Interactive R Tutorials to Accompany Field (2016), "An Adventure
in Statistics"
person("Andy", "Field", email = "andyf@sussex.ac.uk", role = c("aut", "cre"))
Interactive 'R' tutorials written using 'learnr' for Field (2016), "An Adventure in Statistics", <ISBN:9781446210451>.
Topics include general workflow in 'R' and 'Rstudio', the 'R' environment and 'tidyverse', summarizing data, model fitting, central tendency,
visualising data using 'ggplot2', inferential statistics and robust estimation, hypothesis testing, the general linear model, comparing means,
repeated measures designs, factorial designs, multilevel models, growth models, and generalized linear models (logistic regression).
BayesFactor, boot, dplyr, car, effsize, effects, forcats,
GGally, ggplot2, Hmisc, lm.beta, nlme, readr, robust, sandwich,
sjstats, tidyr, WRS2

person("Panfeng", "Zhang", email = "zhangpanfeng@cug.edu.cn", role = c("aut", "cre"))
The irregularly-spaced data are interpolated onto regular latitude-longitude
grids by weighting each station according to its distance and angle from the
center of a search radius.

person("Panfeng", "Zhang", , "zhangpanfeng@cug.edu.cn", role = c("aut", "cre"),
comment = c(ORCID = "0000-0001-6084-9231"))
The irregularly-spaced data are interpolated onto regular latitude-longitude grids by weighting each station according to its distance and angle from the center of a search radius.
Panfeng Zhang [aut, cre] (<https://orcid.org/0000-0001-6084-9231>)

Wavelet Analysis of Genomic Data from Admixed Populations
Implements wavelet-based approaches for describing population admixture. Principal Components Analysis (PCA) is used to define the population structure and produce a localized admixture signal for each individual. Wavelet summaries of the PCA output describe variation present in the data and can be related to population-level demographic processes. For more details, see accompanying paper.

Implements wavelet-based approaches for describing population admixture. Principal Components Analysis (PCA) is used to define the population structure and produce a localized admixture signal for each individual. Wavelet summaries of the PCA output describe variation present in the data and can be related to population-level demographic processes. For more details, see Sanderson et al. (2015).