = conf.level, type = se.type)
-      ret <- dplyr::left_join(ret, ci, by = "term")
-    }
-  }
-  
-  if (fe) {
-    ret <- mutate(ret, N = NA, comp = NA)
-    
-    nn <- c("estimate", "std.error", "N", "comp")
-    ret_fe_prep <- lfe::getfe(x, se = TRUE, bN = 100) %>%
-      tibble::rownames_to_column(var = "term") %>%
-      # effect and se are multiple if multiple y
-      select(term, contains("effect"), contains("se"), obs, comp) %>%
-      rename(N = obs)
-    
-    if (has_multi_response) {
-      ret_fe_prep <- ret_fe_prep %>%
-        tidyr::pivot_longer(
-          cols = c(
-            starts_with("effect."),
-            starts_with("se.")
-          ),
-          names_to = "stat_resp",
-          values_to = "value"
-        ) %>%
-        tidyr::separate(
-          col = "stat_resp",
-          c("stat", "response"),
-          sep = "\\."
-        ) %>%
-        tidyr::pivot_wider(
-          id_cols = c(term, N, comp, response),
-          names_from = stat,
-          values_from = value
-        ) %>%
-        dplyr::arrange(term) %>%
-        as.data.frame()
-    }
-    ret_fe <- ret_fe_prep %>%
-      rename(estimate = effect, std.error = se) %>%
-      select(contains("response"), dplyr::everything()) %>%
-      mutate(statistic = estimate / std.error) %>%
-      mutate(p.value = 2 * (1 - stats::pt(statistic, df = N)))
-    
-    if (conf.int) {
-      crit_val_low <- stats::qnorm(1 - (1 - conf.level) / 2)
-      crit_val_high <- stats::qnorm(1 - (1 - conf.level) / 2)
-      
-      ret_fe <- ret_fe %>%
-        mutate(
-          conf.low = estimate - crit_val_low * std.error,
-          conf.high = estimate + crit_val_high * std.error
-        )
-    }
-    ret <- rbind(ret, ret_fe)
-  }
-  as_tibble(ret)
-}
-
-#' @templateVar class felm
-#' @template title_desc_augment
-#'
-#' @inherit tidy.felm params examples
-#' @template param_data
-#'
-#' @evalRd return_augment()
-#'
-#' @export
-#' @family felm tidiers
-#' @seealso [augment()], [lfe::felm()]
-augment.felm <- function(x, data = model.frame(x), ...) {
-  has_multi_response <- length(x$lhs) > 1
-
-  if (has_multi_response) {
-    stop(
-      "Augment does not support linear models with multiple responses.",
-      call. = FALSE
-    )
-  }
-  df <- as_augment_tibble(data)
-  mutate(df, .fitted = as.vector(x$fitted.values), .resid = as.vector(x$residuals))
-}
-
-#' @templateVar class felm
-#' @template title_desc_glance
-#'
-#' @inherit tidy.felm params examples
-#'
-#' @evalRd return_glance(
-#'   "r.squared",
-#'   "adj.r.squared",
-#'   "sigma",
-#'   "statistic",
-#'   "p.value",
-#'   "df",
-#'   "df.residual",
-#'   "nobs"
-#' )
-#'
-#' @export
-glance.felm <- function(x, ...) {
-  has_multi_response <- length(x$lhs) > 1
-
-  if (has_multi_response) {
-    stop(
-      "Glance does not support linear models with multiple responses.",
-      call. = FALSE
-    )
-  }
-  
-  s <- summary(x)
-  
-  as_glance_tibble(
-    r.squared = s$r2,
-    adj.r.squared = s$r2adj,
-    sigma = s$rse,
-    statistic = s$fstat,
-    p.value = unname(s$pval),
-    df = s$df[1],
-    df.residual = s$rdf,
-    nobs = stats::nobs(x),
-    na_types = "rrrrriii"
-  )
-}
diff --git a/man/augment.felm.Rd b/man/augment.felm.Rd
deleted file mode 100644
index 5bae74e60..000000000
--- a/man/augment.felm.Rd
+++ /dev/null
@@ -1,115 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/lfe-tidiers.R
-\name{augment.felm}
-\alias{augment.felm}
-\title{Augment data with information from a(n) felm object}
-\usage{
-\method{augment}{felm}(x, data = model.frame(x), ...)
