diff --git a/DESCRIPTION b/DESCRIPTION
index ec395ac..ac50400 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -37,6 +37,7 @@ Suggests:
     ggplot2
 Config/testthat/edition: 3
 VignetteBuilder: knitr
+Roxygen: list(markdown = TRUE)
 RoxygenNote: 7.3.3
 MinCorpusSchema: 4
 MaxCorpusSchema: 7
diff --git a/R/balance_caveats.R b/R/balance_caveats.R
index dc25401..593ad0a 100644
--- a/R/balance_caveats.R
+++ b/R/balance_caveats.R
@@ -53,7 +53,7 @@
   )
 }
 
-#' Per-subtype [min year, max year] extents for EVERY balance subtype in the
+#' Per-subtype \[min year, max year\] extents for EVERY balance subtype in the
 #' mounted corpus, memoised for the session.
 #'
 #' The query carries no govid and no year predicate -- its answer is a property
diff --git a/man/cog_balances.Rd b/man/cog_balances.Rd
index eb56bed..6384a34 100644
--- a/man/cog_balances.Rd
+++ b/man/cog_balances.Rd
@@ -21,109 +21,109 @@ cog_balances(
 }
 \arguments{
 \item{govid}{Canonical govid(s): a character vector, or a data frame with a
-`canonical_govid` column (e.g. from [cog_gov_search()]). `NULL` to name
-the cohort by `state`/`type` instead.}
+\code{canonical_govid} column (e.g. from \code{\link[=cog_gov_search]{cog_gov_search()}}). \code{NULL} to name
+the cohort by \code{state}/\code{type} instead.}
 
 \item{years}{Integer vector of fiscal years.}
 
 \item{category}{Optional character vector of categories to keep. One of
-`"Fund Balances"`, `"Insurance Trust Balances"`,
-`"Retirement System Holdings"`. There is deliberately no `subtype`
-argument: for holdings, `category` is a strict coarsening of
-`balance_subtype` (unlike the money verbs, where the two axes cross), so
+\code{"Fund Balances"}, \code{"Insurance Trust Balances"},
+\code{"Retirement System Holdings"}. There is deliberately no \code{subtype}
+argument: for holdings, \code{category} is a strict coarsening of
+\code{balance_subtype} (unlike the money verbs, where the two axes cross), so
 every combination would be either redundant or empty.
-`category = "Fund Balances"` is exactly the `general` family
-(`W01`/`W31`/`W61`). `balance_subtype` is returned, so a finer split is
-one `dplyr::filter()` away. The reserved pseudo-category
-`"All Categories"` (see [cog_spending()]) is **not** supported here and
-errors with class `uscogdata_all_categories_unsupported`: it sums a
+\code{category = "Fund Balances"} is exactly the \code{general} family
+(\code{W01}/\code{W31}/\code{W61}). \code{balance_subtype} is returned, so a finer split is
+one \code{dplyr::filter()} away. The reserved pseudo-category
+\code{"All Categories"} (see \code{\link[=cog_spending]{cog_spending()}}) is \strong{not} supported here and
+errors with class \code{uscogdata_all_categories_unsupported}: it sums a
 concept's subtype scope, and holdings are a stock with no concept
-vocabulary to sum across. Omit `category` to get every category broken
+vocabulary to sum across. Omit \code{category} to get every category broken
 out instead.}
 
-\item{per_capita}{Divide holdings by population. Note this is a **stock per
-resident** (reserves per person), which is *not* comparable to
-[cog_spending()]'s per-capita figures -- those are a flow per person.}
+\item{per_capita}{Divide holdings by population. Note this is a \strong{stock per
+resident} (reserves per person), which is \emph{not} comparable to
+\code{\link[=cog_spending]{cog_spending()}}'s per-capita figures -- those are a flow per person.}
 
 \item{adjust_to_year}{Deflate to this year's dollars (CPI-U).}
 
 \item{basis}{Accepted for uniformity with the money verbs, but currently a
-**no-op**: `harmonization_map` carries no balance-code rows, so harmonized
+\strong{no-op}: \code{harmonization_map} carries no balance-code rows, so harmonized
 and raw space are identical for holdings. Reported in
-`provenance$basis_note`.}
+\code{provenance$basis_note}.}
 
-\item{recipe}{Optional harmonization recipe id (see [cog_recipes()]).
-`"cash_securities_z77_wide"` and `"cash_securities_z78_wide"` bridge the
+\item{recipe}{Optional harmonization recipe id (see \code{\link[=cog_recipes]{cog_recipes()}}).
+\code{"cash_securities_z77_wide"} and \code{"cash_securities_z78_wide"} bridge the
 wide era to the modern one.}
 
-\item{state, type}{Name the cohort by predicate instead of by id: `state` is
-  a 2-letter USPS abbreviation (or a FIPS code) and `type` is one of
-  `"state"`, `"county"`, `"city"`, `"township"` (or the integer `0:3`) --
-  the same vocabulary, and the same internal coercion, as
-  [cog_gov_search()]. Both default to `NULL`.
-
-  The cohort is then expressed as a subquery against `canonical_fips_xwalk`
-  inside each statement rather than round-tripped through R as a literal id
-  list. For a fleet-scale cohort that is the difference between a
-  301,591-character `IN` list re-parsed in 5--8 statements per call and a
-  constant-size predicate: measured at **94 ms versus 449 ms** for the same
-  FY2022 aggregate over the 20,106-government `type = "city"` cohort, within
-  7% of the no-filter floor.
-
-  Supplying `govid` **and** `state`/`type` INTERSECTS them -- the
-  governments in `govid` that also match the predicate -- rather than one
-  silently taking precedence. Naming no cohort at all (`govid`, `state` and
-  `type` all `NULL`) aborts with class `uscogdata_no_cohort`.
-
-  When the cohort is named by predicate, `provenance$scope$govids_found`
-  and `govids_missing` are empty -- there is no id list to report against --
-  and `provenance$scope$cohort` carries `state`, `type` and
-  `n_governments` instead. A `govid`-named cohort reports exactly as before.}
+\item{state, type}{Name the cohort by predicate instead of by id: \code{state} is
+a 2-letter USPS abbreviation (or a FIPS code) and \code{type} is one of
+\code{"state"}, \code{"county"}, \code{"city"}, \code{"township"} (or the integer \code{0:3}) --
+the same vocabulary, and the same internal coercion, as
+\code{\link[=cog_gov_search]{cog_gov_search()}}. Both default to \code{NULL}.
+
+The cohort is then expressed as a subquery against \code{canonical_fips_xwalk}
+inside each statement rather than round-tripped through R as a literal id
+list. For a fleet-scale cohort that is the difference between a
+301,591-character \code{IN} list re-parsed in 5--8 statements per call and a
+constant-size predicate: measured at \strong{94 ms versus 449 ms} for the same
+FY2022 aggregate over the 20,106-government \code{type = "city"} cohort, within
+7\% of the no-filter floor.
+
+Supplying \code{govid} \strong{and} \code{state}/\code{type} INTERSECTS them -- the
+governments in \code{govid} that also match the predicate -- rather than one
+silently taking precedence. Naming no cohort at all (\code{govid}, \code{state} and
+\code{type} all \code{NULL}) aborts with class \code{uscogdata_no_cohort}.
+
+When the cohort is named by predicate, \code{provenance$scope$govids_found}
+and \code{govids_missing} are empty -- there is no id list to report against --
+and \code{provenance$scope$cohort} carries \code{state}, \code{type} and
+\code{n_governments} instead. A \code{govid}-named cohort reports exactly as before.}
 
 \item{limit}{Maximum number of result rows to return, pushed into the SQL
-rather than applied after materializing every row. `NULL` (default)
-returns everything. Cannot be combined with `recipe` -- see `offset` and
-`total_rows`.}
-
-\item{offset}{Rows to skip before `limit` starts counting (0-based).
-Ignored if `limit` is `NULL`; defaults to `0L` when `limit` is set.}
-
-\item{fiscal_period}{If `TRUE`, add `fiscal_year_end`, `period_start` and
-`period_end` to every row, as [cog_spending()] does. Holdings are a stock
-measured at the end of the fiscal year, so `period_end` is the date the
-balance describes and `period_start` is the start of that fiscal year.
-Defaults to `FALSE`, which leaves the result unchanged.}
+rather than applied after materializing every row. \code{NULL} (default)
+returns everything. Cannot be combined with \code{recipe} -- see \code{offset} and
+\code{total_rows}.}
+
+\item{offset}{Rows to skip before \code{limit} starts counting (0-based).
+Ignored if \code{limit} is \code{NULL}; defaults to \code{0L} when \code{limit} is set.}
+
+\item{fiscal_period}{If \code{TRUE}, add \code{fiscal_year_end}, \code{period_start} and
+\code{period_end} to every row, as \code{\link[=cog_spending]{cog_spending()}} does. Holdings are a stock
+measured at the end of the fiscal year, so \code{period_end} is the date the
+balance describes and \code{period_start} is the start of that fiscal year.
+Defaults to \code{FALSE}, which leaves the result unchanged.}
 }
 \value{
-Tibble with columns `year`, `canonical_govid`, `gov_name`,
-  `balance_subtype`, `category`, `amt_nominal`, `codes_included`,
-  `aggregate_fallback`, plus optional `amt_per_capita_nominal` and
-  `pop_source` (when `per_capita = TRUE`), optional `amt_real` (when
-  `adjust_to_year` is set), and optional `amt_per_capita_real` (only when
-  **both** `per_capita = TRUE` and `adjust_to_year` are set -- there is no
-  nominal per-capita column to deflate otherwise), and `fiscal_year_end`,
-  `period_start` and `period_end` when `fiscal_period = TRUE`. Amounts are
-  full US dollars.
-
-  Carries a `provenance` attribute matching
-  `inst/schemas/provenance-v1.json`, whose `balance_caveats` block reports
-  `not_gaap`, `not_gaap_note`, `coverage_window` (measured year extents for
-  every balance subtype in the mounted corpus, not only the observed ones)
-  and `truncated` (the observed subtypes whose coverage falls short of the
-  requested years). `expenditure_concept`/`revenue_concept` are `NA` --
-  holdings are a stock, not a flow, so neither concept vocabulary applies.
-
-  When `limit` is set, also carries a `total_rows` attribute: the full
-  unpaginated row count, computed by the same query (`COUNT(*) OVER()`)
-  rather than a second scan.
+Tibble with columns \code{year}, \code{canonical_govid}, \code{gov_name},
+\code{balance_subtype}, \code{category}, \code{amt_nominal}, \code{codes_included},
+\code{aggregate_fallback}, plus optional \code{amt_per_capita_nominal} and
+\code{pop_source} (when \code{per_capita = TRUE}), optional \code{amt_real} (when
+\code{adjust_to_year} is set), and optional \code{amt_per_capita_real} (only when
+\strong{both} \code{per_capita = TRUE} and \code{adjust_to_year} are set -- there is no
+nominal per-capita column to deflate otherwise), and \code{fiscal_year_end},
+\code{period_start} and \code{period_end} when \code{fiscal_period = TRUE}. Amounts are
+full US dollars.
+
+Carries a \code{provenance} attribute matching
+\code{inst/schemas/provenance-v1.json}, whose \code{balance_caveats} block reports
+\code{not_gaap}, \code{not_gaap_note}, \code{coverage_window} (measured year extents for
+every balance subtype in the mounted corpus, not only the observed ones)
+and \code{truncated} (the observed subtypes whose coverage falls short of the
+requested years). \code{expenditure_concept}/\code{revenue_concept} are \code{NA} --
+holdings are a stock, not a flow, so neither concept vocabulary applies.
+
+When \code{limit} is set, also carries a \code{total_rows} attribute: the full
+unpaginated row count, computed by the same query (\verb{COUNT(*) OVER()})
+rather than a second scan.
 }
 \description{
-Returns Census cash-and-security holdings (`category_type = "balance"`):
+Returns Census cash-and-security holdings (\code{category_type = "balance"}):
 fund balances, retirement system holdings and insurance trust balances.
 }
 \section{Holdings are not GAAP fund balance}{
 
-Census holdings are **gross** -- no liabilities are netted -- so a reserve
+Census holdings are \strong{gross} -- no liabilities are netted -- so a reserve
 ratio built from them overstates what is actually available. They are not
 comparable to a GAAP fund balance from an ACFR.
 }
diff --git a/man/cog_basket_resolution.Rd b/man/cog_basket_resolution.Rd
index 3c1825f..1fca3f0 100644
--- a/man/cog_basket_resolution.Rd
+++ b/man/cog_basket_resolution.Rd
@@ -7,21 +7,21 @@
 cog_basket_resolution(x, expand_candidates = FALSE)
 }
 \arguments{
-\item{x}{A tibble returned by basket-mode [cog_gov_search()].}
+\item{x}{A tibble returned by basket-mode \code{\link[=cog_gov_search]{cog_gov_search()}}.}
 
