Link glossary terms in website pages (#39)

Co-authored-by: seabbs <20317649+seabbs@users.noreply.github.com>

Author: github-actions[bot]

methods/data-revisions.qmd

@@ -14,7 +14,7 @@ This page is under development.
 - Flagging and annotating revision events so downstream analyses can account for them
 - Redistributing negative entries in [reporting triangles](../resources/glossary.qmd#reporting-triangle) across neighbouring cells
 - [Nowcasting](../resources/glossary.qmd#nowcasting) methods that model cumulative counts without enforcing a proper CDF (i.e. monotonically increasing) can accommodate negative increments directly
-- Methods that allow an improper PMF, such as `baselinenowcast`, can also handle negative counts in the reporting triangle
+- Methods that allow an improper PMF, such as `baselinenowcast`, can also handle negative counts in the [reporting triangle](../resources/glossary.qmd#reporting-triangle)
 - Many methods can be used without modification when negative counts are present for individual cells provided the net count for each delay is non-negative
 
 ### Implementation

methods/reporting-delays.qmd

@@ -98,7 +98,7 @@ This structure makes it straightforward to incorporate mechanistic components, s
 It also makes it straightforward to add constraints to the delay distribution, such as requiring reporting proportions to sum to one or specifying a parametric form.
 Two main variants exist: conditional and marginal.
 Conditional generative models separate variability in incidence from variability in reporting by modelling total counts directly and then distributing them across delays, producing well-calibrated uncertainty for the quantities decision makers most care about [@hohle; @Stoner2020; @Stoner2023; @Seaman2022].
-Marginal generative models instead treat each cell of the reporting triangle as an independent draw [@McGough2020; @gunther2021nowcasting; @lison2024; @epinowcast].
+Marginal generative models instead treat each cell of the [reporting triangle](../resources/glossary.qmd#reporting-triangle) as an independent draw [@McGough2020; @gunther2021nowcasting; @lison2024; @epinowcast].
 Both variants can support parametric delay distributions, time-varying delays through covariates such as day-of-week effects, hierarchical pooling across regions or age groups, joint estimation of the reproduction number, and incorporation of leading indicators [@Stoner2023; @Seaman2022; @Bergstrom2022; @epinowcast].
 
 ::: {.callout-tip}
@@ -158,7 +158,7 @@ For multiplicative methods, [baselinenowcast](https://github.com/epinowcast/base
 
 For regression approaches, [nowcaster](https://github.com/covid19br/nowcaster) [@bastos] uses smooth functions of event time and delay, with user-specified count distributions, support for day-of-week effects, and stratification by age or geography.
 A benefit of the regression framework is that it builds on widely used statistical software.
-@vandeKassteele2019 provide accompanying scripts for their constrained P-spline approach, and the UK Health Security Agency (UKHSA) nowcasting pipelines are available as public code repositories [@Overton2023-dk; @Mellor2025; @Tang2025].
+@vandeKassteele2019 provide accompanying scripts for their constrained P-spline approach, and the UK Health Security Agency (UKHSA) [nowcasting](../resources/glossary.qmd#nowcasting) pipelines are available as public code repositories [@Overton2023-dk; @Mellor2025; @Tang2025].
 
 For generative models, [NobBS](https://cran.r-project.org/package=NobBS) [@McGough2020] implements a simple marginal generative model with a random walk expectation model.
 [EpiLPS](https://github.com/oswaldogressani/EpiLPS) [@Gressani2024] uses a marginal generative approach with smooth functions of event time and delay and user-specified count distributions.