-}
-\arguments{
-\item{x}{A \code{felm} object returned from \code{\link[lfe:felm]{lfe::felm()}}.}
-
-\item{data}{A \link[base:data.frame]{base::data.frame} or \code{\link[tibble:tibble]{tibble::tibble()}} containing the original
-data that was used to produce the object \code{x}. Defaults to
-\code{stats::model.frame(x)} so that \code{augment(my_fit)} returns the augmented
-original data. \strong{Do not} pass new data to the \code{data} argument.
-Augment will report information such as influence and cooks distance for
-data passed to the \code{data} argument. These measures are only defined for
-the original training data.}
-
-\item{...}{Additional arguments. Not used. Needed to match generic
-signature only. \strong{Cautionary note:} Misspelled arguments will be
-absorbed in \code{...}, where they will be ignored. If the misspelled
-argument has a default value, the default value will be used.
-For example, if you pass \code{conf.lvel = 0.9}, all computation will
-proceed using \code{conf.level = 0.95}. Additionally, if you pass
-\code{newdata = my_tibble} to an \code{\link[=augment]{augment()}} method that does not
-accept a \code{newdata} argument, it will use the default value for
-the \code{data} argument.}
-}
-\description{
-Augment accepts a model object and a dataset and adds
-information about each observation in the dataset. Most commonly, this
-includes predicted values in the \code{.fitted} column, residuals in the
-\code{.resid} column, and standard errors for the fitted values in a \code{.se.fit}
-column. New columns always begin with a \code{.} prefix to avoid overwriting
-columns in the original dataset.
-
-Users may pass data to augment via either the \code{data} argument or the
-\code{newdata} argument. If the user passes data to the \code{data} argument,
-it \strong{must} be exactly the data that was used to fit the model
-object. Pass datasets to \code{newdata} to augment data that was not used
-during model fitting. This still requires that at least all predictor
-variable columns used to fit the model are present. If the original outcome
-variable used to fit the model is not included in \code{newdata}, then no
-\code{.resid} column will be included in the output.
-
-Augment will often behave differently depending on whether \code{data} or
-\code{newdata} is given. This is because there is often information
-associated with training observations (such as influences or related)
-measures that is not meaningfully defined for new observations.
-
-For convenience, many augment methods provide default \code{data} arguments,
-so that \code{augment(fit)} will return the augmented training data. In these
-cases, augment tries to reconstruct the original data based on the model
-object with varying degrees of success.
-
-The augmented dataset is always returned as a \link[tibble:tibble]{tibble::tibble} with the
-\strong{same number of rows} as the passed dataset. This means that the
-passed data must be coercible to a tibble. At this time, tibbles do not
-support matrix-columns. This means you should not specify a matrix
-of covariates in a model formula during the original model fitting
-process, and that \code{\link[splines:ns]{splines::ns()}}, \code{\link[stats:poly]{stats::poly()}} and
-\code{\link[survival:Surv]{survival::Surv()}} objects are not supported in input data. If you
-encounter errors, try explicitly passing a tibble, or fitting the original
-model on data in a tibble.
-
-We are in the process of defining behaviors for models fit with various
-\code{na.action} arguments, but make no guarantees about behavior when data is
-missing at this time.