-\item{expand_candidates}{Logical. If `TRUE`, keeps the `candidates`
+\item{expand_candidates}{Logical. If \code{TRUE}, keeps the \code{candidates}
 list-column (full-schema match candidates per input row). Default
-`FALSE`.}
+\code{FALSE}.}
 }
 \value{
 A tibble with the resolution audit trail.
 }
 \description{
 Returns the resolution tibble attached to a basket-mode result of
-[cog_gov_search()]. One row per input entry; `status` is one of
-`"resolved"`, `"largest_pop"`, `"ambiguous"`, `"no_match"`. By default
-the `candidates` list-column is dropped for readable printing; pass
-`expand_candidates = TRUE` to keep it.
+\code{\link[=cog_gov_search]{cog_gov_search()}}. One row per input entry; \code{status} is one of
+\code{"resolved"}, \code{"largest_pop"}, \code{"ambiguous"}, \code{"no_match"}. By default
+the \code{candidates} list-column is dropped for readable printing; pass
+\code{expand_candidates = TRUE} to keep it.
 }
 \examples{
 \dontrun{
diff --git a/man/cog_basket_unresolved.Rd b/man/cog_basket_unresolved.Rd
index e6a5145..eca7a4f 100644
--- a/man/cog_basket_unresolved.Rd
+++ b/man/cog_basket_unresolved.Rd
@@ -7,15 +7,15 @@
 cog_basket_unresolved(x)
 }
 \arguments{
-\item{x}{A tibble returned by basket-mode [cog_gov_search()].}
+\item{x}{A tibble returned by basket-mode \code{\link[=cog_gov_search]{cog_gov_search()}}.}
 }
 \value{
-A tibble (subset of [cog_basket_resolution()]).
+A tibble (subset of \code{\link[=cog_basket_resolution]{cog_basket_resolution()}}).
 }
 \description{
-Convenience wrapper that returns just the rows where `status` is
-`"ambiguous"` or `"no_match"` — the ones the user likely wants to
-refine before piping into a query verb. The `candidates` list-column
+Convenience wrapper that returns just the rows where \code{status} is
+\code{"ambiguous"} or \code{"no_match"} — the ones the user likely wants to
+refine before piping into a query verb. The \code{candidates} list-column
 is preserved so the user can drill into ambiguous match sets.
 }
 \examples{
diff --git a/man/cog_categories.Rd b/man/cog_categories.Rd
index 8e72212..6142ef0 100644
--- a/man/cog_categories.Rd
+++ b/man/cog_categories.Rd
@@ -7,33 +7,33 @@
 cog_categories(type = NULL, pattern = NULL)
 }
 \arguments{
-\item{type}{Either `NULL` (default, every row: expenditure, revenue and
-balance), `"spending"`, `"revenue"`, or `"balance"`.}
+\item{type}{Either \code{NULL} (default, every row: expenditure, revenue and
+balance), \code{"spending"}, \code{"revenue"}, or \code{"balance"}.}
 
 \item{pattern}{Optional regex matched case-insensitively against the
-`category` column (e.g. `"Police"` or `"Tax"`).}
+\code{category} column (e.g. \code{"Police"} or \code{"Tax"}).}
 }
 \value{
-Tibble with columns `category`, `category_type`, `subtype`,
-  `n_codes`, `item_codes` (comma-separated, alphabetical). Sorted by
-  `category_type`, `category`, `subtype`. Includes one row per flow for the
-  reserved pseudo-category `"All Categories"`, which carries `NA` for
-  `subtype`, `n_codes` and `item_codes` because it is a query mode rather
-  than a crosswalk entry — see [cog_spending()]'s `category` argument.
+Tibble with columns \code{category}, \code{category_type}, \code{subtype},
+\code{n_codes}, \code{item_codes} (comma-separated, alphabetical). Sorted by
+\code{category_type}, \code{category}, \code{subtype}. Includes one row per flow for the
+reserved pseudo-category \code{"All Categories"}, which carries \code{NA} for
+\code{subtype}, \code{n_codes} and \code{item_codes} because it is a query mode rather
+than a crosswalk entry — see \code{\link[=cog_spending]{cog_spending()}}'s \code{category} argument.
 }
 \description{
 Returns the category taxonomy exposed by the corpus's
-`summary_categories` view, grouped to one row per
-`(category, subtype)` pair. Use this to discover valid `category`
-values for [cog_spending()] / [cog_revenue()] / [cog_balances()] /
-[cog_geographic_rollup()] and to audit which Census item codes feed
+\code{summary_categories} view, grouped to one row per
+\verb{(category, subtype)} pair. Use this to discover valid \code{category}
+values for \code{\link[=cog_spending]{cog_spending()}} / \code{\link[=cog_revenue]{cog_revenue()}} / \code{\link[=cog_balances]{cog_balances()}} /
+\code{\link[=cog_geographic_rollup]{cog_geographic_rollup()}} and to audit which Census item codes feed
 each category.
 }
 \details{
-`subtype` COALESCEs the crosswalk's three subtype columns, so it carries
-`spend_subtype` on expenditure rows, `revenue_subtype` on revenue rows and
-`balance_subtype` on balance rows. Note that [cog_balances()] itself takes
-no `subtype` argument — for holdings, `category` is a strict coarsening of
-`balance_subtype` — but the value is surfaced here because it is the
+\code{subtype} COALESCEs the crosswalk's three subtype columns, so it carries
+\code{spend_subtype} on expenditure rows, \code{revenue_subtype} on revenue rows and
+\code{balance_subtype} on balance rows. Note that \code{\link[=cog_balances]{cog_balances()}} itself takes
+no \code{subtype} argument — for holdings, \code{category} is a strict coarsening of
+\code{balance_subtype} — but the value is surfaced here because it is the
 discovery surface downstream consumers build their vocabulary from.
 }
diff --git a/man/cog_explain.Rd b/man/cog_explain.Rd
index 6a4e712..46b7fce 100644
--- a/man/cog_explain.Rd
+++ b/man/cog_explain.Rd
@@ -7,18 +7,18 @@
 cog_explain(result, format = c("print", "list"))
 }
 \arguments{
-\item{result}{A tibble returned by a `cog_*` verb.}
+\item{result}{A tibble returned by a \verb{cog_*} verb.}
 
-\item{format}{`"print"` (default) for a human-readable cli summary;
-returns `result` invisibly for chaining. `"list"` returns the raw
-provenance list (identical to `attr(result, "provenance")`).}
+\item{format}{\code{"print"} (default) for a human-readable cli summary;
+returns \code{result} invisibly for chaining. \code{"list"} returns the raw
+provenance list (identical to \code{attr(result, "provenance")}).}
 }
 \value{
-Either `result` (invisibly) or the provenance list.
+Either \code{result} (invisibly) or the provenance list.
 }
 \description{
 Prints the structured provenance attached to a tibble returned by any
-`cog_*` verb, or returns it as a list for downstream use (MCP tools,
+\verb{cog_*} verb, or returns it as a list for downstream use (MCP tools,
 dashboards, JSON export).
 }
 \section{Two kinds of series break}{
@@ -26,28 +26,29 @@ dashboards, JSON export).
 Catalogued breaks reach you without being asked for, in two disjoint
 fields, because a caveat about one series and a caveat about the whole
 corpus are different claims:
+\itemize{
+\item \strong{\code{series_break_refs}} — breaks matched against the item codes actually
+present in this result. A break in one code you queried.
+\item \strong{\code{corpus_break_refs}} — breaks catalogued with \code{fin_code = "ALL"},
+which are statements about the corpus rather than about any one code:
+dollar precision across the 1976/1977 boundary (\code{SB085}), imputation
+exclusion from FY2002 (\code{SB087}), the FY2012 dense-to-sparse
+representation change (\code{SB194}), and the FY2017 government-identifier
+change (\code{SB086}). These are selected on the break-year window alone.
+}
 
-* **`series_break_refs`** — breaks matched against the item codes actually
-  present in this result. A break in one code you queried.
-* **`corpus_break_refs`** — breaks catalogued with `fin_code = "ALL"`,
-  which are statements about the corpus rather than about any one code:
-  dollar precision across the 1976/1977 boundary (`SB085`), imputation
-  exclusion from FY2002 (`SB087`), the FY2012 dense-to-sparse
-  representation change (`SB194`), and the FY2017 government-identifier
-  change (`SB086`). These are selected on the break-year window alone.
-
-`SB194` is the one most likely to matter: a query spanning FY2011 to FY2012
+\code{SB194} is the one most likely to matter: a query spanning FY2011 to FY2012
 crosses the boundary where an absent cell stops meaning "Census published
 $0" and starts meaning "not reported".
 }
 
 \section{Other provenance blocks}{
 
-`transformations$units_conversion` records the `$1,000s`-to-dollars
+\code{transformations$units_conversion} records the \verb{$1,000s}-to-dollars
 multiply that every amount column has already had applied.
-`transformations$per_capita` records the population denominator and its
-year range. `coverage` and `coverage_mode` appear on multi-government
-results (see [cog_geographic_rollup()]). `completion` appears when
-`complete = TRUE`. `balance_caveats` appears on [cog_balances()] results.
+\code{transformations$per_capita} records the population denominator and its
+year range. \code{coverage} and \code{coverage_mode} appear on multi-government
+results (see \code{\link[=cog_geographic_rollup]{cog_geographic_rollup()}}). \code{completion} appears when
+\code{complete = TRUE}. \code{balance_caveats} appears on \code{\link[=cog_balances]{cog_balances()}} results.
 }
 
diff --git a/man/cog_find_peers.Rd b/man/cog_find_peers.Rd
index 6de2665..88a571f 100644
--- a/man/cog_find_peers.Rd
+++ b/man/cog_find_peers.Rd
@@ -16,41 +16,41 @@ cog_find_peers(
 )
 }
 \arguments{
-\item{target_govid}{Character scalar — `canonical_govid` of the target.}
+\item{target_govid}{Character scalar — \code{canonical_govid} of the target.}
 
-\item{year}{Integer scalar. Cohort vintage. When `NULL` (default), uses the
+\item{year}{Integer scalar. Cohort vintage. When \code{NULL} (default), uses the
 most recent year for which the target has an observed population in
-`gov_population_yearly`.}
+\code{gov_population_yearly}.}
 
-\item{same_type}{If `TRUE` (default) restrict peers to the target's
-`govs_type`.}
+\item{same_type}{If \code{TRUE} (default) restrict peers to the target's
+\code{govs_type}.}
 
-\item{same_state}{If `TRUE` restrict peers to the target's `fips_state`.
-Default `FALSE`.}
+\item{same_state}{If \code{TRUE} restrict peers to the target's \code{fips_state}.
+Default \code{FALSE}.}
 
 \item{pop_range}{Length-2 numeric vector giving lower/upper bounds.}
 
-\item{is_ratio}{If `TRUE` (default) `pop_range` is multiplied by the
-target's population at `year` to produce absolute bounds. If `FALSE`,
-`pop_range` is interpreted as absolute population counts.}
+\item{is_ratio}{If \code{TRUE} (default) \code{pop_range} is multiplied by the
+target's population at \code{year} to produce absolute bounds. If \code{FALSE},
+\code{pop_range} is interpreted as absolute population counts.}
 
 \item{max_peers}{Integer cap on the number of peers returned.}
 
-\item{coverage}{Survey-cycle handling; see [cog_peer_compare()]. Here it
-governs the cohort VINTAGE when `year` is `NULL`: `"census"` snaps to the
+\item{coverage}{Survey-cycle handling; see \code{\link[=cog_peer_compare]{cog_peer_compare()}}. Here it
+governs the cohort VINTAGE when \code{year} is \code{NULL}: \code{"census"} snaps to the
 most recent census year with an observed population, so a cohort is not
 built from a sample year in which most of the candidate universe is
-absent. `"consistent"` needs a year range, which cohort selection does not
-have, so it selects like `"all"` and is carried on the result as
-`attr(x, "coverage")` for [cog_peer_compare()].}
+absent. \code{"consistent"} needs a year range, which cohort selection does not
+have, so it selects like \code{"all"} and is carried on the result as
+\code{attr(x, "coverage")} for \code{\link[=cog_peer_compare]{cog_peer_compare()}}.}
 }
 \value{
-Tibble with columns `canonical_govid`, `gov_name`, `fips_state`,
-  `population`, `pop_ratio`, `rank`. The cohort year is attached as
-  `attr(x, "cohort_year")`.
+Tibble with columns \code{canonical_govid}, \code{gov_name}, \code{fips_state},
+\code{population}, \code{pop_ratio}, \code{rank}. The cohort year is attached as
+\code{attr(x, "cohort_year")}.
 }
 \description{
 Selects peer governments by combinations of government type, state, and
-population range at a chosen `year`. Peers are ordered by `|log(pop_ratio)|`
+population range at a chosen \code{year}. Peers are ordered by \verb{|log(pop_ratio)|}
 ascending (closest to the target's population first).
 }
diff --git a/man/cog_fiscal_periods.Rd b/man/cog_fiscal_periods.Rd
index 5b5448f..4f88731 100644
--- a/man/cog_fiscal_periods.Rd
+++ b/man/cog_fiscal_periods.Rd
@@ -7,19 +7,19 @@
 cog_fiscal_periods(govid, years)
 }
 \arguments{
-\item{govid}{Character vector of `canonical_govid` values, or a data frame
-with a `canonical_govid` column.}
+\item{govid}{Character vector of \code{canonical_govid} values, or a data frame
+with a \code{canonical_govid} column.}
 
 \item{years}{Integer vector of survey years.}
 }
 \value{
 Tibble with one row per requested government and year, in the
-  order `govid` x `years`: `canonical_govid`, `year`, `fiscal_year_end`
-  (integer MMDD), `period_start` and `period_end` (Dates, inclusive).
+order \code{govid} x \code{years}: \code{canonical_govid}, \code{year}, \code{fiscal_year_end}
+(integer MMDD), \code{period_start} and \code{period_end} (Dates, inclusive).
 }
 \description{
 Returns the calendar months each survey year covers for each government.
-The corpus's `year` is the Census survey year, and a survey year covers
+The corpus's \code{year} is the Census survey year, and a survey year covers
 different months for different governments.
 }
 \section{The survey-year rule}{
@@ -27,17 +27,18 @@ different months for different governments.
 Census assigns each government's fiscal year to a survey year by when the
 fiscal year ended. Survey year Y holds the fiscal year that ended between
 July 1 of Y-1 and June 30 of Y. The corpus records each government-year's
-fiscal year end as `fiscal_year_end`, an MMDD integer such as `630` or
-`1231`.
+fiscal year end as \code{fiscal_year_end}, an MMDD integer such as \code{630} or
+\code{1231}.
 
