diff --git a/NEWS.md b/NEWS.md
index 2ac125f9..412bdfb6 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -1,5 +1,10 @@
 # lava 1.9.1
   Development version
+  - `estimate.default`: the `null`, `contrast`, `type`, and `var.adj`
+    arguments are soft-deprecated. Use
+    `summary(estimate(...), null=, contrast=, type=, var.adj=)` instead.
+    The default-path Wald p-value (H0: beta = 0) continues to be reported by
+    `estimate()`.
   - `estimate.default`: removed `score.deriv` and `folds` arguments.
   - `estimate.default`: removed `R` and `null.sim` arguments
   - Safe evaluation of rank in `wald_test`
diff --git a/R/diagtest.R b/R/diagtest.R
index a4b9e4d1..5c9dde14 100644
--- a/R/diagtest.R
+++ b/R/diagtest.R
@@ -156,7 +156,7 @@ diagtest <- function(table,positive=2,exact=FALSE,p0=NA,confint=c("logit","arcsi
                 }
             }
         }
-        res <- estimate(M,calc_diag,print=prfun,null=c(rep(p0,7),0),back.transform=btransform,...)
+        res <- suppressWarnings(estimate(M,calc_diag,print=prfun,null=c(rep(p0,7),0),back.transform=btransform,...))
     }
     CI <- confint[1]
     if (exact) CI <- "exact"
diff --git a/R/estimate.default.R b/R/estimate.default.R
index ce9780b9..98993c8b 100644
--- a/R/estimate.default.R
+++ b/R/estimate.default.R
@@ -20,15 +20,19 @@ estimate <- function(x, ...) UseMethod("estimate")
 ##' @param level level of confidence limits
 ##' @param IC if TRUE (default) the influence function decompositions are also
 ##'   returned (extract with \code{IC} method)
-##' @param type type of small-sample correction
+##' @param type type of small-sample correction (deprecated; see
+##'   [summary.estimate])
 ##' @param var.adj variance adjustment parameter for small-sample correction
+##'   (deprecated; see [summary.estimate])
 ##' @param keep (optional) index of parameters to keep from final result
 ##' @param use (optional) index of parameters to use in calculations
 ##' @param regex If TRUE use regular expression (perl compatible) for keep, use
 ##'   arguments
 ##' @param ignore.case Ignore case-sensitiveness in regular expression
 ##' @param contrast (optional) Contrast matrix for final Wald test
-##' @param null (optional) null hypothesis to test
+##'   (deprecated; see [summary.estimate])
+##' @param null (optional) null hypothesis to test (deprecated; see
+##'   [summary.estimate])
 ##' @param vcov (optional) covariance matrix of parameter estimates or logical.
 ##' If TRUE, then [stats::vcov] is used to obtain the covariance matrix from the
 ##' provided model object `x`
@@ -66,16 +70,15 @@ estimate <- function(x, ...) UseMethod("estimate")
 ##' estimate(g)
 ##'
 ##' ## Testing contrasts
-##' estimate(g, null=0)
 ##' estimate(g, rbind(c(1,1,0), c(1,0,2)))
-##' estimate(g, rbind(c(1,1,0), c(1,0,2)), null=c(1,2))
+##' summary(estimate(g, rbind(c(1,1,0), c(1,0,2))), null=c(1,2))
 ##' estimate(g, 2:3) ## same as cbind(0,1,-1)
 ##' estimate(g, as.list(2:3)) ## same as rbind(c(0,1,0),c(0,0,1))
 ##' ## Alternative syntax
 ##' estimate(g, "z", "z"-"x", 2*"z"-3*"x")
 ##' estimate(g, "?")  ## Wildcards
 ##' estimate(g, "*Int*", "z")
-##' estimate(g, "1", "2"-"3", null = c(0,1))
+##' summary(estimate(g, "1", "2"-"3"), null = c(0,1))
 ##' estimate(g, 2, 3)
 ##'
 ##' ## Usual (non-robust) confidence intervals
@@ -219,6 +222,16 @@ estimate.default <- function(x=NULL, f=NULL, ..., data, id,
       "Use the 'vcov' argument to compute model-based SEs."
     )
   }
+  if (!missing(null) || !missing(contrast) ||
+      !missing(type) || !missing(var.adj)) {
+    .Deprecated(
+      msg = paste0(
+        "The 'null', 'contrast', 'type', and 'var.adj' arguments of ",
+        "estimate.default() are deprecated. Use ",
+        "summary(estimate(...), null=, contrast=, type=, var.adj=) instead."
+      )
+    )
+  }
   if (!missing(use)) {
     p0 <- c(
       "f", "contrast", "only.coef",
@@ -376,6 +389,8 @@ estimate.default <- function(x=NULL, f=NULL, ..., data, id,
   }
   if (inherits(x, "lm") && family(x)$family == "gaussian"
       && is.null(df) && !missing(vcov)) {
+    # defaults to t-distribution when calculating p-values with model-based
+    # SEs
     df <- x$df.residual
   }
   if (!is.null(ic_theta) && (missing(vcov) || is.null(vcov))) {
@@ -568,6 +583,8 @@ estimate.default <- function(x=NULL, f=NULL, ..., data, id,
     if (!missing(null) && missing(contrast))
       beta0 <- beta0-null
     if (!is.null(df)) {
+      # the idea should be to assign df as an attribute to the returned
+      # object, and re-use it inside summary.estimate
       za <- qt(1-alpha/2, df=df)
       pval <- 2*pt(abs(res[, 1]/res[, 2]), df=df, lower.tail=FALSE)
     } else {
@@ -596,7 +613,7 @@ estimate.default <- function(x=NULL, f=NULL, ..., data, id,
     names(coefs) <- rownames(res)
   }
   res <- structure(list(coef=coefs, coefmat=res, vcov=V,
-                        IC=NULL, print=print, id=idstack),
+                        IC=NULL, print=print, id=idstack, df=df),
                    class="estimate")
   if (IC) ## && is.null(back.transform))
     res$IC <- ic_theta
@@ -636,7 +653,7 @@ estimate.default <- function(x=NULL, f=NULL, ..., data, id,
       rownames(res$coefmat) <- names(res$coef)
     }
   }
-
+  
   if (!is.null(back.transform)) {
     res$coefmat[, c(1, 3, 4)] <- do.call(back.transform,
                                          list(res$coefmat[, c(1, 3, 4)]))
@@ -821,20 +838,78 @@ with_unique_warnings <- function(expr) {
   })
 }
 