-}
-\examples{
-
-library(lfe)
-
-# Use built-in "airquality" dataset
-head(airquality)
-
-# No FEs; same as lm()
-est0 <- felm(Ozone ~ Temp + Wind + Solar.R, airquality)
-tidy(est0)
-augment(est0)
-
-# Add month fixed effects
-est1 <- felm(Ozone ~ Temp + Wind + Solar.R  | Month, airquality)
-tidy(est1)
-tidy(est1, fe = TRUE)
-augment(est1)
-glance(est1)
-
-# The "se.type" argument can be used to switch out different standard errors 
-# types on the fly. In turn, this can be useful exploring the effect of 
-# different error structures on model inference.
-tidy(est1, se.type = "iid")
-tidy(est1, se.type = "robust")
-
-# Add clustered SEs (also by month)
-est2 <- felm(Ozone ~ Temp + Wind + Solar.R  | Month | 0 | Month, airquality)
-tidy(est2, conf.int = TRUE) 
-tidy(est2, conf.int = TRUE, se.type = "cluster")
-tidy(est2, conf.int = TRUE, se.type = "robust")
-tidy(est2, conf.int = TRUE, se.type = "iid")
-}
-\seealso{
-\code{\link[=augment]{augment()}}, \code{\link[lfe:felm]{lfe::felm()}}
-
-Other felm tidiers: 
-\code{\link{tidy.felm}()}
-}
-\concept{felm tidiers}
-\value{
-A \code{\link[tibble:tibble]{tibble::tibble()}} with columns:
-  \item{.fitted}{Fitted or predicted value.}
-  \item{.resid}{The difference between observed and fitted values.}
-
-}
diff --git a/man/broom.Rd b/man/broom.Rd
index 59efc4dd8..96123a414 100644
--- a/man/broom.Rd
+++ b/man/broom.Rd
@@ -163,6 +163,7 @@ Other contributors:
   \item R. Willem Vervoort \email{Willemvervoort@gmail.com} [contributor]
   \item Brenton M. Wiernik \email{brenton@wiernik.org} [contributor]
   \item Josh Yamamoto \email{joshuayamamoto5@gmail.com} [contributor]
+  \item Jasme Lee [contributor]
 }
 
 }
diff --git a/man/glance.felm.Rd b/man/glance.felm.Rd
deleted file mode 100644
index 6522423e4..000000000
--- a/man/glance.felm.Rd
+++ /dev/null
@@ -1,85 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/lfe-tidiers.R
-\name{glance.felm}
-\alias{glance.felm}
-\title{Glance at a(n) felm object}
-\usage{
-\method{glance}{felm}(x, ...)
-}
-\arguments{
-\item{x}{A \code{felm} object returned from \code{\link[lfe:felm]{lfe::felm()}}.}
-
-\item{...}{Additional arguments. Not used. Needed to match generic
-signature only. \strong{Cautionary note:} Misspelled arguments will be
-absorbed in \code{...}, where they will be ignored. If the misspelled
-argument has a default value, the default value will be used.
-For example, if you pass \code{conf.lvel = 0.9}, all computation will
-proceed using \code{conf.level = 0.95}. Additionally, if you pass
-\code{newdata = my_tibble} to an \code{\link[=augment]{augment()}} method that does not
-accept a \code{newdata} argument, it will use the default value for
-the \code{data} argument.}
-}
-\description{
-Glance accepts a model object and returns a \code{\link[tibble:tibble]{tibble::tibble()}}
-with exactly one row of model summaries. The summaries are typically
-goodness of fit measures, p-values for hypothesis tests on residuals,
-or model convergence information.
-
-Glance never returns information from the original call to the modeling
-function. This includes the name of the modeling function or any
-arguments passed to the modeling function.
-
-Glance does not calculate summary measures. Rather, it farms out these
-computations to appropriate methods and gathers the results together.
-Sometimes a goodness of fit measure will be undefined. In these cases
-the measure will be reported as \code{NA}.
-
-Glance returns the same number of columns regardless of whether the
-model matrix is rank-deficient or not. If so, entries in columns
-that no longer have a well-defined value are filled in with an \code{NA}
-of the appropriate type.
