From dedc4cd8a861954553a4f9bd181b5e8564bfd7f4 Mon Sep 17 00:00:00 2001 From: vlucet Date: Thu, 25 May 2023 09:58:25 -0400 Subject: [PATCH] fixes #135 --- paper/paper.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/paper/paper.md b/paper/paper.md index 2ffdf87b..a25cff5a 100644 --- a/paper/paper.md +++ b/paper/paper.md @@ -67,7 +67,7 @@ The key workflow steps are: 2. The region within the boundary is discretized into patches with the `spm_discretize()` function, creating a `sspm_discrete_boundary` object. * Spatiotemporal smoothing of biomass and environmental predictors using GAMs. - 3. The `spm_as_dataset()` function turns user-provided data frames of raw observations into `sspm_dataset` objects that explicitly track locations, data types, and aggregation scales for each input. `sspm` recognizes three types of data: **trawl** (i.e. biomass estimates from scientific surveys), **predictors**, and **catch** (i.e., harvest). + 3. The `spm_as_dataset()` function turns user-provided data frames of raw observations into `sspm_dataset` objects that explicitly track locations, data types, and aggregation scales for each input. `sspm` recognizes three types of data: **trawl** (i.e., biomass estimates from scientific surveys), **predictors**, and **catch** (i.e., harvest). 4. The `spm_smooth()` uses GAMs to calculate spatially smoothed yearly estimates of biomass and environmental predictors for each patch from trawl-level data, based on the spatial structure from the `sspm_discrete_boundary` object. The user specifies a GAM formula with custom smooth terms. The output is another `sspm_dataset` object with a `smoothed_data` slot which contains the smoothed predictions for all patches. * Computation of surplus production based on biomass density and fishing effort.