Imports: car, emmeans, Hmisc, lme4, lmerTest, magrittr, purrr, stats,

Description: Easily explore data by generating different kinds of graphs with few lines of code. Use these ggplot() wrappers to quickly draw graphs of scatter/dots with box-whiskers, violins or SD error bars, data distributions, before-after graphs, factorial ANOVA and more. Customise graphs in many ways. Choose from colourblind-friendly 12 discreet , 3 continuous (3 palettes) and 3 divergent colour palettes. Simple code for ANOVA as ordinary (lm()) or mixed-effects linear models (lmer()), including randomised-block or repeated-measures designs. Obtain estimated marginal means and perform post-hoc comparisons on fitted models (via emmeans() wrappers). Also includes small datasets for practicing code and teaching basics before users move on to more complex designs. See vignettes for details on usage <https://grafify-vignettes.netlify.app/>. Citation: <doi:10.5281/zenodo.5136508>.

Description: Easily explore data by plotting graphs with a few lines of code. Use these ggplot() wrappers to quickly draw graphs of scatter/dots with box-whiskers, violins or SD error bars, data distributions, before-after graphs, factorial ANOVA and more. Customise graphs in many ways, for example, by choosing from colour blind-friendly palettes (12 discreet, 3 continuous and 2 divergent palettes). Use the simple code for ANOVA as ordinary (lm()) or mixed-effects linear models (lmer()), including randomised-block or repeated-measures designs, and fit non-linear outcomes as a generalised additive model (gam) using mgcv(). Obtain estimated marginal means and perform post-hoc comparisons on fitted models (via emmeans()). Also includes small datasets for practicing code and teaching basics before users move on to more complex designs. See vignettes for details on usage <https://grafify-vignettes.netlify.app/>. Citation: <doi:10.5281/zenodo.5136508>.
Imports: car, emmeans, Hmisc, lme4, lmerTest, magrittr, mgcv,

Title: Moving-Window and Direct Data Aggregation
comment = "Input on initial conceptual development"),
comment = "Input on package development and structure"))
Description: Data aggregation via moving window or direct methods. Aggregate a
fine-resolution raster to a grid. The moving window method smooths the surface
using a specified function within a moving window of a specified size and shape
prior to aggregation. The direct method simply aggregates to the grid using the
Imports: raster, sf, furrr, checkmate, methods
Suggests: testthat, spelling, knitr, rmarkdown, covr, ggplot2,
Author: Laura Graham [aut, cre] (<https://orcid.org/0000-0002-3611-7281>),
Felix Eigenbrod [ctb] (Input on initial conceptual development),
Marco Sciaini [ctb] (Input on package development and structure)