+##' Summary of estimate objects
+##'
+##' Computes hypothesis tests, contrasts, and small-sample corrections for
+##' an [estimate] object. The arguments `null`, `contrast`, `type`, and
+##' `var.adj` were previously available on [estimate.default()] and have
+##' been moved here.
+##'
+##' @param object an `estimate` object.
+##' @param contrast (optional) contrast matrix for the final Wald test.
+##' @param null (optional) null hypothesis to test.
+##' @param type type of small-sample correction. Requires the estimate
+##'   to have been computed with `IC=TRUE` (the default).
+##' @param var.adj variance adjustment parameter for small-sample
+##'   correction. Requires the estimate to have been computed with
+##'   `IC=TRUE` (the default).
+##' @param ... additional arguments passed to [estimate()].
+##' @seealso [estimate.default()]
 ##' @export
 summary.estimate <- function(object,
                              contrast,
+                             null,
+                             type,
+                             var.adj,
                              ...) {
   with_unique_warnings({
     p <- coef(object)
-    if (missing(contrast)) contrast <- diag(1,nrow=length(p))#as.list(seq_along(p))
-    test <- estimate(coef=p,
-                     vcov=vcov(object),
-                     f = FALSE,
-                     contrast = contrast,
-                     ...)
+    df <- object$df
+    correction <- !missing(type) || !missing(var.adj)
+    user_contrast <- !missing(contrast)
+    user_null <- !missing(null)
+    if (missing(contrast)) contrast <- diag(1, nrow=length(p))
+    args <- list(
+      coef = p,
+      contrast = contrast,
+      df = df
+    )
+    ## When the user supplies any of null/contrast/type/var.adj we want
+    ## the recall to run the full Wald-test override (contrast.transform
+    ## must be TRUE). In the no-op case we preserve the historical
+    ## behavior of passing f=FALSE so the H0:beta=0 p-values in coefmat
+    ## are kept verbatim.
+    if (!user_contrast && !user_null && !correction) {
+      args$f <- FALSE
+    }
+    if (user_null) args$null <- null
+    if (correction) {
+      if (is.null(object$IC)) {
+        stop(
+          "Small-sample corrections in summary() require an estimate ",
+          "computed with IC=TRUE."
+        )
+      }
+      args$IC <- object$IC
+      args$id <- object$id
+      if (!missing(type)) args$type <- type
+      if (!missing(var.adj)) args$var.adj <- var.adj
+    } else {
+      args$vcov <- vcov(object)
+    }
+    args <- c(args, list(...))
+    test <- suppressWarnings(do.call(estimate, args))
     class(test) <- "NULL"
     test$compare <- test$compare
+    ## Preserve dimnames on vcov: the recall path through type/var.adj
+    ## corrections builds V from the influence function and may drop
+    ## dimnames. Restore them from the original coef names so the
+    ## summary output is consistent with the deprecated estimate() path.
+    if (!is.null(test$vcov) && is.null(dimnames(test$vcov)) &&
+        !is.null(names(test$coef)) &&
+        nrow(test$vcov) == length(test$coef)) {
+      dimnames(test$vcov) <- list(names(test$coef), names(test$coef))
+    }
     object <- test[c("coef", "coefmat", "vcov", "call",
                      "ncluster", "model.index", "compare")]
     class(object) <- "summary.estimate"
diff --git a/R/estimate_calculus.R b/R/estimate_calculus.R
index 47416459..7c5d7963 100644
--- a/R/estimate_calculus.R
+++ b/R/estimate_calculus.R
@@ -584,5 +584,5 @@ operator_grad <- function(x, y, x_const, y_const, dx, dy) {
   if (!(is.numeric(e1) || is.numeric(e2))) stop("numeric comperator needed")
   null <- if (is.numeric(e1)) e1 else e2
   e <- if (is.numeric(e1)) e2 else e1
-  estimate(e, null=null)
+  suppressWarnings(estimate(e, null=null))
 }
diff --git a/man/estimate.default.Rd b/man/estimate.default.Rd
index 91c019c1..9a62a268 100644
--- a/man/estimate.default.Rd
+++ b/man/estimate.default.Rd
@@ -62,9 +62,11 @@ expression or variable name)}
 \item{IC}{if TRUE (default) the influence function decompositions are also
 returned (extract with \code{IC} method)}
 
