gganimate, methods, data.table, bit64, progress, tidyselect (>=

1.0.0), purrr, rlang, dtplyr (>= 1.0.0), R2jags, coda

stringr, sf, parallel, methods, data.table, bit64, tidyselect
Suggests: knitr, rmarkdown, gganimate, R2jags, coda

Description: 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.

Title: Estimation of Forest Variables using the FIA Database

Title: Feasible Solution Algorithm for Finding Best Subsets and
Description: 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

Description: Assists in statistical model building to find optimal and semi-optimal higher order interactions
and best subsets. Uses the lm(), glm(), and other R functions to fit models generated from a feasible
solution algorithm. Discussed in Subset Selection in Regression, A Miller (2002). Applied and explained
for least median of squares in Hawkins (1993) <doi:10.1016/0167-9473(93)90246-P>. 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.
Imports: parallel, hashmap, methods, tibble