 Survey year 2017 therefore covers, for a county, city or township:
-
-  * `fiscal_year_end = 630` (June 30): the fiscal year ending June 30,
-    2017, so July 2016 to June 2017.
-  * `930` (September 30): the fiscal year ending September 30, 2016, so
-    October 2015 to September 2016.
-  * `1231` (December 31): the fiscal year ending December 31, 2016, so
-    January to December 2016.
+\itemize{
+\item \code{fiscal_year_end = 630} (June 30): the fiscal year ending June 30,
+2017, so July 2016 to June 2017.
+\item \code{930} (September 30): the fiscal year ending September 30, 2016, so
+October 2015 to September 2016.
+\item \code{1231} (December 31): the fiscal year ending December 31, 2016, so
+January to December 2016.
+}
 
 A city with a December year end and a city with a June year end are both
 "2017" in the corpus, but their figures are six months apart. A September
@@ -55,11 +56,11 @@ way. That treatment has not been checked against the data. For New York
 the same months.
 
 A government can change its fiscal year end, so the periods are looked up
-per government-year. Where the corpus has no `fiscal_year_end` for a
+per government-year. Where the corpus has no \code{fiscal_year_end} for a
 government-year, or the government has no rows that year, the period is
-`NA`. Most of FY1993 is `NA`: 99% of its government-years have no usable
-value in the public corpus as of November 2026. So do 5% to 11% in FY1978
-and FY1987 to FY1990, and under 3% in every other year.
+\code{NA}. Most of FY1993 is \code{NA}: 99\% of its government-years have no usable
+value in the public corpus as of November 2026. So do 5\% to 11\% in FY1978
+and FY1987 to FY1990, and under 3\% in every other year.
 
 From FY2023 the source files mark some rows as estimates carried forward
 from an earlier year. The periods here describe the survey year, not the
@@ -76,7 +77,7 @@ cog_fiscal_periods(
 }
 }
 \seealso{
-[cog_survey_year()] for the reverse mapping; the `fiscal_period`
-  argument of [cog_spending()], [cog_revenue()] and [cog_balances()] to get
-  the same columns on every row.
+\code{\link[=cog_survey_year]{cog_survey_year()}} for the reverse mapping; the \code{fiscal_period}
+argument of \code{\link[=cog_spending]{cog_spending()}}, \code{\link[=cog_revenue]{cog_revenue()}} and \code{\link[=cog_balances]{cog_balances()}} to get
+the same columns on every row.
 }
diff --git a/man/cog_geographic_rollup.Rd b/man/cog_geographic_rollup.Rd
index aa38128..dc034a9 100644
--- a/man/cog_geographic_rollup.Rd
+++ b/man/cog_geographic_rollup.Rd
@@ -16,102 +16,104 @@ cog_geographic_rollup(
 }
 \arguments{
 \item{govids}{Named list with any non-empty subset of elements named
-`state`, `county`, `city`. Each element is a character vector of
-`canonical_govid` values. At least one layer required.}
+\code{state}, \code{county}, \code{city}. Each element is a character vector of
+\code{canonical_govid} values. At least one layer required.}
 
 \item{category}{Single category name or character vector (passed through
-to [cog_spending()]), or the reserved `"All Categories"` for one summed
-row per `(year, canonical_govid, subtype)` covering every category in the
-concept's scope. `"All Categories"` is the efficient way to build a
+to \code{\link[=cog_spending]{cog_spending()}}), or the reserved \code{"All Categories"} for one summed
+row per \verb{(year, canonical_govid, subtype)} covering every category in the
+concept's scope. \code{"All Categories"} is the efficient way to build a
 geographic total: without it a caller must issue one rollup per category
 and sum the results themselves.}
 
 \item{years}{Integer vector of years.}
 
-\item{per_capita}{If `TRUE`, per-capita uses each gov's own per-year
-population from `gov_population_yearly`. Govs with missing population
+\item{per_capita}{If \code{TRUE}, per-capita uses each gov's own per-year
+population from \code{gov_population_yearly}. Govs with missing population
 are excluded from the result.}
 
-\item{adjust_to_year}{Integer base year for CPI-U conversion, or `NULL`.}
+\item{adjust_to_year}{Integer base year for CPI-U conversion, or \code{NULL}.}
 
-\item{expenditure_concept}{`"primary"` (default), `"direct"`, or
-`"total"` -- see [cog_spending()] for the three concepts. `"total"` is
+\item{expenditure_concept}{\code{"primary"} (default), \code{"direct"}, or
+\code{"total"} -- see \code{\link[=cog_spending]{cog_spending()}} for the three concepts. \code{"total"} is
 refused here because combining Total across multiple layers of
 government double-counts intergovernmental transfers (a state's payment
 to a school district is the same dollar the district reports as its own
-Direct spending); `"primary"` and `"direct"` combine safely.}
+Direct spending); \code{"primary"} and \code{"direct"} combine safely.}
 
 \item{coverage}{How to handle the Census of Governments survey cycle,
-  which is a **complete census only in years ending in 2 and 7** -- every
-  other year is a sample, and the sample varies enormously (on the bundled
-  fixture, Wisconsin's 608-city universe reports 597 governments in FY2012
-  and 112 in FY2019).
-
-  * `"all"` (default) -- every unit that reported that year. Unchanged
-    behaviour, so existing code keeps working.
-  * `"census"` -- census years only. Aborts if the requested range holds
-    none, rather than silently returning nothing.
-  * `"consistent"` -- only units reporting in *every* requested year, giving
-    a balanced panel.
+which is a \strong{complete census only in years ending in 2 and 7} -- every
+other year is a sample, and the sample varies enormously (on the bundled
+fixture, Wisconsin's 608-city universe reports 597 governments in FY2012
+and 112 in FY2019).
+\itemize{
+\item \code{"all"} (default) -- every unit that reported that year. Unchanged
+behaviour, so existing code keeps working.
+\item \code{"census"} -- census years only. Aborts if the requested range holds
+none, rather than silently returning nothing.
+\item \code{"consistent"} -- only units reporting in \emph{every} requested year, giving
+a balanced panel.
+}
 
-  Regardless of mode, `provenance$coverage` always carries per-year
-  `n_units_expected`, `n_units_collected`, `n_units_reporting` and
-  `is_census_year`, and `provenance$coverage_mode` records the mode.
-  `is_census_year` is a statement about the **survey calendar**, never a
-  claim of completeness: FY1967 is a census year in which only 97 of
-  Wisconsin's 608 cities report. `n_units_reporting` is
-  category-conditional and is not a response rate on its own -- see
-  "Reading `coverage`" below for what each counter answers.}
+Regardless of mode, \code{provenance$coverage} always carries per-year
+\code{n_units_expected}, \code{n_units_collected}, \code{n_units_reporting} and
+\code{is_census_year}, and \code{provenance$coverage_mode} records the mode.
+\code{is_census_year} is a statement about the \strong{survey calendar}, never a
+claim of completeness: FY1967 is a census year in which only 97 of
+Wisconsin's 608 cities report. \code{n_units_reporting} is
+category-conditional and is not a response rate on its own -- see
+"Reading \code{coverage}" below for what each counter answers.}
 }
 \value{
-Tibble with columns `year`, `layer`, `canonical_govid`, `gov_name`,
-  `spend_subtype`, `category`, `amt_nominal`, optional `amt_real` /
-  `amt_per_capita_nominal` / `amt_per_capita_real`, optional `pop_source`,
-  `codes_included`, `aggregate_fallback`, `scope_note`, `notes`. Carries a
-  `provenance` attribute with `verb = "cog_geographic_rollup"`, `layers`,
-  and `rollup$included_govids` / `rollup$excluded_govids`.
+Tibble with columns \code{year}, \code{layer}, \code{canonical_govid}, \code{gov_name},
+\code{spend_subtype}, \code{category}, \code{amt_nominal}, optional \code{amt_real} /
+\code{amt_per_capita_nominal} / \code{amt_per_capita_real}, optional \code{pop_source},
+\code{codes_included}, \code{aggregate_fallback}, \code{scope_note}, \code{notes}. Carries a
+\code{provenance} attribute with \code{verb = "cog_geographic_rollup"}, \code{layers},
+and \code{rollup$included_govids} / \code{rollup$excluded_govids}.
 }
 \description{
-Wraps [cog_spending()], tags each row with its layer, and attaches a
-human-readable `scope_note` documenting geographic-scope caveats (e.g.
+Wraps \code{\link[=cog_spending]{cog_spending()}}, tags each row with its layer, and attaches a
+human-readable \code{scope_note} documenting geographic-scope caveats (e.g.
 "county totals include areas outside the listed city"). Useful for
 "place portraits" that compare a city to the surrounding county and
 containing state on one set of axes.
 }
 \details{
-When `per_capita = TRUE`, rows whose government has no observed
-population in that year (`pop_source == "unavailable"`) are dropped from
+When \code{per_capita = TRUE}, rows whose government has no observed
+population in that year (\code{pop_source == "unavailable"}) are dropped from
 the result. The dropped govids are recorded in
-`provenance$rollup$excluded_govids`. This excludes special districts
+\code{provenance$rollup$excluded_govids}. This excludes special districts
 (gov type 4) and school districts (gov type 5) from per-capita rollups
-by design — see `vignette('population-denominators')`.
+by design — see \code{vignette('population-denominators')}.
 }
-\section{Reading `coverage`}{
+\section{Reading \code{coverage}}{
 
-`provenance$coverage` carries three per-year counters:
-
-  * `n_units_expected` -- how many governments you asked about.
-  * `n_units_collected` -- how many of those appear in the corpus at all
-    that year (in ANY category), separating governments Census did not
-    collect from governments it did.
-  * `n_units_reporting` -- how many have rows for the SPECIFIC category you
-    requested. This is always <= n_units_collected. A collected government
-    can have no "Police" rows and still pay for policing: the rows leave out
-    intergovernmental expenditure, so a city that pays the county sheriff
-    and reports the payment as intergovernmental (code `M62`) has none. Of
-    34 cities with documented sheriff contracts, nine of 102 city-years
-    (FY2012, FY2017 and FY2022; corpus build of 2026-10-26) looked like
-    this, and the other 93 reported direct Police spending.
+\code{provenance$coverage} carries three per-year counters:
+\itemize{
+\item \code{n_units_expected} -- how many governments you asked about.
+\item \code{n_units_collected} -- how many of those appear in the corpus at all
+that year (in ANY category), separating governments Census did not
+collect from governments it did.
+\item \code{n_units_reporting} -- how many have rows for the SPECIFIC category you
+requested. This is always <= n_units_collected. A collected government
+can have no "Police" rows and still pay for policing: the rows leave out
+intergovernmental expenditure, so a city that pays the county sheriff
+and reports the payment as intergovernmental (code \code{M62}) has none. Of
+34 cities with documented sheriff contracts, nine of 102 city-years
+(FY2012, FY2017 and FY2022; corpus build of 2026-10-26) looked like
+this, and the other 93 reported direct Police spending.
+}
 
-**`n_units_reporting` is category-conditional** and therefore **not a
-response rate**: `n_units_reporting / n_units_expected` conflates sampling
+\strong{\code{n_units_reporting} is category-conditional} and therefore \strong{not a
+response rate}: \code{n_units_reporting / n_units_expected} conflates sampling
 (never collected) with collected governments that have no rows in your
-category. Use `n_units_collected / n_units_expected` for the true
-collection rate, and `n_units_reporting / n_units_collected` for category
+category. Use \code{n_units_collected / n_units_expected} for the true
+collection rate, and \code{n_units_reporting / n_units_collected} for category
 participation among collected units.
 