-\item{type}{type of small-sample correction}
+\item{type}{type of small-sample correction (deprecated; see
+\link{summary.estimate})}
 
-\item{var.adj}{variance adjustment parameter for small-sample correction}
+\item{var.adj}{variance adjustment parameter for small-sample correction
+(deprecated; see \link{summary.estimate})}
 
 \item{keep}{(optional) index of parameters to keep from final result}
 
@@ -75,9 +77,11 @@ arguments}
 
 \item{ignore.case}{Ignore case-sensitiveness in regular expression}
 
-\item{contrast}{(optional) Contrast matrix for final Wald test}
+\item{contrast}{(optional) Contrast matrix for final Wald test
+(deprecated; see \link{summary.estimate})}
 
-\item{null}{(optional) null hypothesis to test}
+\item{null}{(optional) null hypothesis to test (deprecated; see
+\link{summary.estimate})}
 
 \item{vcov}{(optional) covariance matrix of parameter estimates or logical.
 If TRUE, then \link[stats:vcov]{stats::vcov} is used to obtain the covariance matrix from the
@@ -125,16 +129,15 @@ estimate(g, g0)
 estimate(g)
 
 ## Testing contrasts
-estimate(g, null=0)
 estimate(g, rbind(c(1,1,0), c(1,0,2)))
-estimate(g, rbind(c(1,1,0), c(1,0,2)), null=c(1,2))
+summary(estimate(g, rbind(c(1,1,0), c(1,0,2))), null=c(1,2))
 estimate(g, 2:3) ## same as cbind(0,1,-1)
 estimate(g, as.list(2:3)) ## same as rbind(c(0,1,0),c(0,0,1))
 ## Alternative syntax
 estimate(g, "z", "z"-"x", 2*"z"-3*"x")
 estimate(g, "?")  ## Wildcards
 estimate(g, "*Int*", "z")
-estimate(g, "1", "2"-"3", null = c(0,1))
+summary(estimate(g, "1", "2"-"3"), null = c(0,1))
 estimate(g, 2, 3)
 