-}
-\examples{
-
-library(lfe)
-
-# Use built-in "airquality" dataset
-head(airquality)
-
-# No FEs; same as lm()
-est0 <- felm(Ozone ~ Temp + Wind + Solar.R, airquality)
-tidy(est0)
-augment(est0)
-
-# Add month fixed effects
-est1 <- felm(Ozone ~ Temp + Wind + Solar.R  | Month, airquality)
-tidy(est1)
-tidy(est1, fe = TRUE)
-augment(est1)
-glance(est1)
-
-# The "se.type" argument can be used to switch out different standard errors 
-# types on the fly. In turn, this can be useful exploring the effect of 
-# different error structures on model inference.
-tidy(est1, se.type = "iid")
-tidy(est1, se.type = "robust")
-
-# Add clustered SEs (also by month)
-est2 <- felm(Ozone ~ Temp + Wind + Solar.R  | Month | 0 | Month, airquality)
-tidy(est2, conf.int = TRUE) 
-tidy(est2, conf.int = TRUE, se.type = "cluster")
-tidy(est2, conf.int = TRUE, se.type = "robust")
-tidy(est2, conf.int = TRUE, se.type = "iid")
-}
-\value{
-A \code{\link[tibble:tibble]{tibble::tibble()}} with exactly one row and columns:
-  \item{adj.r.squared}{Adjusted R squared statistic, which is like the R squared statistic except taking degrees of freedom into account.}
-  \item{df}{Degrees of freedom used by the model.}
-  \item{df.residual}{Residual degrees of freedom.}
-  \item{nobs}{Number of observations used.}
-  \item{p.value}{P-value corresponding to the test statistic.}
-  \item{r.squared}{R squared statistic, or the percent of variation explained by the model. Also known as the coefficient of determination.}
-  \item{sigma}{Estimated standard error of the residuals.}
-  \item{statistic}{Test statistic.}
-
-}
diff --git a/man/tidy.felm.Rd b/man/tidy.felm.Rd
deleted file mode 100644
index 503ae1013..000000000
--- a/man/tidy.felm.Rd
+++ /dev/null
@@ -1,105 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/lfe-tidiers.R
-\name{tidy.felm}
-\alias{tidy.felm}
-\alias{felm_tidiers}
-\alias{lfe_tidiers}
-\title{Tidy a(n) felm object}
-\usage{
-\method{tidy}{felm}(
-  x,
-  conf.int = FALSE,
-  conf.level = 0.95,
-  fe = FALSE,
-  se.type = c("default", "iid", "robust", "cluster"),
-  ...
-)
-}
-\arguments{
-\item{x}{A \code{felm} object returned from \code{\link[lfe:felm]{lfe::felm()}}.}
-
-\item{conf.int}{Logical indicating whether or not to include a confidence
-interval in the tidied output. Defaults to \code{FALSE}.}
-
-\item{conf.level}{The confidence level to use for the confidence interval
-if \code{conf.int = TRUE}. Must be strictly greater than 0 and less than 1.
-Defaults to 0.95, which corresponds to a 95 percent confidence interval.}
-
-\item{fe}{Logical indicating whether or not to include estimates of
-fixed effects. Defaults to \code{FALSE}.}
-
-\item{se.type}{Character indicating the type of standard errors. Defaults to
-using those of the underlying felm() model object, e.g. clustered errors
-for models that were provided a cluster specification. Users can override
-these defaults by specifying an appropriate alternative: "iid" (for
-homoskedastic errors), "robust" (for Eicker-Huber-White robust errors), or
-"cluster" (for clustered standard errors; if the model object supports it).}
-
-\item{...}{Additional arguments. Not used. Needed to match generic
-signature only. \strong{Cautionary note:} Misspelled arguments will be
-absorbed in \code{...}, where they will be ignored. If the misspelled
-argument has a default value, the default value will be used.