 In FY2022, a census year, 393 of Georgia's 567 cities report
-`category = "Police"`. Of the other 174, 32 were not collected and 142 were
+\code{category = "Police"}. Of the other 174, 32 were not collected and 142 were
 collected with no Police rows. A city that contracts with the county sheriff
 lands in the second group only when it reports no direct Police spending, for
 example when it files the payment as intergovernmental expenditure; the
@@ -120,7 +122,7 @@ counters do not say how many of the 142 do.
 A census year and a sample year collect different sets of governments, so
 participation across the two mixes a change in which governments were
 collected with any change in spending. Compare census years with each other,
-or the same governments across years. `is_census_year` marks which is which,
-and `vignette("coverage-counters")` has worked examples.
+or the same governments across years. \code{is_census_year} marks which is which,
+and \code{vignette("coverage-counters")} has worked examples.
 }
 
diff --git a/man/cog_gov_search.Rd b/man/cog_gov_search.Rd
index 1656b15..22f33a6 100644
--- a/man/cog_gov_search.Rd
+++ b/man/cog_gov_search.Rd
@@ -16,69 +16,72 @@ cog_gov_search(
 \item{name}{Character vector of place name(s). Length 1 = utility mode;
 length >1 = basket mode.}
 
-\item{state}{2-letter USPS abbreviation (e.g. `"FL"`), FIPS integer
-(e.g. `12`), or `NULL`. In basket mode, length 1 recycles across
-all entries; otherwise must match `length(name)`.}
+\item{state}{2-letter USPS abbreviation (e.g. \code{"FL"}), FIPS integer
+(e.g. \code{12}), or \code{NULL}. In basket mode, length 1 recycles across
+all entries; otherwise must match \code{length(name)}.}
 
-\item{type}{Government type: integer in `0:3` or one of `"state"`,
-`"county"`, `"city"`, `"township"`, or `NA`/`NULL`. Per-row optional
-in basket mode (recycles from length 1). Excluded types `4`/`5` (or
-`"special_district"` / `"school_district"`) trigger an explanatory
+\item{type}{Government type: integer in \code{0:3} or one of \code{"state"},
+\code{"county"}, \code{"city"}, \code{"township"}, or \code{NA}/\code{NULL}. Per-row optional
+in basket mode (recycles from length 1). Excluded types \code{4}/\code{5} (or
+\code{"special_district"} / \code{"school_district"}) trigger an explanatory
 message and an empty result.}
 
-\item{limit}{Maximum number of rows to return, applied in SQL. `NULL`
+\item{limit}{Maximum number of rows to return, applied in SQL. \code{NULL}
 (default) returns every match -- which, with no other filter, is the
 entire crosswalk. Utility mode only: pagination has no meaning in basket
 mode, where the result is one resolved row per requested name in input
 order, and is refused there with class
-`uscogdata_basket_pagination_conflict`.}
+\code{uscogdata_basket_pagination_conflict}.}
 
-\item{offset}{Rows to skip before `limit` starts counting (0-based).
-Ignored if `limit` is `NULL`; defaults to `0L` when `limit` is set.}
+\item{offset}{Rows to skip before \code{limit} starts counting (0-based).
+Ignored if \code{limit} is \code{NULL}; defaults to \code{0L} when \code{limit} is set.}
 }
 \value{
-A tibble of `canonical_fips_xwalk` rows. In utility mode, all
-  matches sorted by `population_acs` desc, ties broken by
-  `canonical_govid`. In basket mode, resolved rows in input order, with
-  `attr(., "resolution")` set to the sidecar tibble.
+A tibble of \code{canonical_fips_xwalk} rows. In utility mode, all
+matches sorted by \code{population_acs} desc, ties broken by
+\code{canonical_govid}. In basket mode, resolved rows in input order, with
+\code{attr(., "resolution")} set to the sidecar tibble.
 
-  When `limit` is set, carries a `total_rows` attribute: the full
-  unpaginated match count, computed by the same query (`COUNT(*) OVER()`)
-  rather than a second scan.
+When \code{limit} is set, carries a \code{total_rows} attribute: the full
+unpaginated match count, computed by the same query (\verb{COUNT(*) OVER()})
+rather than a second scan.
 }
 \description{
-Resolves human-readable place names into rows of `canonical_fips_xwalk`,
+Resolves human-readable place names into rows of \code{canonical_fips_xwalk},
 the cross-vintage canonical-government registry. Operates in two modes:
 }
 \details{
-* **Utility mode** (single `name`, the original behavior): returns all
-  rows whose `gov_name` contains `name` as a **literal, case-insensitive
-  substring**, sorted by `population_acs` descending. Useful for
-  exploratory lookups. Regex metacharacters in `name` are escaped, so a
-  government is findable by its own complete name even when that name
-  contains parentheses or a period.
-* **Basket mode** (`length(name) > 1`): resolves each input row to a
-  single canonical govid and returns a tibble in input order, suitable
-  for piping straight into [cog_spending()] / [cog_revenue()] /
-  [cog_geographic_rollup()]. Carries an audit sidecar accessible via
-  [cog_basket_resolution()] / [cog_basket_unresolved()].
-
+\itemize{
+\item \strong{Utility mode} (single \code{name}, the original behavior): returns all
+rows whose \code{gov_name} contains \code{name} as a \strong{literal, case-insensitive
+substring}, sorted by \code{population_acs} descending. Useful for
+exploratory lookups. Regex metacharacters in \code{name} are escaped, so a
+government is findable by its own complete name even when that name
+contains parentheses or a period.
+\item \strong{Basket mode} (\code{length(name) > 1}): resolves each input row to a
+single canonical govid and returns a tibble in input order, suitable
+for piping straight into \code{\link[=cog_spending]{cog_spending()}} / \code{\link[=cog_revenue]{cog_revenue()}} /
+\code{\link[=cog_geographic_rollup]{cog_geographic_rollup()}}. Carries an audit sidecar accessible via
+\code{\link[=cog_basket_resolution]{cog_basket_resolution()}} / \code{\link[=cog_basket_unresolved]{cog_basket_unresolved()}}.
+}
 
-**Basket-mode resolution algorithm** (per input row):
-1. Filter `canonical_fips_xwalk` by `state` and (if non-NA) `type`.
-2. **Exact pass:** case-insensitive equality against `gov_name`.
-   Single hit -> resolved. Multiple -> step 4.
-3. **Substring fallback:** case-insensitive literal substring against
-   `gov_name` (metacharacters escaped).
-   Single hit -> resolved (`match_method = "substring"`). Zero hits ->
-   `status = "no_match"`. Multiple hits -> step 4.
-4. **Disambiguation:** if matches share one `govs_type`, pick the
-   largest-population row (`status = "largest_pop"`). If they span >=2
-   types, no row is added (`status = "ambiguous"`); the user should
-   re-run with `type` specified.
+\strong{Basket-mode resolution algorithm} (per input row):
+\enumerate{
+\item Filter \code{canonical_fips_xwalk} by \code{state} and (if non-NA) \code{type}.
+\item \strong{Exact pass:} case-insensitive equality against \code{gov_name}.
+Single hit -> resolved. Multiple -> step 4.
+\item \strong{Substring fallback:} case-insensitive literal substring against
+\code{gov_name} (metacharacters escaped).
+Single hit -> resolved (\code{match_method = "substring"}). Zero hits ->
+\code{status = "no_match"}. Multiple hits -> step 4.
+\item \strong{Disambiguation:} if matches share one \code{govs_type}, pick the
+largest-population row (\code{status = "largest_pop"}). If they span >=2
+types, no row is added (\code{status = "ambiguous"}); the user should
+re-run with \code{type} specified.
+}
 
 Resolved rows form the returned tibble in input order. Unresolved
-inputs (`ambiguous` / `no_match`) appear only in the sidecar.
+inputs (\code{ambiguous} / \code{no_match}) appear only in the sidecar.
 }
 \examples{
 \dontrun{
@@ -113,6 +116,6 @@ refined <- cog_gov_search(
 }
 }
 \seealso{
-[cog_basket_resolution()], [cog_basket_unresolved()],
-  [cog_spending()], [cog_revenue()].
+\code{\link[=cog_basket_resolution]{cog_basket_resolution()}}, \code{\link[=cog_basket_unresolved]{cog_basket_unresolved()}},
+\code{\link[=cog_spending]{cog_spending()}}, \code{\link[=cog_revenue]{cog_revenue()}}.
 }
diff --git a/man/cog_manifest.Rd b/man/cog_manifest.Rd
index a04718f..85ad749 100644
--- a/man/cog_manifest.Rd
+++ b/man/cog_manifest.Rd
@@ -7,12 +7,12 @@
 cog_manifest()
 }
 \value{
-Named list: `schema_version`, `built_at`, `pipeline_commit`,
-  `data_vintage`, `scope`, `years` (schema v5+), `schema`, `files`.
+Named list: \code{schema_version}, \code{built_at}, \code{pipeline_commit},
+\code{data_vintage}, \code{scope}, \code{years} (schema v5+), \code{schema}, \code{files}.
 }
 \description{
 Opens a session (connecting to the configured corpus) if none is active,
-then returns the manifest exactly as parsed from `manifest.json`. Useful
-for consumers that need the published year range (`years` block, schema
+then returns the manifest exactly as parsed from \code{manifest.json}. Useful
+for consumers that need the published year range (\code{years} block, schema
 v5+) or the partition list without issuing a data query.
 }
diff --git a/man/cog_mirror.Rd b/man/cog_mirror.Rd
index 23b292e..8099953 100644
--- a/man/cog_mirror.Rd
+++ b/man/cog_mirror.Rd
@@ -14,22 +14,22 @@ cog_mirror(
 \arguments{
 \item{dest}{Destination directory. Created if missing.}
 
-\item{include}{Subset of `c("long", "metadata", "docs")` controlling
+\item{include}{Subset of \code{c("long", "metadata", "docs")} controlling
 which manifest sections to mirror.}
 
-\item{overwrite}{If `TRUE`, re-copy even when the local SHA matches.}
+\item{overwrite}{If \code{TRUE}, re-copy even when the local SHA matches.}
 
-\item{progress}{If `TRUE`, show a cli progress bar.}
+\item{progress}{If \code{TRUE}, show a cli progress bar.}
 }
 \value{
-Invisibly, a tibble of `(path, sha256, size_bytes, status)`
-  rows where `status` is `"downloaded"` or `"cached"`.
+Invisibly, a tibble of \verb{(path, sha256, size_bytes, status)}
+rows where \code{status} is \code{"downloaded"} or \code{"cached"}.
 }
 \description{
 Downloads (or copies, for local-path fixture URLs) every file listed in
-the session's `manifest.json` into `dest`, preserving the relative path
+the session's \code{manifest.json} into \code{dest}, preserving the relative path
 structure. Files already present with a matching SHA-256 hash are
-skipped unless `overwrite = TRUE`. The resulting directory can be
-pointed at via `options(uscogdata.url = paste0(dest, "/"))` for offline
+skipped unless \code{overwrite = TRUE}. The resulting directory can be
+pointed at via \code{options(uscogdata.url = paste0(dest, "/"))} for offline
 analysis.
 }
diff --git a/man/cog_peer_compare.Rd b/man/cog_peer_compare.Rd
index 1c5d4c8..6afba08 100644
--- a/man/cog_peer_compare.Rd
+++ b/man/cog_peer_compare.Rd
@@ -18,123 +18,123 @@ cog_peer_compare(
 \arguments{
 \item{target_govid}{Character scalar.}
 
-\item{peers}{A tibble from [cog_find_peers()] or a character vector of
-`canonical_govid`s.}
+\item{peers}{A tibble from \code{\link[=cog_find_peers]{cog_find_peers()}} or a character vector of
+\code{canonical_govid}s.}
 
 \item{category}{Character scalar or vector.}
 
 \item{years}{Integer vector.}
 
-\item{per_capita}{Default `TRUE` — peer compare usually normalizes by
+\item{per_capita}{Default \code{TRUE} — peer compare usually normalizes by
 population.}
 
-\item{adjust_to_year}{Integer base year for CPI-U conversion or `NULL`.}
+\item{adjust_to_year}{Integer base year for CPI-U conversion or \code{NULL}.}
 
-\item{expenditure_concept}{`"primary"` (default), `"direct"`, or
-`"total"` -- see [cog_spending()] for the three concepts. `"total"` is
+\item{expenditure_concept}{\code{"primary"} (default), \code{"direct"}, or
+\code{"total"} -- see \code{\link[=cog_spending]{cog_spending()}} for the three concepts. \code{"total"} is
 refused here because combining Total across peer sets counts
-intergovernmental transfers twice; `"primary"` and `"direct"` combine
+intergovernmental transfers twice; \code{"primary"} and \code{"direct"} combine
 safely.}
 