 ## Usual (non-robust) confidence intervals
diff --git a/man/summary.estimate.Rd b/man/summary.estimate.Rd
new file mode 100644
index 00000000..b6f9710c
--- /dev/null
+++ b/man/summary.estimate.Rd
@@ -0,0 +1,33 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/estimate.default.R
+\name{summary.estimate}
+\alias{summary.estimate}
+\title{Summary of estimate objects}
+\usage{
+\method{summary}{estimate}(object, contrast, null, type, var.adj, ...)
+}
+\arguments{
+\item{object}{an \code{estimate} object.}
+
+\item{contrast}{(optional) contrast matrix for the final Wald test.}
+
+\item{null}{(optional) null hypothesis to test.}
+
+\item{type}{type of small-sample correction. Requires the estimate
+to have been computed with \code{IC=TRUE} (the default).}
+
+\item{var.adj}{variance adjustment parameter for small-sample
+correction. Requires the estimate to have been computed with
+\code{IC=TRUE} (the default).}
+
+\item{...}{additional arguments passed to \code{\link[=estimate]{estimate()}}.}
+}
+\description{
+Computes hypothesis tests, contrasts, and small-sample corrections for
+an \link{estimate} object. The arguments \code{null}, \code{contrast}, \code{type}, and
+\code{var.adj} were previously available on \code{\link[=estimate.default]{estimate.default()}} and have
+been moved here.
+}
+\seealso{
+\code{\link[=estimate.default]{estimate.default()}}
+}
diff --git a/tests/testthat/test-estimate-deprecation.R b/tests/testthat/test-estimate-deprecation.R
new file mode 100644
index 00000000..721fcad3
--- /dev/null
+++ b/tests/testthat/test-estimate-deprecation.R
@@ -0,0 +1,65 @@
+## Tests for soft deprecation of null/contrast/type/var.adj in estimate.default
+
+context("estimate.default deprecation warnings")
+
+make_glm <- function() {
+  set.seed(1)
+  d <- data.frame(
+    y = rbinom(100, 1, 0.5),
+    x = rnorm(100),
+    z = rnorm(100)
+  )
+  glm(y ~ x + z, data = d, family = binomial())
+}
+
+test_that("estimate(..., null=) emits deprecation warning", {
+  g <- make_glm()
+  expect_warning(estimate(g, null = 0), class = "deprecatedWarning")
+})
+
+test_that("estimate(..., null=0) warns even when null=0 (Q7)", {
+  g <- make_glm()
+  expect_warning(estimate(g, null = 0), class = "deprecatedWarning")
+})
+
+test_that("estimate(..., contrast=) emits deprecation warning", {
+  g <- make_glm()
+  B <- rbind(c(1, 0, 0), c(0, 1, 0))
+  expect_warning(estimate(g, contrast = B), class = "deprecatedWarning")
+})
+
+test_that("estimate(..., type=) emits deprecation warning", {
+  g <- make_glm()
+  expect_warning(estimate(g, type = "df"), class = "deprecatedWarning")
+})
+
+test_that("estimate(..., var.adj=) emits deprecation warning", {
+  g <- make_glm()
+  expect_warning(
+    estimate(g, type = "hc3", var.adj = 0.5),
+    class = "deprecatedWarning"
+  )
+})
+
+test_that("multiple deprecated args produce exactly one deprecation warning", {
+  g <- make_glm()
+  ws <- capture_warnings(estimate(g, null = 1, type = "df"))
+  dep_msgs <- grep("deprecated", ws, value = TRUE)
+  expect_equal(length(dep_msgs), 1L)
+})
+
+test_that("internally-derived contrast from f does NOT warn (Q8)", {
+  g <- make_glm()
+  ws <- capture_warnings(estimate(g, "x"))
+  expect_equal(length(grep("deprecated", ws)), 0L)
+  ws <- capture_warnings(estimate(g, 2:3))
+  expect_equal(length(grep("deprecated", ws)), 0L)
+  ws <- capture_warnings(estimate(g, rbind(c(1, 0, 0), c(0, 1, 0))))
+  expect_equal(length(grep("deprecated", ws)), 0L)
+})
+
+test_that("plain estimate() emits no deprecation warning (Q4)", {
+  g <- make_glm()
+  ws <- capture_warnings(estimate(g))
+  expect_equal(length(grep("deprecated", ws)), 0L)
+})
diff --git a/tests/testthat/test-estimate-summary-equivalence.R b/tests/testthat/test-estimate-summary-equivalence.R
new file mode 100644
index 00000000..ad6f11b2
--- /dev/null
+++ b/tests/testthat/test-estimate-summary-equivalence.R
@@ -0,0 +1,91 @@
+## Equivalence tests: estimate(g, X=...) should produce the same coefmat/vcov
+## as summary(estimate(g), X=...) during the soft-deprecation window.
+
+context("estimate / summary equivalence for migrated args")
+
+make_glm <- function() {
+  set.seed(1)
+  d <- data.frame(
+    y = rbinom(100, 1, 0.5),
+    x = rnorm(100),
+    z = rnorm(100)
+  )
+  glm(y ~ x + z, data = d, family = binomial())
+}
+
+## Helper: get coefmat from either deprecated or new path
+deprecated_estimate <- function(...) {
+  suppressWarnings(estimate(...))
+}
+
+test_that("null= gives identical coefmat via estimate() and summary()", {
+  g <- make_glm()
+  e1 <- deprecated_estimate(g, null = 1)
+  e2 <- summary(estimate(g), null = 1)
+  expect_equal(e1$coefmat, e2$coefmat)
+  expect_equal(unname(e1$vcov), unname(e2$vcov))
+})
+
+test_that("contrast= gives identical coefmat via estimate() and summary()", {
+  g <- make_glm()
+  B <- rbind(c(1, 0, 0), c(0, 1, -1))
+  e1 <- deprecated_estimate(g, contrast = B)
+  e2 <- summary(estimate(g), contrast = B)
+  expect_equal(e1$coefmat, e2$coefmat)
+  expect_equal(unname(e1$vcov), unname(e2$vcov))
+})
+
+test_that("null + contrast gives identical results via estimate() and summary()", {
+  g <- make_glm()
+  B <- rbind(c(1, 0, 0), c(0, 1, -1))
+  e1 <- deprecated_estimate(g, contrast = B, null = c(0, 0))
