add fqacalc reference

paper/fqar_refs.bib

@@ -1,13 +1,30 @@
 %% This BibTeX bibliography file was created using BibDesk.
 %% https://bibdesk.sourceforge.io/
 
-%% Created for Andrew Gard at 2023-08-12 13:59:04 -0500 
+%% Created for Andrew Gard at 2024-04-14 10:37:20 -0500 
 
 
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+@manual{fqadata,
+	author = {Iris Foxfoot},
+	date-added = {2024-04-14 10:36:45 -0500},
+	date-modified = {2024-04-14 10:37:16 -0500},
+	title = {fqadata: Contains Regional Floristic Quality Assessment Databases},
+	url = {https://CRAN.R-project.org/package=fqadata},
+	year = {2023}}
+
+@manual{fqacalc,
+	author = {Iris Foxfoot},
+	date-added = {2024-04-14 10:34:05 -0500},
+	date-modified = {2024-04-14 10:35:36 -0500},
+	title = {fqacalc: Calculate Floristic Quality Assessment Metrics},
+	url = {https://CRAN.R-project.org/package=fqacalc},
+	year = {2023},
+	bdsk-url-1 = {https://CRAN.R-project.org/package=fqacalc}}
+
 @article{JSSv059i10,
 	abstract = {A huge amount of effort is spent cleaning data to get it ready for analysis, but there has been little research on how to make data cleaning as easy and effective as possible. This paper tackles a small, but important, component of data cleaning: data tidying. Tidy datasets are easy to manipulate, model and visualize, and have a specific structure: each variable is a column, each observation is a row, and each type of observational unit is a table. This framework makes it easy to tidy messy datasets because only a small set of tools are needed to deal with a wide range of un-tidy datasets. This structure also makes it easier to develop tidy tools for data analysis, tools that both input and output tidy datasets. The advantages of a consistent data structure and matching tools are demonstrated with a case study free from mundane data manipulation chores.},
 	author = {Wickham, Hadley},

paper/paper.md

@@ -36,7 +36,7 @@ In recent years, it has become increasingly standard for practitioners to upload
 
 The [universalfqa.org](https://universalfqa.org/) website is calibrated for practitioners in the field rather than data analysts at their desks. It facilitates the recording, storing, and publicizing of individual floristic quality assessments and performs calculations of the statistical measures most often cited by land managers and conservation organizations in their reporting, including native mean-C. However, its focus on individual assessments is not well-suited to analyses that might wish to consider multiple assessments simultaneously.
 
-This package compliments existing R packages for floristic quality analysis, including `fqacalc` and `fqadata`, which support the work of field practitioners wishing to make use of R. The `fqar` package enables analysis with a wider lens, allowing users to consider database-wide records of plant taxa or characteristics. By examining entire collections of assessments simultaneously, ecologists may gain insights into floristic quality assessment as well as the various plant species it tracks. Among the wide range of questions made answerable by `fqar` are the following:
+This package compliments existing R packages for floristic quality analysis, including `fqacalc` and `fqadata`, which support the work of field practitioners wishing to make use of R [@fqacalc; @fqadata]. The `fqar` package enables analysis with a wider lens, allowing users to consider database-wide records of plant taxa or characteristics. By examining entire collections of assessments simultaneously, ecologists may gain insights into floristic quality assessment as well as the various plant species it tracks. Among the wide range of questions made answerable by `fqar` are the following:
 
 - what is the co-occurrence profile of a given species of interest? What other plants (or types of plants) is it most frequently identified alongside?