feat: cog_find_peers + cog_peer_compare

cog_find_peers selects peers from canonical_fips_xwalk by same-type,
same-state, and population-range (ratio or absolute) criteria,
ordered by |log(pop_ratio)| ascending.

cog_peer_compare accepts the find_peers result (or a plain character
vector of govids), pulls spending for target + peers via cog_spending,
and appends summary rows (summary_p25/p50/p75) so the whole result
can be faceted by `role` in a single ggplot call. Summary rows honor
per_capita + adjust_to_year by picking the right value column.
target_rank reports the target's rank among target+peers at max(years).

Provenance is rewritten with verb = cog_peer_compare and peer_count.

Also: globalVariables('.data') in zzz.R to silence R CMD check on
tidy-eval pronouns.

Tests: 21 new / 120 total pass. devtools::check() 0E/0W/2N.

man/cog_peer_compare.Rd

@@ -0,0 +1,44 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/peers.R
+\name{cog_peer_compare}
+\alias{cog_peer_compare}
+\title{Compare a target government against a peer set}
+\usage{
+cog_peer_compare(
+  target_govid,
+  peers,
+  category,
+  years,
+  per_capita = TRUE,
+  adjust_to_year = NULL
+)
+}
+\arguments{
+\item{target_govid}{Character scalar.}
+
+\item{peers}{A tibble from [cog_find_peers()] or a character vector of
+`canonical_govid`s.}
+
+\item{category}{Character scalar or vector.}
+
+\item{years}{Integer vector.}
+
+\item{per_capita}{Default `TRUE` — peer compare usually normalizes by
+population.}
+
+\item{adjust_to_year}{Integer base year for CPI-U conversion or `NULL`.}
+}
+\value{
+Tibble matching [cog_spending()]'s columns, plus a `role`
+  column taking values `"target"`, `"peer"`, `"summary_p25"`,
+  `"summary_p50"`, or `"summary_p75"`, and `target_rank` (target's rank
+  among target+peers at `max(years)`, NA for other rows). Provenance
+  attribute reports `verb = "cog_peer_compare"` and `peer_count`.
+}
+\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.
+}