diff --git a/R/linear_pool.R b/R/linear_pool.R
index e6eae35..012a64d 100644
--- a/R/linear_pool.R
+++ b/R/linear_pool.R
@@ -3,23 +3,7 @@
 #' each combination of model task, output type, and output type id. Supported
 #' output types include `mean`, `quantile`, `cdf`, and `pmf`.
 #'
-#' @param model_outputs an object of class `model_output_df` with component
-#'   model outputs (e.g., predictions).
-#' @param weights an optional `data.frame` with component model weights. If
-#'   provided, it should have a column named `model_id` and a column containing
-#'   model weights. Optionally, it may contain additional columns corresponding
-#'   to task id variables, `output_type`, or `output_type_id`, if weights are
-#'   specific to values of those variables. The default is `NULL`, in which case
-#'   an equally-weighted ensemble is calculated.
-#' @param weights_col_name `character` string naming the column in `weights`
-#'   with model weights. Defaults to `"weight"`
-#' @param model_id `character` string with the identifier to use for the
-#'   ensemble model.
-#' @param task_id_cols `character` vector with names of columns in
-#'   `model_outputs` that specify modeling tasks. Defaults to `NULL`, in which
-#'   case all columns in `model_outputs` other than `"model_id"`, the specified
-#'   `output_type_col` and `output_type_id_col`, and `"value"` are used as task
-#'   ids.
+#' @inheritParams simple_ensemble
 #' @param n_samples `numeric` that specifies the number of samples to use when
 #'   calculating quantiles from an estimated quantile function. Defaults to `1e4`.
 #' @param ... parameters that are passed to `distfromq::make_q_fn`, specifying
@@ -37,14 +21,14 @@
 #'   in three steps:
 #'     1. Interpolate and extrapolate from the provided quantiles for each component
 #'        model to obtain an estimate of the cdf of that distribution.
-#'     2. Draw samples from the distribution for each component model. To reduce Monte
-#'        Carlo variability, we use pseudo-random samples corresponding to quantiles
-#'        of the estimated distribution.
+#'     2. Draw samples from the distribution for each component model. To reduce
+#'        Monte Carlo variability, we use quasi-random samples corresponding to
+#'        quantiles of the estimated distribution.
 #'     3. Collect the samples from all component models and extract the desired quantiles.
 #'   Steps 1 and 2 in this process are performed by `distfromq::make_q_fn`.
 #'
-#' @return a `model_out_tbl` object of ensemble predictions. Note that any additional
-#'   columns in the input `model_outputs` are dropped.
+#' @return a `model_out_tbl` object of ensemble predictions. Note that any
+#'   additional columns in the input `model_outputs` are dropped.
 #'
 #' @export
 #'
diff --git a/R/linear_pool_quantile.R b/R/linear_pool_quantile.R
index f71c942..6dc63b5 100644
--- a/R/linear_pool_quantile.R
+++ b/R/linear_pool_quantile.R
@@ -2,39 +2,9 @@
 #' (distributional mixture) of component model outputs for the `quantile`
 #' output type.
 #'
-#' @param model_outputs an object of class `model_output_df` with component
-#'   model outputs (e.g., predictions) with only a `quantile` output type.
-#'   Should be pre-validated.
-#' @param weights an optional `data.frame` with component model weights. If
-#'   provided, it should have a column named `model_id` and a column containing
-#'   model weights. Optionally, it may contain additional columns corresponding
-#'   to task id variables, `output_type`, or `output_type_id`, if weights are
-#'   specific to values of those variables. The default is `NULL`, in which case
-#'   an equally-weighted ensemble is calculated. Should be pre-validated.
-#' @param weights_col_name `character` string naming the column in `weights`
-#'   with model weights. Defaults to `"weight"`.
-#' @param model_id `character` string with the identifier to use for the
-#'   ensemble model.
-#' @param task_id_cols `character` vector with names of columns in
-#'   `model_outputs` that specify modeling tasks. Defaults to `NULL`, in which
-#'   case all columns in `model_outputs` other than `"model_id"`, the specified
-#'   `output_type_col` and `output_type_id_col`, and `"value"` are used as task
-#'   ids. Should be pre-validated.
-#' @param n_samples `numeric` that specifies the number of samples to use when
-#'   calculating quantiles from an estimated quantile function. Defaults to `1e4`.
-#' @param ... parameters that are passed to `distfromq::make_q_fun`, specifying
-#'   details of how to estimate a quantile function from provided quantile levels
-#'   and quantile values for `output_type` `"quantile"`.
+#' @inherit linear_pool params details
 #' @noRd
-#' @details The underlying mechanism for the computations to obtain the quantiles
-#'   of a linear pool in three steps is as follows:
-#'     1. Interpolate and extrapolate from the provided quantiles for each component
-#'        model to obtain an estimate of the cdf of that distribution.
-#'     2. Draw samples from the distribution for each component model. To reduce Monte
-#'        Carlo variability, we use pseudo-random samples corresponding to quantiles
-#'        of the estimated distribution.
-#'     3. Collect the samples from all component models and extract the desired quantiles.
-#'   Steps 1 and 2 in this process are performed by `distfromq::make_q_fun`.
+#'
 #' @return a `model_out_tbl` object of ensemble predictions for the `quantile` output type.
 #' @importFrom rlang .data
 