+  e2 <- summary(estimate(g), contrast = B, null = c(0, 0))
+  expect_equal(e1$coefmat, e2$coefmat)
+  expect_equal(unname(e1$vcov), unname(e2$vcov))
+})
+
+test_that("type='df' gives identical coefmat via estimate() and summary()", {
+  g <- make_glm()
+  e1 <- deprecated_estimate(g, type = "df")
+  e2 <- summary(estimate(g), type = "df")
+  expect_equal(e1$coefmat, e2$coefmat)
+  expect_equal(unname(e1$vcov), unname(e2$vcov))
+})
+
+test_that("type='df1' gives identical coefmat via estimate() and summary()", {
+  g <- make_glm()
+  e1 <- deprecated_estimate(g, type = "df1")
+  e2 <- summary(estimate(g), type = "df1")
+  expect_equal(e1$coefmat, e2$coefmat)
+})
+
+test_that("type='hc3' + var.adj gives identical results via estimate() and summary()", {
+  g <- make_glm()
+  e1 <- deprecated_estimate(g, type = "hc3", var.adj = 0.5)
+  e2 <- summary(estimate(g), type = "hc3", var.adj = 0.5)
+  expect_equal(e1$coefmat, e2$coefmat)
+  expect_equal(unname(e1$vcov), unname(e2$vcov))
+})
+
+test_that("type + null gives identical results via estimate() and summary()", {
+  g <- make_glm()
+  e1 <- deprecated_estimate(g, type = "df", null = 0.2)
+  e2 <- summary(estimate(g), type = "df", null = 0.2)
+  expect_equal(e1$coefmat, e2$coefmat)
+})
+
+test_that("df + null gives identical results via estimate() and summary()", {
+  g <- make_glm()
+  ## Per Q11: df is stored on the estimate object and reused by summary().
+  e1 <- deprecated_estimate(g, df = 10, null = 1)
+  e2 <- summary(estimate(g, df = 10), null = 1)
+  expect_equal(e1$coefmat, e2$coefmat)
+})
+
+test_that("df is stored on the estimate object", {
+  g <- make_glm()
+  e <- estimate(g, df = 7)
+  expect_equal(e$df, 7)
+  e0 <- estimate(g)
+  expect_null(e0$df)
+})
diff --git a/tests/testthat/test-estimate_default.R b/tests/testthat/test-estimate_default.R
index f82b4450..d6467d69 100644
--- a/tests/testthat/test-estimate_default.R
+++ b/tests/testthat/test-estimate_default.R
@@ -82,8 +82,8 @@ test_that("summary.estimate compared with estimate", {
   q <- compute_wald(B, coef(a3d), vcov(a3d), null)
   df <- attr(q, "df")
   e1 <- estimate(a3d, f=B)
-  e1b <- estimate(a3d, f=B, null=null)
-  e1a <- estimate(e1, f=diag(3), null=null)
+  e1b <- suppressWarnings(estimate(a3d, f=B, null=null))
+  e1a <- suppressWarnings(estimate(e1, f=diag(3), null=null))
   expect_equal(unname(df), unname(e1a$compare$parameter))
   expect_equal(unname(df), unname(e1b$compare$parameter))
   expect_true(abs(pchisq(q, df=df, lower.tail=FALSE) - e1a$compare$p.value)<1e-16)
@@ -92,8 +92,8 @@ test_that("summary.estimate compared with estimate", {
   expect_true(abs(q - e1b$compare$statistic) < 1e-9)
   # function spec.
   e2 <- estimate(a3d, function(p) c(p[1]-p[2], p[2]-p[3], p[1]-p[3]))
-  e2b <- estimate(a3d, function(p) c(p[1]-p[2], p[2]-p[3], p[1]-p[3]), null=null)
-  e2a <- estimate(e2, f=diag(3), null=null)
+  e2b <- suppressWarnings(estimate(a3d, function(p) c(p[1]-p[2], p[2]-p[3], p[1]-p[3]), null=null))
+  e2a <- suppressWarnings(estimate(e2, f=diag(3), null=null))
   expect_equal(unname(df), unname(e2a$compare$parameter))
   expect_equal(unname(df), unname(e2b$compare$parameter))
   expect_true(abs(pchisq(q, df=df, lower.tail=FALSE) - e2a$compare$p.value)<1e-16)
@@ -113,8 +113,8 @@ test_that("summary.estimate compared with estimate", {
   q <- compute_wald(B, coef(a3d), vcov(a3d), null)
   df <- attr(q, "df")
   e1 <- estimate(a3d, f=B)
-  e1b <- estimate(a3d, f=B, null=null)
-  e1a <- estimate(e1, f=diag(4), null=null)
+  e1b <- suppressWarnings(estimate(a3d, f=B, null=null))
+  e1a <- suppressWarnings(estimate(e1, f=diag(4), null=null))
   expect_equal(unname(df), unname(e1a$compare$parameter))
   expect_equal(unname(df), unname(e1b$compare$parameter))
   expect_true(abs(pchisq(q, df=df, lower.tail=FALSE) - e1a$compare$p.value)<1e-16)
@@ -131,7 +131,7 @@ test_that("summary.estimate compared with estimate", {
 test_that("1D: Identity contrast, null = 0", {
   B    <- matrix(1, nrow = 1, ncol = 1)
   null <- 0
-  e    <- estimate(a1, f = B, null = null)
+  e    <- suppressWarnings(estimate(a1, f = B, null = null))
   p <- coef(a1)
   S <- vcov(a1)
   expected <- compute_wald(B, p, S, null)
@@ -141,7 +141,7 @@ test_that("1D: Identity contrast, null = 0", {
 test_that("1D: Identity contrast, null = 1", {
   B    <- matrix(1, nrow = 1, ncol = 1)
   null <- 1
-  e <- estimate(a1, f = B, null = null)
+  e <- suppressWarnings(estimate(a1, f = B, null = null))
   p <- coef(a1)
   S <- vcov(a1)
   expected <- compute_wald(B, p, S, null)
@@ -151,7 +151,7 @@ test_that("1D: Identity contrast, null = 1", {
 test_that("1D: Scalar contrast (scaling), null = 0", {
   B    <- matrix(2, nrow = 1, ncol = 1)
   null <- 0
-  e <- estimate(a1, f = B, null = null)
+  e <- suppressWarnings(estimate(a1, f = B, null = null))
   p <- coef(a1)
   S <- vcov(a1)
   expected <- compute_wald(B, p, S, null)
@@ -161,7 +161,7 @@ test_that("1D: Scalar contrast (scaling), null = 0", {
 test_that("2D: Identity contrast, null = c(0, 0)", {
   B    <- diag(2)
   null <- c(0, 0)
-  e <- estimate(a, f = B, null = null)
+  e <- suppressWarnings(estimate(a, f = B, null = null))
   p <- coef(a)
   S <- vcov(a)