 \item{coverage}{How to handle the Census of Governments survey cycle,
-  which is a **complete census only in years ending in 2 and 7** -- every
-  other year is a sample, and the sample varies enormously (on the bundled
-  fixture, Wisconsin's 608-city universe reports 597 governments in FY2012
-  and 112 in FY2019).
-
-  * `"all"` (default) -- every unit that reported that year. Unchanged
-    behaviour, so existing code keeps working.
-  * `"census"` -- census years only. Aborts if the requested range holds
-    none, rather than silently returning nothing.
-  * `"consistent"` -- only units reporting in *every* requested year, giving
-    a balanced panel.
-
-  Regardless of mode, `provenance$coverage` always carries per-year
-  `n_units_expected`, `n_units_collected`, `n_units_reporting` and
-  `is_census_year`, and `provenance$coverage_mode` records the mode.
-  `is_census_year` is a statement about the **survey calendar**, never a
-  claim of completeness: FY1967 is a census year in which only 97 of
-  Wisconsin's 608 cities report. `n_units_reporting` is
-  category-conditional and is not a response rate on its own -- see
-  "Reading `coverage`" below for what each counter answers.
-
-  The comparison target is exempt from `"consistent"` balancing -- it is the
-  subject of the comparison, not a member of the cohort -- and the
-  `summary_*` quantiles are computed AFTER the filter, so they describe the
-  cohort actually returned. `n_units_reporting` counts peers only, against
-  the cohort size: "3 of your 15 peers reported in FY2019".}
+which is a \strong{complete census only in years ending in 2 and 7} -- every
+other year is a sample, and the sample varies enormously (on the bundled
+fixture, Wisconsin's 608-city universe reports 597 governments in FY2012
+and 112 in FY2019).
+\itemize{
+\item \code{"all"} (default) -- every unit that reported that year. Unchanged
+behaviour, so existing code keeps working.
+\item \code{"census"} -- census years only. Aborts if the requested range holds
+none, rather than silently returning nothing.
+\item \code{"consistent"} -- only units reporting in \emph{every} requested year, giving
+a balanced panel.
+}
+
+Regardless of mode, \code{provenance$coverage} always carries per-year
+\code{n_units_expected}, \code{n_units_collected}, \code{n_units_reporting} and
+\code{is_census_year}, and \code{provenance$coverage_mode} records the mode.
+\code{is_census_year} is a statement about the \strong{survey calendar}, never a
+claim of completeness: FY1967 is a census year in which only 97 of
+Wisconsin's 608 cities report. \code{n_units_reporting} is
+category-conditional and is not a response rate on its own -- see
+"Reading \code{coverage}" below for what each counter answers.
+
+The comparison target is exempt from \code{"consistent"} balancing -- it is the
+subject of the comparison, not a member of the cohort -- and the
+\verb{summary_*} quantiles are computed AFTER the filter, so they describe the
+cohort actually returned. \code{n_units_reporting} counts peers only, against
+the cohort size: "3 of your 15 peers reported in FY2019".}
 }
 \value{
-Tibble matching [cog_spending()]'s columns, plus a `role`
-  column taking values `"target"`, `"peer"`, `"summary_p25"`,
-  `"summary_p50"`, or `"summary_p75"`, `target_rank` (target's rank
-  among target+peers at `max(years)`, NA for other rows), and
-  `cohort_year` (the year used to build the peer cohort, read from
-  `attr(peers, "cohort_year")`; `NA` when `peers` was a bare character
-  vector). Provenance reports `verb = "cog_peer_compare"`, `peer_count`,
-  `cohort_year`, and `cohort_govids`.
-
-  **The `summary_*` rows are per-category quantiles: they are not additive.**
-  Each one is computed **within each `(year, spend_subtype,
-  category)` cell** across the peer set, so a `summary_p50` row is *the
-  median peer's value in that one category*, not *the value of the median
-  peer's total*. The median peer for Police and the median peer for Fire
-  are usually different governments, so summing `summary_*` rows across
-  categories does not give any peer's total and misstates the band it
-  appears to describe — measured at −32.7% to +251.0% across 24 years on
-  one cohort, with a sign flip at FY2012.
-
-  Facet by `role` **and** `category` (the documented use, and what the
-  rows are built for). For a genuine "median peer's total spending" line,
-  sum each peer's own categories first and take the quantile of those
-  per-government totals:
-
-  ```r
-  library(dplyr)
-  cmp |>
-    filter(role %in% c("target", "peer")) |>
-    group_by(year, role, canonical_govid) |>
-    summarise(total = sum(amt_per_capita_real, na.rm = TRUE), .groups = "drop") |>
-    filter(role == "peer") |>
-    group_by(year) |>
-    summarise(p50 = quantile(total, 0.5, na.rm = TRUE))
-  ```
+Tibble matching \code{\link[=cog_spending]{cog_spending()}}'s columns, plus a \code{role}
+column taking values \code{"target"}, \code{"peer"}, \code{"summary_p25"},
+\code{"summary_p50"}, or \code{"summary_p75"}, \code{target_rank} (target's rank
+among target+peers at \code{max(years)}, NA for other rows), and
+\code{cohort_year} (the year used to build the peer cohort, read from
+\code{attr(peers, "cohort_year")}; \code{NA} when \code{peers} was a bare character
+vector). Provenance reports \code{verb = "cog_peer_compare"}, \code{peer_count},
+\code{cohort_year}, and \code{cohort_govids}.
+
+\strong{The \verb{summary_*} rows are per-category quantiles: they are not additive.}
+Each one is computed \strong{within each \verb{(year, spend_subtype, category)} cell} across the peer set, so a \code{summary_p50} row is \emph{the
+median peer's value in that one category}, not \emph{the value of the median
+peer's total}. The median peer for Police and the median peer for Fire
+are usually different governments, so summing \verb{summary_*} rows across
+categories does not give any peer's total and misstates the band it
+appears to describe — measured at −32.7\% to +251.0\% across 24 years on
+one cohort, with a sign flip at FY2012.
+
+Facet by \code{role} \strong{and} \code{category} (the documented use, and what the
+rows are built for). For a genuine "median peer's total spending" line,
+sum each peer's own categories first and take the quantile of those
+per-government totals:
+
+\if{html}{\out{<div class="sourceCode r">}}\preformatted{library(dplyr)
+cmp |>
+  filter(role \%in\% c("target", "peer")) |>
+  group_by(year, role, canonical_govid) |>
+  summarise(total = sum(amt_per_capita_real, na.rm = TRUE), .groups = "drop") |>
+  filter(role == "peer") |>
+  group_by(year) |>
+  summarise(p50 = quantile(total, 0.5, na.rm = TRUE))
+}\if{html}{\out{</div>}}
 }
 \description{
 Pulls spending for the target plus a peer set (either a
-[cog_find_peers()] result or a character vector of `canonical_govid`) and
-appends peer-distribution summary rows (`summary_p25`, `summary_p50`,
-`summary_p75`) so the result can be faceted by `role` in a single ggplot
-call. Those summary rows are quantiles **within each category**, not
-quantiles of each peer's total — see the `@return` section before summing
+\code{\link[=cog_find_peers]{cog_find_peers()}} result or a character vector of \code{canonical_govid}) and
+appends peer-distribution summary rows (\code{summary_p25}, \code{summary_p50},
+\code{summary_p75}) so the result can be faceted by \code{role} in a single ggplot
+call. Those summary rows are quantiles \strong{within each category}, not
+quantiles of each peer's total — see the \verb{@return} section before summing
 them.
 }
-\section{Reading `coverage`}{
-
-`provenance$coverage` carries three per-year counters:
-
-  * `n_units_expected` -- how many governments you asked about.
-  * `n_units_collected` -- how many of those appear in the corpus at all
-    that year (in ANY category), separating governments Census did not
-    collect from governments it did.
-  * `n_units_reporting` -- how many have rows for the SPECIFIC category you
-    requested. This is always <= n_units_collected. A collected government
-    can have no "Police" rows and still pay for policing: the rows leave out
-    intergovernmental expenditure, so a city that pays the county sheriff
-    and reports the payment as intergovernmental (code `M62`) has none. Of
-    34 cities with documented sheriff contracts, nine of 102 city-years
-    (FY2012, FY2017 and FY2022; corpus build of 2026-10-26) looked like
-    this, and the other 93 reported direct Police spending.
-
-**`n_units_reporting` is category-conditional** and therefore **not a
-response rate**: `n_units_reporting / n_units_expected` conflates sampling
+\section{Reading \code{coverage}}{
+
+\code{provenance$coverage} carries three per-year counters:
+\itemize{
+\item \code{n_units_expected} -- how many governments you asked about.
+\item \code{n_units_collected} -- how many of those appear in the corpus at all
+that year (in ANY category), separating governments Census did not
+collect from governments it did.
+\item \code{n_units_reporting} -- how many have rows for the SPECIFIC category you
+requested. This is always <= n_units_collected. A collected government
+can have no "Police" rows and still pay for policing: the rows leave out
+intergovernmental expenditure, so a city that pays the county sheriff
+and reports the payment as intergovernmental (code \code{M62}) has none. Of
+34 cities with documented sheriff contracts, nine of 102 city-years
+(FY2012, FY2017 and FY2022; corpus build of 2026-10-26) looked like
+this, and the other 93 reported direct Police spending.
+}
+
+\strong{\code{n_units_reporting} is category-conditional} and therefore \strong{not a
+response rate}: \code{n_units_reporting / n_units_expected} conflates sampling
 (never collected) with collected governments that have no rows in your
-category. Use `n_units_collected / n_units_expected` for the true
-collection rate, and `n_units_reporting / n_units_collected` for category
+category. Use \code{n_units_collected / n_units_expected} for the true
+collection rate, and \code{n_units_reporting / n_units_collected} for category
 participation among collected units.
 
 In FY2022, a census year, 393 of Georgia's 567 cities report
-`category = "Police"`. Of the other 174, 32 were not collected and 142 were
+\code{category = "Police"}. Of the other 174, 32 were not collected and 142 were
 collected with no Police rows. A city that contracts with the county sheriff
 lands in the second group only when it reports no direct Police spending, for
 example when it files the payment as intergovernmental expenditure; the
@@ -143,7 +143,7 @@ counters do not say how many of the 142 do.
 A census year and a sample year collect different sets of governments, so
 participation across the two mixes a change in which governments were
 collected with any change in spending. Compare census years with each other,
-or the same governments across years. `is_census_year` marks which is which,
-and `vignette("coverage-counters")` has worked examples.
+or the same governments across years. \code{is_census_year} marks which is which,
+and \code{vignette("coverage-counters")} has worked examples.
 }
 
diff --git a/man/cog_recipes.Rd b/man/cog_recipes.Rd
index 149fb76..6ae72f7 100644
--- a/man/cog_recipes.Rd
+++ b/man/cog_recipes.Rd
@@ -8,15 +8,15 @@ cog_recipes(pattern = NULL)
 }
 \arguments{
 \item{pattern}{Optional regex matched case-insensitively against
-`recipe_id` or `label`.}
+\code{recipe_id} or \code{label}.}
 }
 \value{
-Tibble with columns `recipe_id`, `label`, `n_components`,
-  `year_min`, `year_max` (the min/max component year coverage), sorted by
-  `recipe_id`.
+Tibble with columns \code{recipe_id}, \code{label}, \code{n_components},
+\code{year_min}, \code{year_max} (the min/max component year coverage), sorted by
+\code{recipe_id}.
 }
 \description{
-Recipes are multi-code cross-vintage series (see [cog_spending()]'s
-`recipe` argument) catalogued in the corpus's `harmonization_recipes`
-table. Use this to discover valid `recipe` ids.
+Recipes are multi-code cross-vintage series (see \code{\link[=cog_spending]{cog_spending()}}'s
+\code{recipe} argument) catalogued in the corpus's \code{harmonization_recipes}
+table. Use this to discover valid \code{recipe} ids.
 }
diff --git a/man/cog_revenue.Rd b/man/cog_revenue.Rd
index d53ae2d..c83620b 100644
--- a/man/cog_revenue.Rd
+++ b/man/cog_revenue.Rd
@@ -22,163 +22,163 @@ cog_revenue(
 )
 }
 \arguments{
-\item{govid}{Character vector of `canonical_govid` values, or `NULL` to name
-the cohort by `state`/`type` instead. One of `govid`, `state`, or `type`
+\item{govid}{Character vector of \code{canonical_govid} values, or \code{NULL} to name
+the cohort by \code{state}/\code{type} instead. One of \code{govid}, \code{state}, or \code{type}
 is required.}
 
 \item{years}{Integer vector of years.}
 
 \item{category}{Character vector of category names (from
-`summary_categories.category`), or `NULL` for all categories broken out
-one row each. The reserved value `"All Categories"` instead returns a
-single summed row per `(year, canonical_govid, subtype)`, covering every
+\code{summary_categories.category}), or \code{NULL} for all categories broken out
+one row each. The reserved value \code{"All Categories"} instead returns a
+single summed row per \verb{(year, canonical_govid, subtype)}, covering every
 category inside the requested concept's subtype scope. It cannot be
 combined with other category names, and it is not the same thing as
-`revenue_concept = "total"`: the concept chooses which subtypes are in
-scope, `"All Categories"` chooses whether rows inside that scope are
+\code{revenue_concept = "total"}: the concept chooses which subtypes are in
+scope, \code{"All Categories"} chooses whether rows inside that scope are
 broken out or summed. Because the result keeps one row per
-`revenue_subtype`, filtering the returned frame to
-`revenue_subtype == "own_source"` gives an own-source revenue total.}
+\code{revenue_subtype}, filtering the returned frame to
+\code{revenue_subtype == "own_source"} gives an own-source revenue total.}
 
-\item{per_capita}{If `TRUE`, adds `amt_per_capita_nominal` (and
-`amt_per_capita_real` when `adjust_to_year` is set) using the per-year
-Census F-33 population from `gov_population_yearly`. Result also gains
-a `pop_source` column with values `"census_f33"` or `"unavailable"`
+\item{per_capita}{If \code{TRUE}, adds \code{amt_per_capita_nominal} (and
+\code{amt_per_capita_real} when \code{adjust_to_year} is set) using the per-year
+Census F-33 population from \code{gov_population_yearly}. Result also gains
+a \code{pop_source} column with values \code{"census_f33"} or \code{"unavailable"}
 (the latter for gov types 4/5 and any row whose population is missing
 in that year).}
 
 \item{adjust_to_year}{Integer base year for CPI-U real-dollar conversion,
-or `NULL` for nominal only.}
+or \code{NULL} for nominal only.}
 