diff --git a/R/simple_ensemble.R b/R/simple_ensemble.R
index f4c1fd2..8169ef0 100644
--- a/R/simple_ensemble.R
+++ b/R/simple_ensemble.R
@@ -9,7 +9,7 @@
 #'   model weights. Optionally, it may contain additional columns corresponding
 #'   to task id variables, `output_type`, or `output_type_id`, if weights are
 #'   specific to values of those variables. The default is `NULL`, in which case
-#'   an equally-weighted ensemble is calculated.
+#'   an equally-weighted ensemble is calculated. Should be pre-validated.
 #' @param weights_col_name `character` string naming the column in `weights`
 #'   with model weights. Defaults to `"weight"`
 #' @param agg_fun a function or character string name of a function to use for
@@ -21,9 +21,8 @@
 #'   ensemble model.
 #' @param task_id_cols `character` vector with names of columns in
 #'   `model_outputs` that specify modeling tasks. Defaults to `NULL`, in which
-#'   case all columns in `model_outputs` other than `"model_id"`, the specified
-#'   `output_type_col` and `output_type_id_col`, and `"value"` are used as task
-#'   ids.
+#'   case all columns in `model_outputs` other than `"model_id"`, `"output_type"`,
+#'   `"output_type_id"`, and `"value"` are used as task ids.
 #'
 #' @details The default for `agg_fun` is `"mean"`, in which case the ensemble's
 #'   output is the average of the component model outputs within each group
diff --git a/man/linear_pool.Rd b/man/linear_pool.Rd
index 1911a36..9dba060 100644
--- a/man/linear_pool.Rd
+++ b/man/linear_pool.Rd
@@ -18,7 +18,7 @@ linear_pool(
 )
 }
 \arguments{
-\item{model_outputs}{an object of class \code{model_output_df} with component
+\item{model_outputs}{an object of class \code{model_out_tbl} with component
 model outputs (e.g., predictions).}
 
 \item{weights}{an optional \code{data.frame} with component model weights. If
@@ -26,7 +26,7 @@ provided, it should have a column named \code{model_id} and a column containing
 model weights. Optionally, it may contain additional columns corresponding
 to task id variables, \code{output_type}, or \code{output_type_id}, if weights are
 specific to values of those variables. The default is \code{NULL}, in which case
-an equally-weighted ensemble is calculated.}
+an equally-weighted ensemble is calculated. Should be pre-validated.}
 
 \item{weights_col_name}{\code{character} string naming the column in \code{weights}
 with model weights. Defaults to \code{"weight"}}
@@ -36,9 +36,8 @@ ensemble model.}
 
 \item{task_id_cols}{\code{character} vector with names of columns in
 \code{model_outputs} that specify modeling tasks. Defaults to \code{NULL}, in which
-case all columns in \code{model_outputs} other than \code{"model_id"}, the specified
-\code{output_type_col} and \code{output_type_id_col}, and \code{"value"} are used as task
-ids.}
+case all columns in \code{model_outputs} other than \code{"model_id"}, \code{"output_type"},
+\code{"output_type_id"}, and \code{"value"} are used as task ids.}
 
 \item{n_samples}{\code{numeric} that specifies the number of samples to use when
 calculating quantiles from an estimated quantile function. Defaults to \code{1e4}.}
@@ -48,8 +47,8 @@ details of how to estimate a quantile function from provided quantile levels
 and quantile values for \code{output_type} \code{"quantile"}.}
 }
 \value{
-a \code{model_out_tbl} object of ensemble predictions. Note that any additional
-columns in the input \code{model_outputs} are dropped.
+a \code{model_out_tbl} object of ensemble predictions. Note that any
+additional columns in the input \code{model_outputs} are dropped.
 }
 \description{
 Compute ensemble model outputs as a linear pool, otherwise known as a
@@ -70,9 +69,9 @@ When the \code{output_type} is \code{quantile}, we obtain the quantiles of a lin
 in three steps:
 1. Interpolate and extrapolate from the provided quantiles for each component
 model to obtain an estimate of the cdf of that distribution.
-2. Draw samples from the distribution for each component model. To reduce Monte
-Carlo variability, we use pseudo-random samples corresponding to quantiles
-of the estimated distribution.
+2. Draw samples from the distribution for each component model. To reduce
+Monte Carlo variability, we use quasi-random samples corresponding to
+quantiles of the estimated distribution.
 3. Collect the samples from all component models and extract the desired quantiles.
 Steps 1 and 2 in this process are performed by \code{distfromq::make_q_fn}.
 }
diff --git a/man/simple_ensemble.Rd b/man/simple_ensemble.Rd
index 3f514cd..b2c78d9 100644
--- a/man/simple_ensemble.Rd
+++ b/man/simple_ensemble.Rd
@@ -25,7 +25,7 @@ provided, it should have a column named \code{model_id} and a column containing
 model weights. Optionally, it may contain additional columns corresponding
 to task id variables, \code{output_type}, or \code{output_type_id}, if weights are
 specific to values of those variables. The default is \code{NULL}, in which case
-an equally-weighted ensemble is calculated.}
+an equally-weighted ensemble is calculated. Should be pre-validated.}
 
 \item{weights_col_name}{\code{character} string naming the column in \code{weights}
 with model weights. Defaults to \code{"weight"}}
@@ -42,9 +42,8 @@ ensemble model.}
 
 \item{task_id_cols}{\code{character} vector with names of columns in
 \code{model_outputs} that specify modeling tasks. Defaults to \code{NULL}, in which
-case all columns in \code{model_outputs} other than \code{"model_id"}, the specified
-\code{output_type_col} and \code{output_type_id_col}, and \code{"value"} are used as task
-ids.}
+case all columns in \code{model_outputs} other than \code{"model_id"}, \code{"output_type"},
+\code{"output_type_id"}, and \code{"value"} are used as task ids.}
 }
 \value{
 a \code{model_out_tbl} object of ensemble predictions. Note that