   expected <- compute_wald(B, p, S, null)
@@ -171,7 +171,7 @@ test_that("2D: Identity contrast, null = c(0, 0)", {
 test_that("2D: Identity contrast, null equals true coefs", {
   B    <- diag(2)
   null <- c(1, 2)
-  e <- estimate(a, f = B, null = null)
+  e <- suppressWarnings(estimate(a, f = B, null = null))
   p <- coef(a)
   S <- vcov(a)
   expected <- compute_wald(B, p, S, null)
@@ -184,7 +184,7 @@ test_that("2D: Identity contrast, null equals true coefs", {
 test_that("2D: Identity contrast, null = c(3, 4)", {
   B    <- diag(2)
   null <- c(3, 4)
-  e <- estimate(a, f = B, null = null)
+  e <- suppressWarnings(estimate(a, f = B, null = null))
   p <- coef(a)
   S <- vcov(a)
   expected <- compute_wald(B, p, S, null)
@@ -194,7 +194,7 @@ test_that("2D: Identity contrast, null = c(3, 4)", {
 test_that("2D: Difference contrast c(1,-1), null = 0", {
   B    <- matrix(c(1, -1), nrow = 1)
   null <- 0
-  e <- estimate(a, f = B, null = null)
+  e <- suppressWarnings(estimate(a, f = B, null = null))
   p <- coef(a)
   S <- vcov(a)
   expected <- compute_wald(B, p, S, null)
@@ -204,7 +204,7 @@ test_that("2D: Difference contrast c(1,-1), null = 0", {
 test_that("2D: Difference contrast c(1,-1), null = -1", {
   B    <- matrix(c(1, -1), nrow = 1)
   null <- -1  # Testing H0: a1 - a2 = -1 (true, since 1 - 2 = -1)
-  e <- estimate(a, f = B, null = null)
+  e <- suppressWarnings(estimate(a, f = B, null = null))
   p <- coef(a)
   S <- vcov(a)
   expected <- compute_wald(B, p, S, null)
@@ -216,7 +216,7 @@ test_that("2D: Difference contrast c(1,-1), null = -1", {
 test_that("2D: Sum contrast c(1,1), null = 3", {
   B    <- matrix(c(1, 1), nrow = 1)
   null <- 3  # True sum = 1 + 2 = 3
-  e <- estimate(a, f = B, null = null)
+  e <- suppressWarnings(estimate(a, f = B, null = null))
   p <- coef(a)
   S <- vcov(a)
   expected <- compute_wald(B, p, S, null)
@@ -228,7 +228,7 @@ test_that("2D: Sum contrast c(1,1), null = 3", {
 test_that("2D: Scaling contrast, null = c(2, 6)", {
   B    <- 2 * diag(2)
   null <- c(2, 6)  # True: 2*c(1,2) = c(2,4), so null != true
-  e <- estimate(a, f = B, null = null)
+  e <- suppressWarnings(estimate(a, f = B, null = null))
   p <- coef(a)
   S <- vcov(a)
   expected <- compute_wald(B, p, S, null)
@@ -238,7 +238,7 @@ test_that("2D: Scaling contrast, null = c(2, 6)", {
 test_that("3D: Identity contrast, null = true coefs", {
   B    <- diag(3)
   null <- c(1, 2, 3)
-  e <- estimate(a3d, f = B, null = null)
+  e <- suppressWarnings(estimate(a3d, f = B, null = null))
   p <- coef(a3d)
   S <- vcov(a3d)
   expected <- compute_wald(B, p, S, null)
@@ -249,7 +249,7 @@ test_that("3D: Identity contrast, null = true coefs", {
 test_that("3D: Identity contrast, null = c(0, 0, 0)", {
   B    <- diag(3)
   null <- c(0, 0, 0)
-  e <- estimate(a3d, f = B, null = null)
+  e <- suppressWarnings(estimate(a3d, f = B, null = null))
   p <- coef(a3d)
   S <- vcov(a3d)
   expected <- compute_wald(B, p, S, null)
@@ -263,7 +263,7 @@ test_that("3D: Pairwise differences, null = c(-1, -2)", {
     0,  1, -1
   ), nrow = 2, byrow = TRUE)
   null <- c(-1, -1)
-  e <- estimate(a3d, f = B, null = null)
+  e <- suppressWarnings(estimate(a3d, f = B, null = null))
   p <- coef(a3d)
   S <- vcov(a3d)
   expected <- compute_wald(B, p, S, null)
@@ -275,7 +275,7 @@ test_that("3D: Single-row contrast, null = 6", {
   # H0: a1 + a2 + a3 = 6 (true: 1+2+3=6)
   B    <- matrix(c(1, 1, 1), nrow = 1)
   null <- 6
-  e <- estimate(a3d, f = B, null = null)
+  e <- suppressWarnings(estimate(a3d, f = B, null = null))
   p <- coef(a3d)
   S <- vcov(a3d)
   expected <- compute_wald(B, p, S, null)
@@ -286,7 +286,7 @@ test_that("3D: Single-row contrast, null = 6", {
 test_that("4D: Identity contrast, null equals true coefs", {
   B    <- diag(4)
   null <- c(1, 2, 3, 4)
-  e <- estimate(a4d, f = B, null = null)
+  e <- suppressWarnings(estimate(a4d, f = B, null = null))
   p <- coef(a4d)
   S <- vcov(a4d)
   expected <- compute_wald(B, p, S, null)
@@ -301,7 +301,7 @@ test_that("4D: Non-square contrast (2x4), null = c(0, 0)", {
     0,  0,  1, -1
   ), nrow = 2, byrow = TRUE)
   null <- c(0, 0)
-  e <- estimate(a4d, f = B, null = null)
+  e <- suppressWarnings(estimate(a4d, f = B, null = null))
   p <- coef(a4d)
   S <- vcov(a4d)
   expected <- compute_wald(B, p, S, null)
@@ -315,7 +315,7 @@ test_that("4D: Non-square contrast, null equals true values", {
     0,  0,  1, -1
   ), nrow = 2, byrow = TRUE)
   null <- c(-1, -1)
-  e <- estimate(a4d, f = B, null = null)
+  e <- suppressWarnings(estimate(a4d, f = B, null = null))
   p <- coef(a4d)
   S <- vcov(a4d)
   expected <- compute_wald(B, p, S, null)
@@ -326,11 +326,11 @@ test_that("4D: Non-square contrast, null equals true values", {
 test_that("Degrees of freedom equals nrow(B)", {
   B    <- diag(2)
   null <- c(0, 0)
-  e    <- estimate(a, f = B, null = null)
+  e    <- suppressWarnings(estimate(a, f = B, null = null))
   expect_identical(unname(e$compare$parameter), nrow(B))
   B    <- matrix(c(1, -1, 0, 0, 1, -1), nrow = 2, byrow = TRUE)
   null <- c(0, 0)
-  e    <- estimate(a3d, f = B, null = null)
+  e    <- suppressWarnings(estimate(a3d, f = B, null = null))
   expect_equal(unname(e$compare$parameter), nrow(B))
 })
 