-\item{basis}{`"harmonized"` (default) sums item codes through the
+\item{basis}{\code{"harmonized"} (default) sums item codes through the
 cross-vintage harmonization mapping (folding series-break-affected
 codes onto a comparable target and excluding aggregate / discontinued
-rows -- see the `harmonization` block in `cog_explain()`); `"raw"`
+rows -- see the \code{harmonization} block in \code{cog_explain()}); \code{"raw"}
 reproduces the pre-Phase-R2 behavior (published item codes, no
-folding). On a corpus with `schema_version < 5` (no harmonization
-tables), `basis` silently resolves to `"raw"` when left at its default
+folding). On a corpus with \code{schema_version < 5} (no harmonization
+tables), \code{basis} silently resolves to \code{"raw"} when left at its default
 and the resolution is recorded in the provenance; explicitly passing
-`basis = "harmonized"` on such a corpus aborts. Ignored when `recipe`
+\code{basis = "harmonized"} on such a corpus aborts. Ignored when \code{recipe}
 is set (see below).}
 
-\item{recipe}{Optional harmonization recipe id (see [cog_recipes()]) for
+\item{recipe}{Optional harmonization recipe id (see \code{\link[=cog_recipes]{cog_recipes()}}) for
 multi-code cross-vintage series that a 1:1 harmonized_code mapping
 can't express (e.g. a wide-era aggregate that only splits into leaf
-codes in the modern era). Mutually exclusive with `category`. The
-result's subtype column reads `"recipe"` and `category` reads the
-recipe's label. Requires `schema_version >= 5`. A recipe query bypasses
-`basis` entirely (it joins `long` directly rather than going through
-the `*_annotated`/`*_annotated_harmonized` views), so the `basis`
+codes in the modern era). Mutually exclusive with \code{category}. The
+result's subtype column reads \code{"recipe"} and \code{category} reads the
+recipe's label. Requires \code{schema_version >= 5}. A recipe query bypasses
+\code{basis} entirely (it joins \code{long} directly rather than going through
+the \verb{*_annotated}/\verb{*_annotated_harmonized} views), so the \code{basis}
 argument is ignored and the result's provenance reports
-`basis = "recipe"` with an inert `harmonization` block (`applied =
-FALSE`, pointing at the `recipe` block instead) rather than a
-possibly-misleading `"harmonized"`/`"raw"` value.}
+\code{basis = "recipe"} with an inert \code{harmonization} block (\code{applied = FALSE}, pointing at the \code{recipe} block instead) rather than a
+possibly-misleading \code{"harmonized"}/\code{"raw"} value.}
 
 \item{revenue_concept}{Which of Census's two published revenue concepts to
-  return. Concepts are defined as sets of the crosswalk's `revenue_subtype`
-  values -- never as item-code first letters, which cannot classify
-  correctly (prefix `Y` spans revenue, expenditure and balance codes, and
-  prefix `X` does the same):
-
-  * `"general"` (default) -- Census General Revenue: `own_source` +
-    `federal` + `state` + `local_aid`. The manual defines this concept by
-    subtraction (section 4.3: *"General revenue comprises all revenue
-    except that classified as liquor store, utility, or insurance trust
-    revenue"*), so utility (`A91`-`A94`), liquor store (`A90`) and
-    insurance trust revenue are all excluded.
-  * `"total"` -- Census Total Revenue: every revenue subtype, i.e.
-    `general` plus utility, liquor store, and insurance trust revenue
-    (`Y01`/`Y02`/`Y04`/`Y11`/`Y12`/`Y51`/`Y52` and the employee-retirement
-    `X01`/`X02`/`X05`/`X08`).
-
-  The two are related by Census's own identity, `Total Revenue = General +
-  Utility + Liquor Store + Insurance Trust`.
-
-  Note that the employee-retirement (`X`) codes stop at FY2016, when those
-  systems moved out of the annual finance file into the separate Annual
-  Survey of Public Pensions, so a `"total"` series steps down at the
-  FY2016/FY2017 seam for reasons that are about collection scope rather
-  than revenue (series breaks `SB197`-`SB202`).}
-
-\item{complete}{If `TRUE`, fill the requested grid so that a cell the
-  corpus does not carry still appears, labelled with **why** it is
-  missing, and add a `value_source` column to every row:
-
-  * `"reported"` — the corpus carries this cell.
-  * `"census_zero"` — dense-source year (`<= FY2011`), cell absent:
-    Census published `$0`. `amt_nominal` is `0`.
-  * `"not_reported"` — sparse-source year (`>= FY2012`), cell absent: the
-    government did not report, and the value is unknown. `amt_nominal` is
-    `NA`, **not** `0` — writing a zero there would invent data.
-
-  The grid comes from the corpus's `code_set` table, scoped to each
-  government's own type, so a county is never filled with cells only a
-  state can report. Reported rows are passed through untouched.
-
-  Defaults to `FALSE` (the historical behaviour: absent cells simply do
-  not appear). Needs a corpus published from 2026-07-29 onward, which is
-  when `representation`/`code_set` began shipping; aborts with class
-  `uscogdata_representation_unavailable` otherwise. Not available with
-  `recipe` or with `expenditure_concept = "total"` (class
-  `uscogdata_complete_unsupported`) — neither draws its cells from
-  `code_set`.}
+return. Concepts are defined as sets of the crosswalk's \code{revenue_subtype}
+values -- never as item-code first letters, which cannot classify
+correctly (prefix \code{Y} spans revenue, expenditure and balance codes, and
+prefix \code{X} does the same):
+\itemize{
+\item \code{"general"} (default) -- Census General Revenue: \code{own_source} +
+\code{federal} + \code{state} + \code{local_aid}. The manual defines this concept by
+subtraction (section 4.3: \emph{"General revenue comprises all revenue
+except that classified as liquor store, utility, or insurance trust
+revenue"}), so utility (\code{A91}-\code{A94}), liquor store (\code{A90}) and
+insurance trust revenue are all excluded.
+\item \code{"total"} -- Census Total Revenue: every revenue subtype, i.e.
+\code{general} plus utility, liquor store, and insurance trust revenue
+(\code{Y01}/\code{Y02}/\code{Y04}/\code{Y11}/\code{Y12}/\code{Y51}/\code{Y52} and the employee-retirement
+\code{X01}/\code{X02}/\code{X05}/\code{X08}).
+}
+
+The two are related by Census's own identity, \verb{Total Revenue = General + Utility + Liquor Store + Insurance Trust}.
+
+Note that the employee-retirement (\code{X}) codes stop at FY2016, when those
+systems moved out of the annual finance file into the separate Annual
+Survey of Public Pensions, so a \code{"total"} series steps down at the
+FY2016/FY2017 seam for reasons that are about collection scope rather
+than revenue (series breaks \code{SB197}-\code{SB202}).}
+
+\item{complete}{If \code{TRUE}, fill the requested grid so that a cell the
+corpus does not carry still appears, labelled with \strong{why} it is
+missing, and add a \code{value_source} column to every row:
+\itemize{
+\item \code{"reported"} — the corpus carries this cell.
+\item \code{"census_zero"} — dense-source year (\verb{<= FY2011}), cell absent:
+Census published \verb{$0}. \code{amt_nominal} is \code{0}.
+\item \code{"not_reported"} — sparse-source year (\verb{>= FY2012}), cell absent: the
+government did not report, and the value is unknown. \code{amt_nominal} is
+\code{NA}, \strong{not} \code{0} — writing a zero there would invent data.
+}
+
+The grid comes from the corpus's \code{code_set} table, scoped to each
+government's own type, so a county is never filled with cells only a
+state can report. Reported rows are passed through untouched.
+
+Defaults to \code{FALSE} (the historical behaviour: absent cells simply do
+not appear). Needs a corpus published from 2026-07-29 onward, which is
+when \code{representation}/\code{code_set} began shipping; aborts with class
+\code{uscogdata_representation_unavailable} otherwise. Not available with
+\code{recipe} or with \code{expenditure_concept = "total"} (class
+\code{uscogdata_complete_unsupported}) — neither draws its cells from
+\code{code_set}.}
 
 \item{limit}{Maximum number of result rows to return, pushed into the SQL
-query itself (`LIMIT`/`OFFSET`) rather than applied after the full
-result is materialized. `NULL` (the default) returns every matching row,
+query itself (\code{LIMIT}/\code{OFFSET}) rather than applied after the full
+result is materialized. \code{NULL} (the default) returns every matching row,
 exactly as before this parameter existed. Mutually exclusive with
-`recipe` and with `complete = TRUE` -- see `offset` and `total_rows`.}
-
-\item{offset}{Rows to skip before `limit` starts counting (0-based).
-Ignored if `limit` is `NULL`; defaults to `0L` when `limit` is set.}
-
-\item{state, type}{Name the cohort by predicate instead of by id: `state` is
-  a 2-letter USPS abbreviation (or a FIPS code) and `type` is one of
-  `"state"`, `"county"`, `"city"`, `"township"` (or the integer `0:3`) --
-  the same vocabulary, and the same internal coercion, as
-  [cog_gov_search()]. Both default to `NULL`.
-
-  The cohort is then expressed as a subquery against `canonical_fips_xwalk`
-  inside each statement rather than round-tripped through R as a literal id
-  list. For a fleet-scale cohort that is the difference between a
-  301,591-character `IN` list re-parsed in 5--8 statements per call and a
-  constant-size predicate: measured at **94 ms versus 449 ms** for the same
-  FY2022 aggregate over the 20,106-government `type = "city"` cohort, within
-  7% of the no-filter floor.
-
-  Supplying `govid` **and** `state`/`type` INTERSECTS them -- the
-  governments in `govid` that also match the predicate -- rather than one
-  silently taking precedence. Naming no cohort at all (`govid`, `state` and
-  `type` all `NULL`) aborts with class `uscogdata_no_cohort`.
-
-  When the cohort is named by predicate, `provenance$scope$govids_found`
-  and `govids_missing` are empty -- there is no id list to report against --
-  and `provenance$scope$cohort` carries `state`, `type` and
-  `n_governments` instead. A `govid`-named cohort reports exactly as before.}
-
-\item{fiscal_period}{If `TRUE`, add three columns to every row:
-`fiscal_year_end` (the government's fiscal year end that survey year, an
-MMDD integer such as `630`), and `period_start` and `period_end` (Dates,
-inclusive), the calendar months the row covers. `year` is the Census
+\code{recipe} and with \code{complete = TRUE} -- see \code{offset} and \code{total_rows}.}
+
+\item{offset}{Rows to skip before \code{limit} starts counting (0-based).
+Ignored if \code{limit} is \code{NULL}; defaults to \code{0L} when \code{limit} is set.}
+
+\item{state, type}{Name the cohort by predicate instead of by id: \code{state} is
+a 2-letter USPS abbreviation (or a FIPS code) and \code{type} is one of
+\code{"state"}, \code{"county"}, \code{"city"}, \code{"township"} (or the integer \code{0:3}) --
+the same vocabulary, and the same internal coercion, as
+\code{\link[=cog_gov_search]{cog_gov_search()}}. Both default to \code{NULL}.
+
+The cohort is then expressed as a subquery against \code{canonical_fips_xwalk}
+inside each statement rather than round-tripped through R as a literal id
+list. For a fleet-scale cohort that is the difference between a
+301,591-character \code{IN} list re-parsed in 5--8 statements per call and a
+constant-size predicate: measured at \strong{94 ms versus 449 ms} for the same
+FY2022 aggregate over the 20,106-government \code{type = "city"} cohort, within
+7\% of the no-filter floor.
+
+Supplying \code{govid} \strong{and} \code{state}/\code{type} INTERSECTS them -- the
+governments in \code{govid} that also match the predicate -- rather than one
+silently taking precedence. Naming no cohort at all (\code{govid}, \code{state} and
+\code{type} all \code{NULL}) aborts with class \code{uscogdata_no_cohort}.
+
+When the cohort is named by predicate, \code{provenance$scope$govids_found}
+and \code{govids_missing} are empty -- there is no id list to report against --
+and \code{provenance$scope$cohort} carries \code{state}, \code{type} and
+\code{n_governments} instead. A \code{govid}-named cohort reports exactly as before.}
+
+\item{fiscal_period}{If \code{TRUE}, add three columns to every row:
+\code{fiscal_year_end} (the government's fiscal year end that survey year, an
+MMDD integer such as \code{630}), and \code{period_start} and \code{period_end} (Dates,
+inclusive), the calendar months the row covers. \code{year} is the Census
 survey year, which covers different months for different governments;
-see "The survey-year rule" in [cog_fiscal_periods()]. The columns come
-from each government-year's own `fiscal_year_end`, so a change of fiscal
-year is respected, and are `NA` where the corpus has no value. The result
-also gains `provenance$transformations$fiscal_period`. Defaults to
-`FALSE`, which returns exactly the columns and provenance this verb
+see "The survey-year rule" in \code{\link[=cog_fiscal_periods]{cog_fiscal_periods()}}. The columns come
+from each government-year's own \code{fiscal_year_end}, so a change of fiscal
+year is respected, and are \code{NA} where the corpus has no value. The result
+also gains \code{provenance$transformations$fiscal_period}. Defaults to
+\code{FALSE}, which returns exactly the columns and provenance this verb
 returned before the argument existed.}
 }
 \value{
-Tibble with columns `year`, `canonical_govid`, `gov_name`,
-  `revenue_subtype`, `category`, `amt_nominal`, optional `amt_real`,
-  optional `amt_per_capita_nominal`, optional `amt_per_capita_real`,
-  optional `pop_source`, `codes_included`, `aggregate_fallback`, `notes`,
-  `value_source` when `complete = TRUE`, and `fiscal_year_end`,
-  `period_start` and `period_end` when `fiscal_period = TRUE`.
+Tibble with columns \code{year}, \code{canonical_govid}, \code{gov_name},
+\code{revenue_subtype}, \code{category}, \code{amt_nominal}, optional \code{amt_real},
+optional \code{amt_per_capita_nominal}, optional \code{amt_per_capita_real},
+optional \code{pop_source}, \code{codes_included}, \code{aggregate_fallback}, \code{notes},
+\code{value_source} when \code{complete = TRUE}, and \code{fiscal_year_end},
+\code{period_start} and \code{period_end} when \code{fiscal_period = TRUE}.
 }
 \description{
-Mirror of [cog_spending()] for revenue categories. One row per
-`(year, canonical_govid, revenue_subtype, category)`. Amounts are returned
-in **full U.S. dollars** (raw Census values are in $1,000s; this verb
-multiplies by 1000 and records the conversion in `provenance`).
+Mirror of \code{\link[=cog_spending]{cog_spending()}} for revenue categories. One row per
+\verb{(year, canonical_govid, revenue_subtype, category)}. Amounts are returned
+in \strong{full U.S. dollars} (raw Census values are in $1,000s; this verb
+multiplies by 1000 and records the conversion in \code{provenance}).
 }
diff --git a/man/cog_spending.Rd b/man/cog_spending.Rd
index 7f8fc3a..3228992 100644
--- a/man/cog_spending.Rd
+++ b/man/cog_spending.Rd
@@ -22,191 +22,192 @@ cog_spending(
 )
 }
 \arguments{
-\item{govid}{Character vector of `canonical_govid` values, or `NULL` to name
-the cohort by `state`/`type` instead. One of `govid`, `state`, or `type`
+\item{govid}{Character vector of \code{canonical_govid} values, or \code{NULL} to name
+the cohort by \code{state}/\code{type} instead. One of \code{govid}, \code{state}, or \code{type}
 is required.}
 
