dplyr, stringr, sp, sf, tidyr (>= 1.0.0), parallel, ggplot2,
gganimate, methods, data.table, bit64, progress, tidyselect (>=
1.0.0), purrr

dplyr, stringr, sp, sf, tidyr (>= 1.0.0), parallel, ggplot2,
gganimate, methods, data.table, bit64, progress, tidyselect (>=
1.0.0), purrr, rlang, dtplyr (>= 1.0.0), R2jags, coda

dplyr (>= 1.0.0), dtplyr (>= 1.0.0), tidyr (>= 1.0.0),
stringr, sf, parallel, methods, data.table, bit64, tidyselect
(>= 1.0.0), rlang, ggplot2, lifecycle
knitr, rmarkdown, gganimate, R2jags, coda

The goal of 'rFIA' is to increase the accessibility and use of the United States Forest Services (USFS) Forest Inventory and Analysis (FIA) Database by providing a user-friendly, open source toolkit to easily query and analyze FIA Data. Designed to accommodate a wide range of potential user objectives, 'rFIA' simplifies the estimation of forest variables from the FIA Database and allows all R users (experts and newcomers alike) to unlock the flexibility inherent to the Enhanced FIA design. Specifically, 'rFIA' improves accessibility to the spatial-temporal estimation capacity of the FIA Database by producing space-time indexed summaries of forest variables within user-defined population boundaries. Direct integration with other popular R packages (e.g., 'dplyr', 'tidyr', and 'sf') facilitates efficient space-time query and data summary, and supports common data representations and API design. The package implements design-based estimation procedures outlined by Bechtold & Patterson (2005) <doi:10.2737/SRS-GTR-80>, and has been validated against estimates and sampling errors produced by FIA 'EVALIDator'. Current development is focused on the implementation of spatially-enabled model-assisted estimators to improve population, change, and ratio estimates.

Estimation of Forest Variables using the FIA Database
dplyr (>= 1.0.0), dtplyr (>= 1.0.0), tidyr (>= 1.0.0),
stringr, sf, parallel, methods, data.table, bit64, tidyselect
(>= 1.0.0), rlang, ggplot2

Feasible Solution Algorithm for Finding Best Subsets and
Interactions
Uses the lm() and glm() functions to fit models
generated from a feasible solution algorithm. The feasible solution algorithm comes up with model forms of a
specific type that can have fixed variables, higher order interactions and their
lower order terms.