@@ -423,7 +423,7 @@ test_that("estimate.default parsedesign dispatch", {
   expect_equal(e$coefmat[, 1], 2.0)
 
   # Character contrasts combine with null hypothesis vector.
-  e <- estimate(a3d, "a1", "a1", null = c(0, 1))
+  e <- suppressWarnings(estimate(a3d, "a1", "a1", null = c(0, 1)))
   expect_true(e$coefmat[1, "P-value"] != e$coefmat[2, "P-value"])
 })
 
diff --git a/tests/testthat/test-summary-requires-IC.R b/tests/testthat/test-summary-requires-IC.R
new file mode 100644
index 00000000..76ebf304
--- /dev/null
+++ b/tests/testthat/test-summary-requires-IC.R
@@ -0,0 +1,41 @@
+## summary.estimate() requires IC=TRUE on the input estimate when applying
+## small-sample corrections (type / var.adj). Per Q5.
+
+context("summary.estimate IC requirement")
+
+make_glm <- function() {
+  set.seed(1)
+  d <- data.frame(
+    y = rbinom(100, 1, 0.5),
+    x = rnorm(100),
+    z = rnorm(100)
+  )
+  glm(y ~ x + z, data = d, family = binomial())
+}
+
+test_that("summary(..., type=) errors when input estimate has IC=FALSE", {
+  g <- make_glm()
+  e <- estimate(g, IC = FALSE)
+  expect_error(
+    summary(e, type = "df"),
+    "IC=TRUE",
+    fixed = TRUE
+  )
+})
+
+test_that("summary(..., var.adj=) errors when input estimate has IC=FALSE", {
+  g <- make_glm()
+  e <- estimate(g, IC = FALSE)
+  expect_error(
+    summary(e, type = "hc3", var.adj = 0.5),
+    "IC=TRUE",
+    fixed = TRUE
+  )
+})
+
+test_that("summary(..., null=) works when input estimate has IC=FALSE", {
+  ## null/contrast do NOT require IC, only type/var.adj do.
+  g <- make_glm()
+  e <- estimate(g, IC = FALSE)
+  expect_silent(summary(e, null = 1))
+})
diff --git a/vignettes/influencefunction.Rmd b/vignettes/influencefunction.Rmd
index 75dfd81e..c4126a55 100644
--- a/vignettes/influencefunction.Rmd
+++ b/vignettes/influencefunction.Rmd
@@ -254,7 +254,7 @@ coef(e)
 ## ## Asymptotic (robust) variance estimate
 vcov(e)
 ## Matrix with estimates and confidence limits
-estimate(e, null=0, level = 0.99) |> parameter()
+estimate(e, level = 0.99) |> parameter()
 ## Influence curve
 IC(e) |> head()
 ## Join estimates
@@ -445,7 +445,7 @@ As we have access to the joint asymptotic distribution we can for example test
 for whether the odds-ratio is the same for the two responses:
 
 ```{r hypo1}
-estimate(e, cbind(0,1,0,-1), null=0)
+summary(estimate(e), contrast=cbind(0,1,0,-1), null=0)
 ```
 More details an be found in the Section on [hypothesis testing](#linear-contrasts-and-hypothesis-testing).
 