 \item{years}{Integer vector of years.}
 
 \item{category}{Character vector of category names (from
-`summary_categories.category`), or `NULL` for all categories broken out
-one row each. The reserved value `"All Categories"` instead returns a
-single summed row per `(year, canonical_govid, subtype)`, covering every
+\code{summary_categories.category}), or \code{NULL} for all categories broken out
+one row each. The reserved value \code{"All Categories"} instead returns a
+single summed row per \verb{(year, canonical_govid, subtype)}, covering every
 category inside the requested concept's subtype scope. It cannot be
 combined with other category names, and it is not the same thing as
-`expenditure_concept = "total"`: the concept chooses which subtypes are in
-scope, `"All Categories"` chooses whether rows inside that scope are
+\code{expenditure_concept = "total"}: the concept chooses which subtypes are in
+scope, \code{"All Categories"} chooses whether rows inside that scope are
 broken out or summed. Because the result keeps one row per
-`spend_subtype`, filtering the returned frame to
-`spend_subtype == "operations"` gives an operating-expenditure total.}
+\code{spend_subtype}, filtering the returned frame to
+\code{spend_subtype == "operations"} gives an operating-expenditure total.}
 
-\item{per_capita}{If `TRUE`, adds `amt_per_capita_nominal` (and
-`amt_per_capita_real` when `adjust_to_year` is set) using the per-year
-Census F-33 population from `gov_population_yearly`. Result also gains
-a `pop_source` column with values `"census_f33"` or `"unavailable"`
+\item{per_capita}{If \code{TRUE}, adds \code{amt_per_capita_nominal} (and
+\code{amt_per_capita_real} when \code{adjust_to_year} is set) using the per-year
+Census F-33 population from \code{gov_population_yearly}. Result also gains
+a \code{pop_source} column with values \code{"census_f33"} or \code{"unavailable"}
 (the latter for gov types 4/5 and any row whose population is missing
 in that year).}
 
 \item{adjust_to_year}{Integer base year for CPI-U real-dollar conversion,
-or `NULL` for nominal only.}
+or \code{NULL} for nominal only.}
 
-\item{basis}{`"harmonized"` (default) sums item codes through the
+\item{basis}{\code{"harmonized"} (default) sums item codes through the
 cross-vintage harmonization mapping (folding series-break-affected
 codes onto a comparable target and excluding aggregate / discontinued
-rows -- see the `harmonization` block in `cog_explain()`); `"raw"`
+rows -- see the \code{harmonization} block in \code{cog_explain()}); \code{"raw"}
 reproduces the pre-Phase-R2 behavior (published item codes, no
-folding). On a corpus with `schema_version < 5` (no harmonization
-tables), `basis` silently resolves to `"raw"` when left at its default
+folding). On a corpus with \code{schema_version < 5} (no harmonization
+tables), \code{basis} silently resolves to \code{"raw"} when left at its default
 and the resolution is recorded in the provenance; explicitly passing
-`basis = "harmonized"` on such a corpus aborts. Ignored when `recipe`
+\code{basis = "harmonized"} on such a corpus aborts. Ignored when \code{recipe}
 is set (see below).}
 
-\item{recipe}{Optional harmonization recipe id (see [cog_recipes()]) for
+\item{recipe}{Optional harmonization recipe id (see \code{\link[=cog_recipes]{cog_recipes()}}) for
 multi-code cross-vintage series that a 1:1 harmonized_code mapping
 can't express (e.g. a wide-era aggregate that only splits into leaf
-codes in the modern era). Mutually exclusive with `category`. The
-result's subtype column reads `"recipe"` and `category` reads the
-recipe's label. Requires `schema_version >= 5`. A recipe query bypasses
-`basis` entirely (it joins `long` directly rather than going through
-the `*_annotated`/`*_annotated_harmonized` views), so the `basis`
+codes in the modern era). Mutually exclusive with \code{category}. The
+result's subtype column reads \code{"recipe"} and \code{category} reads the
+recipe's label. Requires \code{schema_version >= 5}. A recipe query bypasses
+\code{basis} entirely (it joins \code{long} directly rather than going through
+the \verb{*_annotated}/\verb{*_annotated_harmonized} views), so the \code{basis}
 argument is ignored and the result's provenance reports
-`basis = "recipe"` with an inert `harmonization` block (`applied =
-FALSE`, pointing at the `recipe` block instead) rather than a
-possibly-misleading `"harmonized"`/`"raw"` value.}
+\code{basis = "recipe"} with an inert \code{harmonization} block (\code{applied = FALSE}, pointing at the \code{recipe} block instead) rather than a
+possibly-misleading \code{"harmonized"}/\code{"raw"} value.}
 
 \item{expenditure_concept}{Which spending concept to return. Concepts are
-  defined as sets of the crosswalk's `spend_subtype` values -- never as
-  item-code first letters, which cannot classify correctly (prefix `Y`
-  alone spans revenue, expenditure, and balance codes):
-
-  * `"primary"` (default) -- the government's own service provision:
-    `operations` + `capital` + `assistance` subtypes.
-  * `"direct"` -- Census's published Direct Expenditure: `primary` plus
-    `interest` (interest on debt) and `insurance_benefits` (insurance
-    trust benefit payments, e.g. pensions -- Census manual section
-    5.2.2.1 includes payments to retirees in Direct).
-  * `"total"` -- `direct` plus the intergovernmental leg: payments to
-    local governments (`M` codes), to the state government (`L` codes,
-    excluding the `L--` family-total rollup), and state payments to
-    school systems (`Q11`/`Q12`/`Q18`), so results gain rows with
-    `spend_subtype == "intergovernmental"`. Requires the active corpus's
-    `summary_categories` to carry M/L rows (added by cog_pipeline PR
-    #59); aborts with class `uscogdata_ig_categories_unsupported` on an
-    older corpus rather than silently under-reporting. Mutually
-    exclusive with `recipe` (a recipe already defines its own component
-    codes).
-
-  **Do not sum `"total"` results across levels of government** (e.g.
-  state + county + city): a state's `M12` payment to a school district is
-  the same dollar the district reports as its own direct `E12`, so
-  summing both double-counts it. This matters in particular with
-  [cog_geographic_rollup()], which sums across exactly that kind of
-  multi-layer government set.
-
-  In the legacy wide era (<= FY2011), some functions are published ONLY
-  as an aggregate-flagged family total (e.g. Corrections' `E04`/`E05`
-  split), which the Direct leg excludes by construction but the IG leg
-  deliberately keeps (see `inst/sql/24-ig_long.sql`). For a `"total"`
-  query, any (year, category) where this leaves intergovernmental rows
-  with NO Direct counterpart is flagged: the affected rows' `notes`
-  name the harmonization recipe that recovers the missing Direct
-  component (when one exists), and
-  `provenance$expenditure_concept_direct_suppressed` is `TRUE` -- the
-  figure in those rows is the intergovernmental leg alone, not Direct +
-  IG. When `category = "All Categories"` is combined with
-  `expenditure_concept = "total"`, this detection cannot run (it keys on
-  per-category rows, which all-categories mode collapses to one literal
-  value), so `expenditure_concept_direct_suppressed` is `NA` rather than a
-  possibly-false `FALSE`; query an explicit `category` to get a real
-  answer.}
-
-\item{complete}{If `TRUE`, fill the requested grid so that a cell the
-  corpus does not carry still appears, labelled with **why** it is
-  missing, and add a `value_source` column to every row:
-
-  * `"reported"` — the corpus carries this cell.
-  * `"census_zero"` — dense-source year (`<= FY2011`), cell absent:
-    Census published `$0`. `amt_nominal` is `0`.
-  * `"not_reported"` — sparse-source year (`>= FY2012`), cell absent: the
-    government did not report, and the value is unknown. `amt_nominal` is
-    `NA`, **not** `0` — writing a zero there would invent data.
-
-  The grid comes from the corpus's `code_set` table, scoped to each
-  government's own type, so a county is never filled with cells only a
-  state can report. Reported rows are passed through untouched.
-
-  Defaults to `FALSE` (the historical behaviour: absent cells simply do
-  not appear). Needs a corpus published from 2026-07-29 onward, which is
-  when `representation`/`code_set` began shipping; aborts with class
-  `uscogdata_representation_unavailable` otherwise. Not available with
-  `recipe` or with `expenditure_concept = "total"` (class
-  `uscogdata_complete_unsupported`) — neither draws its cells from
-  `code_set`.}
+defined as sets of the crosswalk's \code{spend_subtype} values -- never as
+item-code first letters, which cannot classify correctly (prefix \code{Y}
+alone spans revenue, expenditure, and balance codes):
+\itemize{
+\item \code{"primary"} (default) -- the government's own service provision:
+\code{operations} + \code{capital} + \code{assistance} subtypes.
+\item \code{"direct"} -- Census's published Direct Expenditure: \code{primary} plus
+\code{interest} (interest on debt) and \code{insurance_benefits} (insurance
+trust benefit payments, e.g. pensions -- Census manual section
+5.2.2.1 includes payments to retirees in Direct).
+\item \code{"total"} -- \code{direct} plus the intergovernmental leg: payments to
+local governments (\code{M} codes), to the state government (\code{L} codes,
+excluding the \verb{L--} family-total rollup), and state payments to
+school systems (\code{Q11}/\code{Q12}/\code{Q18}), so results gain rows with
+\code{spend_subtype == "intergovernmental"}. Requires the active corpus's
+\code{summary_categories} to carry M/L rows (added by cog_pipeline PR
+#59); aborts with class \code{uscogdata_ig_categories_unsupported} on an
+older corpus rather than silently under-reporting. Mutually
+exclusive with \code{recipe} (a recipe already defines its own component
+codes).
+}
+
+\strong{Do not sum \code{"total"} results across levels of government} (e.g.
+state + county + city): a state's \code{M12} payment to a school district is
+the same dollar the district reports as its own direct \code{E12}, so
+summing both double-counts it. This matters in particular with
+\code{\link[=cog_geographic_rollup]{cog_geographic_rollup()}}, which sums across exactly that kind of
+multi-layer government set.
+
+In the legacy wide era (<= FY2011), some functions are published ONLY
+as an aggregate-flagged family total (e.g. Corrections' \code{E04}/\code{E05}
+split), which the Direct leg excludes by construction but the IG leg
+deliberately keeps (see \code{inst/sql/24-ig_long.sql}). For a \code{"total"}
+query, any (year, category) where this leaves intergovernmental rows
+with NO Direct counterpart is flagged: the affected rows' \code{notes}
+name the harmonization recipe that recovers the missing Direct
+component (when one exists), and
+\code{provenance$expenditure_concept_direct_suppressed} is \code{TRUE} -- the
+figure in those rows is the intergovernmental leg alone, not Direct +
+IG. When \code{category = "All Categories"} is combined with
+\code{expenditure_concept = "total"}, this detection cannot run (it keys on
+per-category rows, which all-categories mode collapses to one literal
+value), so \code{expenditure_concept_direct_suppressed} is \code{NA} rather than a
+possibly-false \code{FALSE}; query an explicit \code{category} to get a real
+answer.}
+
+\item{complete}{If \code{TRUE}, fill the requested grid so that a cell the
+corpus does not carry still appears, labelled with \strong{why} it is
+missing, and add a \code{value_source} column to every row:
+\itemize{
+\item \code{"reported"} — the corpus carries this cell.
+\item \code{"census_zero"} — dense-source year (\verb{<= FY2011}), cell absent:
+Census published \verb{$0}. \code{amt_nominal} is \code{0}.
+\item \code{"not_reported"} — sparse-source year (\verb{>= FY2012}), cell absent: the
+government did not report, and the value is unknown. \code{amt_nominal} is
+\code{NA}, \strong{not} \code{0} — writing a zero there would invent data.
+}
+
+The grid comes from the corpus's \code{code_set} table, scoped to each
+government's own type, so a county is never filled with cells only a
+state can report. Reported rows are passed through untouched.
+
+Defaults to \code{FALSE} (the historical behaviour: absent cells simply do
+not appear). Needs a corpus published from 2026-07-29 onward, which is
+when \code{representation}/\code{code_set} began shipping; aborts with class
+\code{uscogdata_representation_unavailable} otherwise. Not available with
+\code{recipe} or with \code{expenditure_concept = "total"} (class
+\code{uscogdata_complete_unsupported}) — neither draws its cells from
+\code{code_set}.}
 
