Maps of the 'area potentially available' (APA) of trees is calculated from
mapped forest stands using the approach from Gspaltl et al. (2012) <doi:10.1093/forestry/cps052>.
This is done by computing a rasterized version of
'weighted voronoi diagrams' using a an approximation of the trees competitive
ability (e.g., crown radius, leaf area) as weight. The main output are 'Raster*'-
objects from the 'raster' package that are stored together with the raw data in
apa_list's, the main class of the 'APAtree' package. Aggregation functions are
provided to calculate stand characteristics based on APA-maps such as relative
proportions according to APA-size and the neighborhood diversity index NDiv
(Glatthorn (2021) <doi:10.1016/j.ecolind.2021.108073>).

A New Age-Period-Cohort Model for Describing and Investigating
Inter-Cohort Differences and Life Course Dynamics
It implemented APC-I Model proposed in the paper of Luo and Hodges (2019). A new age-period-cohort model for describing and investigating inter-cohort differences and life course dynamics.
haven, survey, magrittr, tidyverse, dplyr, ggplot2,
data.table, ggpubr, stringr

c(person("Jiahui", "Xu",email = "jpx5053@psu.edu", role = c("aut", "cre")),
person("Liying",  "Luo", email = "liyingluo@psu.edu", role = "aut") )
It implemented Age-Period-Interaction Model (APC-I Model) proposed in the paper of Liying Luo and James S. Hodges in 2019. A new age-period-cohort model for describing and investigating inter-cohort differences and life course dynamics.
survey, magrittr, dplyr, ggplot2, data.table, ggpubr, stringr,
gee

Analysis of Unreplicated Orthogonal Experiments using All
Possible Comparisons
Arden Miller and Abu Zar Md. Shafiullah.
Analysis of  data from unreplicated orthogonal experiments such as 2-level factorial and fractional factorial designs and Plackett-Burman designs using the all possible comparisons (APC) methodology developed by Miller (2005) <doi:10.1198/004017004000000608>.

Routines for Descriptive and Model-Based APC Analysis
c(
person(given = "Alexander",  family = "Bauer",   email = "alexander.bauer@stat.uni-muenchen.de", role = c("aut", "cre"), comment = c(ORCID = "0000-0003-3495-5131")),
person(given = "Maximilian", family = "Weigert", email = "maximilian.weigert@stat.uni-muenchen.de", role = "aut", comment = c(ORCID = "0000-0003-4400-134X")),
person(given = "Hawre",      family = "Jalal",   email = "hjalal@uottawa.ca", role = "aut", comment=c(ORCID = "0000-0002-8224-6834")),
person(given = "Pauline",    family = "Hohenemser", role = "ctb"))
Age-Period-Cohort (APC) analyses are used to differentiate relevant drivers for long-term developments.
The 'APCtools' package offers visualization techniques and general routines to simplify the workflow of an APC analysis.
Sophisticated functions are available both for descriptive and regression model-based analyses.
For the former, we use density (or ridgeline) matrices and (hexagonally binned) heatmaps as innovative visualization
techniques building on the concept of Lexis diagrams.
Model-based analyses build on the separation of the temporal dimensions based on generalized additive models,
where a tensor product interaction surface (usually between age and period) is utilized
to represent the third dimension (usually cohort) on its diagonal.
Such tensor product surfaces can also be estimated while accounting for
further covariates in the regression model.
See Weigert et al. (2021) <doi:10.1177/1354816620987198> for methodological details.
ggpubr, checkmate, knitr, ggplot2, colorspace, dplyr, mgcv,
scales, tidyr
Alexander Bauer [aut, cre] (<https://orcid.org/0000-0003-3495-5131>),
Maximilian Weigert [aut] (<https://orcid.org/0000-0003-4400-134X>),
Hawre Jalal [aut] (<https://orcid.org/0000-0002-8224-6834>),
Pauline Hohenemser [ctb]