@@ -855,7 +855,7 @@ estimate(gg, B)
 
 The $\bm{b}_0$ vector (default assumed to be zero) can be specified via the `null` argument
 ```{r estcontrast1}
-estimate(gg, B, null=1)
+summary(estimate(gg, B), null=1)
 ```
 
 For testing multiple hypotheses we use that
@@ -873,7 +873,7 @@ estimate(gg, B)
 
 Such linear statistics can also be specified directly as expressions of the parameter names
 ```{r}
-estimate(gg, a + a.1, 2*a - a.2, a, null=c(2,1,1))
+summary(estimate(gg, a + a.1, 2*a - a.2, a), null=c(2,1,1))
 ```
 
 We refer to the function `lava::contr`  and `lava::parsedesigns` for defining contrast matrices.
@@ -885,7 +885,7 @@ comparisons of different exposure estimates:
 
 ```{r pairwise.diff}
 pairwise.diff(3)
-estimate(gg, pairwise.diff(3), null=c(1,1,1), use=c(2,4,6))
+summary(estimate(gg, pairwise.diff(3), use=c(2,4,6)), null=c(1,1,1))
 ```
 
 When conducting multiple tests each at a nominal-level of $\alpha$ the overall
@@ -913,7 +913,7 @@ in [@RitzPipper_multcomp] the joint distribution of $Z_{1},\ldots,Z_{p}$
 can be estimated from the IFs. This is implemented in the `alpha_zmax` method
 
 ```{r pcorrect, eval=has_mets }
-gg0 <- estimate(gg, use="^a", regex=TRUE, null=rep(.8, 3))
+gg0 <- suppressWarnings(estimate(gg, use="^a", regex=TRUE, null=rep(.8, 3)))
 alpha_zmax(gg0)
 ```