-For example, if you pass \code{conf.lvel = 0.9}, all computation will
-proceed using \code{conf.level = 0.95}. Additionally, if you pass
-\code{newdata = my_tibble} to an \code{\link[=augment]{augment()}} method that does not
-accept a \code{newdata} argument, it will use the default value for
-the \code{data} argument.}
-}
-\description{
-Tidy summarizes information about the components of a model.
-A model component might be a single term in a regression, a single
-hypothesis, a cluster, or a class. Exactly what tidy considers to be a
-model component varies across models but is usually self-evident.
-If a model has several distinct types of components, you will need to
-specify which components to return.
-}
-\examples{
-
-library(lfe)
-
-# Use built-in "airquality" dataset
-head(airquality)
-
-# No FEs; same as lm()
-est0 <- felm(Ozone ~ Temp + Wind + Solar.R, airquality)
-tidy(est0)
-augment(est0)
-
-# Add month fixed effects
-est1 <- felm(Ozone ~ Temp + Wind + Solar.R  | Month, airquality)
-tidy(est1)
-tidy(est1, fe = TRUE)
-augment(est1)
-glance(est1)
-
-# The "se.type" argument can be used to switch out different standard errors 
-# types on the fly. In turn, this can be useful exploring the effect of 
-# different error structures on model inference.
-tidy(est1, se.type = "iid")
-tidy(est1, se.type = "robust")
-
-# Add clustered SEs (also by month)
-est2 <- felm(Ozone ~ Temp + Wind + Solar.R  | Month | 0 | Month, airquality)
-tidy(est2, conf.int = TRUE) 
-tidy(est2, conf.int = TRUE, se.type = "cluster")
-tidy(est2, conf.int = TRUE, se.type = "robust")
-tidy(est2, conf.int = TRUE, se.type = "iid")
-}
-\seealso{
-\code{\link[=tidy]{tidy()}}, \code{\link[lfe:felm]{lfe::felm()}}
-
-Other felm tidiers: 
-\code{\link{augment.felm}()}
-}
-\concept{felm tidiers}
-\value{
-A \code{\link[tibble:tibble]{tibble::tibble()}} with columns:
-  \item{conf.high}{Upper bound on the confidence interval for the estimate.}
-  \item{conf.low}{Lower bound on the confidence interval for the estimate.}
-  \item{estimate}{The estimated value of the regression term.}
-  \item{p.value}{The two-sided p-value associated with the observed statistic.}
-  \item{statistic}{The value of a T-statistic to use in a hypothesis that the regression term is non-zero.}
-  \item{std.error}{The standard error of the regression term.}
-  \item{term}{The name of the regression term.}
-
-}
diff --git a/tests/testthat/test-lfe.R b/tests/testthat/test-lfe.R
deleted file mode 100644
index 5f07dabd8..000000000
--- a/tests/testthat/test-lfe.R
+++ /dev/null
@@ -1,130 +0,0 @@
-context("lfe")
-
-skip_on_cran()
-
-skip_if_not_installed("modeltests")
-library(modeltests)
-
-skip_if_not_installed("lfe")
-
-set.seed(27)
-n <- 100
-df <- data.frame(
-  id = sample(5, n, TRUE),
-  v1 = sample(5, n, TRUE),
-  v2 = sample(1e6, n, TRUE),
-  v3 = sample(round(runif(100, max = 100), 4), n, TRUE),
-  v4 = sample(round(runif(100, max = 100), 4), n, TRUE)
-)
-
-# no FE or clus
-fit <- lfe::felm(v2 ~ v3, df)
-# with FE
-fit2 <- lfe::felm(v2 ~ v3 | id + v1, df, na.action = na.exclude)
-
-# with clus