 \item{limit}{Maximum number of result rows to return, pushed into the SQL
-query itself (`LIMIT`/`OFFSET`) rather than applied after the full
-result is materialized. `NULL` (the default) returns every matching row,
+query itself (\code{LIMIT}/\code{OFFSET}) rather than applied after the full
+result is materialized. \code{NULL} (the default) returns every matching row,
 exactly as before this parameter existed. Mutually exclusive with
-`recipe` and with `complete = TRUE` -- see `offset` and `total_rows`.}
-
-\item{offset}{Rows to skip before `limit` starts counting (0-based).
-Ignored if `limit` is `NULL`; defaults to `0L` when `limit` is set.}
-
-\item{state, type}{Name the cohort by predicate instead of by id: `state` is
-  a 2-letter USPS abbreviation (or a FIPS code) and `type` is one of
-  `"state"`, `"county"`, `"city"`, `"township"` (or the integer `0:3`) --
-  the same vocabulary, and the same internal coercion, as
-  [cog_gov_search()]. Both default to `NULL`.
-
-  The cohort is then expressed as a subquery against `canonical_fips_xwalk`
-  inside each statement rather than round-tripped through R as a literal id
-  list. For a fleet-scale cohort that is the difference between a
-  301,591-character `IN` list re-parsed in 5--8 statements per call and a
-  constant-size predicate: measured at **94 ms versus 449 ms** for the same
-  FY2022 aggregate over the 20,106-government `type = "city"` cohort, within
-  7% of the no-filter floor.
-
-  Supplying `govid` **and** `state`/`type` INTERSECTS them -- the
-  governments in `govid` that also match the predicate -- rather than one
-  silently taking precedence. Naming no cohort at all (`govid`, `state` and
-  `type` all `NULL`) aborts with class `uscogdata_no_cohort`.
-
-  When the cohort is named by predicate, `provenance$scope$govids_found`
-  and `govids_missing` are empty -- there is no id list to report against --
-  and `provenance$scope$cohort` carries `state`, `type` and
-  `n_governments` instead. A `govid`-named cohort reports exactly as before.}
-
-\item{fiscal_period}{If `TRUE`, add three columns to every row:
-`fiscal_year_end` (the government's fiscal year end that survey year, an
-MMDD integer such as `630`), and `period_start` and `period_end` (Dates,
-inclusive), the calendar months the row covers. `year` is the Census
+\code{recipe} and with \code{complete = TRUE} -- see \code{offset} and \code{total_rows}.}
+
+\item{offset}{Rows to skip before \code{limit} starts counting (0-based).
+Ignored if \code{limit} is \code{NULL}; defaults to \code{0L} when \code{limit} is set.}
+
+\item{state, type}{Name the cohort by predicate instead of by id: \code{state} is
+a 2-letter USPS abbreviation (or a FIPS code) and \code{type} is one of
+\code{"state"}, \code{"county"}, \code{"city"}, \code{"township"} (or the integer \code{0:3}) --
+the same vocabulary, and the same internal coercion, as
+\code{\link[=cog_gov_search]{cog_gov_search()}}. Both default to \code{NULL}.
+
+The cohort is then expressed as a subquery against \code{canonical_fips_xwalk}
+inside each statement rather than round-tripped through R as a literal id
+list. For a fleet-scale cohort that is the difference between a
+301,591-character \code{IN} list re-parsed in 5--8 statements per call and a
+constant-size predicate: measured at \strong{94 ms versus 449 ms} for the same
+FY2022 aggregate over the 20,106-government \code{type = "city"} cohort, within
+7\% of the no-filter floor.
+
+Supplying \code{govid} \strong{and} \code{state}/\code{type} INTERSECTS them -- the
+governments in \code{govid} that also match the predicate -- rather than one
+silently taking precedence. Naming no cohort at all (\code{govid}, \code{state} and
+\code{type} all \code{NULL}) aborts with class \code{uscogdata_no_cohort}.
+
+When the cohort is named by predicate, \code{provenance$scope$govids_found}
+and \code{govids_missing} are empty -- there is no id list to report against --
+and \code{provenance$scope$cohort} carries \code{state}, \code{type} and
+\code{n_governments} instead. A \code{govid}-named cohort reports exactly as before.}
+
+\item{fiscal_period}{If \code{TRUE}, add three columns to every row:
+\code{fiscal_year_end} (the government's fiscal year end that survey year, an
+MMDD integer such as \code{630}), and \code{period_start} and \code{period_end} (Dates,
+inclusive), the calendar months the row covers. \code{year} is the Census
 survey year, which covers different months for different governments;
-see "The survey-year rule" in [cog_fiscal_periods()]. The columns come
-from each government-year's own `fiscal_year_end`, so a change of fiscal
-year is respected, and are `NA` where the corpus has no value. The result
-also gains `provenance$transformations$fiscal_period`. Defaults to
-`FALSE`, which returns exactly the columns and provenance this verb
+see "The survey-year rule" in \code{\link[=cog_fiscal_periods]{cog_fiscal_periods()}}. The columns come
+from each government-year's own \code{fiscal_year_end}, so a change of fiscal
+year is respected, and are \code{NA} where the corpus has no value. The result
+also gains \code{provenance$transformations$fiscal_period}. Defaults to
+\code{FALSE}, which returns exactly the columns and provenance this verb
 returned before the argument existed.}
 }
 \value{
-Tibble with columns `year`, `canonical_govid`, `gov_name`,
-  `spend_subtype`, `category`, `amt_nominal`, optional `amt_real`,
-  optional `amt_per_capita_nominal`, optional `amt_per_capita_real`,
-  optional `pop_source`, `codes_included`, `aggregate_fallback`, `notes`,
-  `value_source` when `complete = TRUE`, and `fiscal_year_end`,
-  `period_start` and `period_end` when `fiscal_period = TRUE`.
-  Carries a `provenance` attribute matching `inst/schemas/provenance-v1.json`,
-  whose `completion` block reports `applied`, `rows_filled`, and the
-  per-year `absence_means` rule that was applied. When `limit` is set,
-  also carries a `total_rows` attribute: the full unpaginated row count,
-  computed by the same query (`COUNT(*) OVER()`) rather than a second
-  round trip -- so a caller walking pages never has to ask "how many are
-  there" separately.
+Tibble with columns \code{year}, \code{canonical_govid}, \code{gov_name},
+\code{spend_subtype}, \code{category}, \code{amt_nominal}, optional \code{amt_real},
+optional \code{amt_per_capita_nominal}, optional \code{amt_per_capita_real},
+optional \code{pop_source}, \code{codes_included}, \code{aggregate_fallback}, \code{notes},
+\code{value_source} when \code{complete = TRUE}, and \code{fiscal_year_end},
+\code{period_start} and \code{period_end} when \code{fiscal_period = TRUE}.
+Carries a \code{provenance} attribute matching \code{inst/schemas/provenance-v1.json},
+whose \code{completion} block reports \code{applied}, \code{rows_filled}, and the
+per-year \code{absence_means} rule that was applied. When \code{limit} is set,
+also carries a \code{total_rows} attribute: the full unpaginated row count,
+computed by the same query (\verb{COUNT(*) OVER()}) rather than a second
+round trip -- so a caller walking pages never has to ask "how many are
+there" separately.
 }
 \description{
-One row per `(year, canonical_govid, spend_subtype, category)`. Amounts are
-returned in **full U.S. dollars** (the raw corpus stores them in $1,000s;
+One row per \verb{(year, canonical_govid, spend_subtype, category)}. Amounts are
+returned in \strong{full U.S. dollars} (the raw corpus stores them in $1,000s;
 this verb multiplies by 1000 so downstream code can freely rescale to
 millions/billions). The conversion is recorded in the provenance attribute
-under `transformations$units_conversion`.
+under \code{transformations$units_conversion}.
 }
diff --git a/man/cog_survey_year.Rd b/man/cog_survey_year.Rd
index ef9d19a..e1aaefa 100644
--- a/man/cog_survey_year.Rd
+++ b/man/cog_survey_year.Rd
@@ -7,29 +7,29 @@
 cog_survey_year(govid, date)
 }
 \arguments{
-\item{govid}{Character vector of `canonical_govid` values, or a data frame
-with a `canonical_govid` column.}
+\item{govid}{Character vector of \code{canonical_govid} values, or a data frame
+with a \code{canonical_govid} column.}
 
-\item{date}{Dates, or character strings in `"YYYY-MM-DD"` form. `govid`
-and `date` must have the same length, or one of them must have length 1.}
+\item{date}{Dates, or character strings in \code{"YYYY-MM-DD"} form. \code{govid}
+and \code{date} must have the same length, or one of them must have length 1.}
 }
 \value{
-Tibble with one row per (`govid`, `date`) pair, in input order:
-  `canonical_govid`, `date`, `year` (the survey year, `NA` when it cannot
-  be determined), `fiscal_year_end`, `period_start` and `period_end`.
+Tibble with one row per (\code{govid}, \code{date}) pair, in input order:
+\code{canonical_govid}, \code{date}, \code{year} (the survey year, \code{NA} when it cannot
+be determined), \code{fiscal_year_end}, \code{period_start} and \code{period_end}.
 }
 \description{
 Maps a date to the survey year whose fiscal period, for that government,
-contains it. This is the reverse of [cog_fiscal_periods()]: use it to place
-an event that happened on a known date against the corpus's `year`.
+contains it. This is the reverse of \code{\link[=cog_fiscal_periods]{cog_fiscal_periods()}}: use it to place
+an event that happened on a known date against the corpus's \code{year}.
 }
 \details{
 The date can fall in survey year Y, Y+1 or Y+2 of its own calendar year Y,
 depending on the government's fiscal year end. Each candidate is looked up
-with the `fiscal_year_end` the corpus records for that government-year, so
+with the \code{fiscal_year_end} the corpus records for that government-year, so
 a change of fiscal year is respected. When the government has no rows, or
-no `fiscal_year_end`, in the survey year that would cover the date, the
-result is `NA`; the function does not borrow another year's value. If a
+no \code{fiscal_year_end}, in the survey year that would cover the date, the
+result is \code{NA}; the function does not borrow another year's value. If a
 change of fiscal year makes two survey years' periods overlap, the earlier
 survey year is returned.
 }
@@ -38,17 +38,18 @@ survey year is returned.
 Census assigns each government's fiscal year to a survey year by when the
 fiscal year ended. Survey year Y holds the fiscal year that ended between
 July 1 of Y-1 and June 30 of Y. The corpus records each government-year's
-fiscal year end as `fiscal_year_end`, an MMDD integer such as `630` or
-`1231`.
+fiscal year end as \code{fiscal_year_end}, an MMDD integer such as \code{630} or
+\code{1231}.
 
 Survey year 2017 therefore covers, for a county, city or township:
-
-  * `fiscal_year_end = 630` (June 30): the fiscal year ending June 30,
-    2017, so July 2016 to June 2017.
-  * `930` (September 30): the fiscal year ending September 30, 2016, so
-    October 2015 to September 2016.
-  * `1231` (December 31): the fiscal year ending December 31, 2016, so
-    January to December 2016.
+\itemize{
+\item \code{fiscal_year_end = 630} (June 30): the fiscal year ending June 30,
+2017, so July 2016 to June 2017.
+\item \code{930} (September 30): the fiscal year ending September 30, 2016, so
+October 2015 to September 2016.
+\item \code{1231} (December 31): the fiscal year ending December 31, 2016, so
+January to December 2016.
+}
 
 A city with a December year end and a city with a June year end are both
 "2017" in the corpus, but their figures are six months apart. A September
@@ -66,11 +67,11 @@ way. That treatment has not been checked against the data. For New York
 the same months.
 
 A government can change its fiscal year end, so the periods are looked up
-per government-year. Where the corpus has no `fiscal_year_end` for a
+per government-year. Where the corpus has no \code{fiscal_year_end} for a
 government-year, or the government has no rows that year, the period is
-`NA`. Most of FY1993 is `NA`: 99% of its government-years have no usable
-value in the public corpus as of November 2026. So do 5% to 11% in FY1978
-and FY1987 to FY1990, and under 3% in every other year.
+\code{NA}. Most of FY1993 is \code{NA}: 99\% of its government-years have no usable
+value in the public corpus as of November 2026. So do 5\% to 11\% in FY1978
+and FY1987 to FY1990, and under 3\% in every other year.
 
 From FY2023 the source files mark some rows as estimates carried forward
 from an earlier year. The periods here describe the survey year, not the
@@ -84,5 +85,5 @@ cog_survey_year(c("121011212191", "010000226085"), as.Date("2020-04-15"))
 }
 }
 \seealso{
-[cog_fiscal_periods()]
+\code{\link[=cog_fiscal_periods]{cog_fiscal_periods()}}
 }