-fit3 <- lfe::felm(v2 ~ v3 | 0 | 0 | id + v1, df, na.action = na.exclude)
-
-## with multiple outcomes
-fit_multi <- lfe::felm(v1 + v2 ~ v3 , df)
-fit_Y2 <- lfe::felm(v1 ~ v3 , df)
-
-form <- v2 ~ v4
-fit_form <- lfe::felm(form, df) # part of a regression test
-
-test_that("felm tidier arguments", {
-  check_arguments(tidy.felm)
-  check_arguments(glance.felm)
-  check_arguments(augment.felm)
-})
-
-test_that("tidy.felm", {
-  td1 <- tidy(fit)
-  td2 <- tidy(fit2, conf.int = TRUE, fe = TRUE, fe.error = FALSE)
-  td3 <- tidy(fit2, conf.int = TRUE, fe = TRUE)
-  td4 <- tidy(fit_form)
-  td5 <- tidy(fit, se = "robust")
-  td6 <- tidy(fit2, se = "robust")
-  td7 <- tidy(fit2, se = "robust", fe = TRUE)
-  td8 <- tidy(fit3)
-  td9 <- tidy(fit3, se = "iid")
-  
-  
-  td_multi <- tidy(fit_multi)
-  td_multi_CI <- tidy(fit_multi, conf.int = TRUE)
-  
-  check_tidy_output(td1)
-  check_tidy_output(td2)
-  check_tidy_output(td3)
-  check_tidy_output(td4)
-  check_tidy_output(td5)
-  check_tidy_output(td6)
-  check_tidy_output(td7)
-  check_tidy_output(td8)
-  check_tidy_output(td9)
-  check_tidy_output(td_multi)
-  check_tidy_output(td_multi_CI)
-  
-  check_dims(td1, 2, 5)
-  check_dims(td_multi_CI, 4, 8)
-  
-  expect_equal(tidy(fit_multi)[3:4, -1],
-               tidy(fit))
-  expect_equal(tidy(fit_multi, conf.int = TRUE)[3:4, -1],
-               tidy(fit, conf.int = TRUE))
-  expect_equal(tidy(fit_multi, conf.int = TRUE)[1:2, -1],
-               tidy(fit_Y2, conf.int = TRUE))
-  
-  expect_equal(dplyr::pull(td5, std.error),
-               as.numeric(lfe:::summary.felm(fit, robust = TRUE)$coef[, "Robust s.e"]))
-  expect_equal(dplyr::pull(td6, std.error),
-               as.numeric(lfe:::summary.felm(fit2, robust = TRUE)$coef[, "Robust s.e"]))
-  expect_equal(dplyr::pull(td8, std.error),
-               as.numeric(lfe:::summary.felm(fit3)$coef[, "Cluster s.e."]))
-  expect_equal(dplyr::pull(td9, std.error),
-               as.numeric(lfe:::summary.felm(fit3, robust = FALSE)$coef[, "Std. Error"]))
-
-  # check for deprecation warning from 0.7.0.9001
-  expect_warning(
-    tidy(fit, robust = TRUE),
-    '"robust" argument has been deprecated'
-  )
-})
-
-test_that("glance.felm", {
-  gl <- glance(fit)
-  gl2 <- glance(fit2)
-
-  check_glance_outputs(gl, gl2)
-  check_dims(gl, expected_cols = 8)
-
-  expect_error(glance(fit_multi), "Glance does not support linear models with multiple responses.")
-})
-
-test_that("augment.felm", {
-  check_augment_function(
-    aug = augment.felm,
-    model = fit,
-    data = df
-  )
-
-  check_augment_function(
-    aug = augment.felm,
-    model = fit2,
-    data = df
-  )
-
-  check_augment_function(
-    aug = augment.felm,
-    model = fit_form,
-    data = df
-  )
-  expect_error(augment(fit_multi), 
-               "Augment does not support linear models with multiple responses.")
-
-  # Ensure that the .resid and .fitted columns are basic columns, not matrix
-  aug <- augment(fit)
-  expect_false(inherits(aug$.resid, "matrix"))
-  expect_false(inherits(aug$.fitted, "matrix"))
-  expect_null(c(colnames(aug$.resid), colnames(aug$.fitted)))
-})