## Not run:
# library(intubate)
# library(magrittr)
# library(vegan)
#
# ## There is cheating going on on these examples,
# ## as the cases need two datasets, and only one
# ## is being piped... I may get back to this down the line.
# ## For now, please close an eye.
#
# ## ntbt_adipart: Additive Diversity Partitioning and Hierarchical Null Model Testing
# data(mite)
# data(mite.xy)
# ## Function to get equal area partitions of the mite data
# cutter <- function (x, cut = seq(0, 10, by = 2.5)) {
# out <- rep(1, length(x))
# for (i in 2:(length(cut) - 1))
# out[which(x > cut[i] & x <= cut[(i + 1)])] <- i
# return(out)}
# ## The hierarchy of sample aggregation
# levsm <- with(mite.xy, data.frame(
# l1=1:nrow(mite),
# l2=cutter(y, cut = seq(0, 10, by = 2.5)),
# l3=cutter(y, cut = seq(0, 10, by = 5)),
# l4=cutter(y, cut = seq(0, 10, by = 10))))
#
# ## Original function to interface
# set.seed(1)
# adipart(mite ~ ., levsm, index="richness", nsimul=19)
#
# ## The interface puts data as first parameter
# set.seed(1)
# ntbt_adipart(levsm, mite ~ ., index="richness", nsimul=19)
#
# ## so it can be used easily in a pipeline.
# set.seed(1)
# levsm %>%
# ntbt_adipart(mite ~ ., index="richness", nsimul=19)
#
#
# ## ntbt_adonis: Permutational Multivariate Analysis of Variance Using Distance Matrices
# data(dune)
# data(dune.env)
#
# ## Original function to interface
# set.seed(1)
# adonis(dune ~ Management*A1, data = dune.env)
# adonis2(dune ~ Management*A1, data = dune.env)
#
# ## The interface puts data as first parameter
# set.seed(1)
# ntbt_adonis(dune.env, dune ~ Management*A1)
# ntbt_adonis2(dune.env, dune ~ Management*A1)
#
# ## so it can be used easily in a pipeline.
# set.seed(1)
# dune.env %>%
# ntbt_adonis(dune ~ Management*A1)
# dune.env %>%
# ntbt_adonis2(dune ~ Management*A1)
#
#
# ## ntbt_bioenv: Best Subset of Environmental Variables with
# ## Maximum (Rank) Correlation with Community Dissimilarities
# data(varespec)
# data(varechem)
#
# ## Original function to interface
# bioenv(wisconsin(varespec) ~ log(N) + P + K + Ca + pH + Al, varechem)
#
# ## The interface puts data as first parameter
# ntbt_bioenv(varechem, wisconsin(varespec) ~ log(N) + P + K + Ca + pH + Al)
#
# ## so it can be used easily in a pipeline.
# varechem %>%
# ntbt_bioenv(wisconsin(varespec) ~ log(N) + P + K + Ca + pH + Al)
#
#
# ## ntbt_capscale: [Partial] Distance-based Redundancy Analysis
# ## ntbt_dbrda:
# ## Original function to interface
# capscale(varespec ~ N + P + K + Condition(Al), varechem,
# dist="bray")
# dbrda(varespec ~ N + P + K + Condition(Al), varechem,
# dist="bray")
#
# ## The interface puts data as first parameter
# ntbt_capscale(varechem, varespec ~ N + P + K + Condition(Al),
# dist="bray")
# ntbt_dbrda(varechem, varespec ~ N + P + K + Condition(Al),
# dist="bray")
#
# ## so it can be used easily in a pipeline.
# varechem %>%
# ntbt_capscale(varespec ~ N + P + K + Condition(Al), dist="bray")
# varechem %>%
# ntbt_dbrda(varespec ~ N + P + K + Condition(Al), dist="bray")
#
#
# ## ntbt_cca: [Partial] [Constrained] Correspondence Analysis
# ## and Redundancy Analysis
#
# ## Original function to interface
# cca(varespec ~ Al + P*(K + Baresoil), data = varechem)
#
# ## The interface puts data as first parameter
# ntbt_cca(varechem, varespec ~ Al + P*(K + Baresoil))
#
# ## so it can be used easily in a pipeline.
# varechem %>%
# ntbt_cca(varespec ~ Al + P*(K + Baresoil))
#
#
# ## ntbt_gdispweight: Dispersion-based weighting of species counts
# data(mite, mite.env)
# ## Original function to interface
# gdispweight(mite ~ Shrub + WatrCont, data = mite.env)
#
# ## The interface puts data as first parameter
# ntbt_gdispweight(mite.env, mite ~ Shrub + WatrCont)
#
# ## so it can be used easily in a pipeline.
# mite.env %>%
# ntbt_gdispweight(mite ~ Shrub + WatrCont)
#
#
# ## ntbt_envfit: Fits an Environmental Vector or Factor onto an Ordination
# ord <- cca(dune)
#
# ## Original function to interface
# envfit(ord ~ Moisture + A1, dune.env, perm = 0)
#
# ## The interface puts data as first parameter
# ntbt_envfit(dune.env, ord ~ Moisture + A1, perm = 0)
#
# ## so it can be used easily in a pipeline.
# dune.env %>%
# ntbt_envfit(ord ~ Moisture + A1, perm = 0)
#
# ## ntbt_multipart: Multiplicative Diversity Partitioning
# ## Original function to interface
# multipart(mite ~ ., levsm, index = "renyi", scales = 1, nsimul = 19)
#
# ## The interface puts data as first parameter
# ntbt_multipart(levsm, mite ~ ., index = "renyi", scales = 1, nsimul = 19)
#
# ## so it can be used easily in a pipeline.
# levsm %>%
# ntbt_multipart(mite ~ ., index = "renyi", scales = 1, nsimul = 19)
#
#
# ## ntbt_ordisurf: Fit and Plot Smooth Surfaces of Variables on Ordination.
# vare.dist <- vegdist(varespec)
# vare.mds <- monoMDS(vare.dist)
#
# ## Original function to interface
# ordisurf(vare.mds ~ Baresoil, varechem, bubble = 5)
#
# ## The interface puts data as first parameter
# ntbt_ordisurf(varechem, vare.mds ~ Baresoil, bubble = 5)
#
# ## so it can be used easily in a pipeline.
# varechem %>%
# ntbt_ordisurf(vare.mds ~ Baresoil, bubble = 5)
#
#
# ## ntbt_ordixyplot: Trellis (Lattice) Plots for Ordination
# ## Original function to interface
# ordicloud(ord, form = CA2 ~ CA3*CA1, groups = Manure, data = dune.env)
# ordisplom(ord, data = dune.env, form = ~ . | Management, groups=Manure)
# ordixyplot(ord, data=dune.env, form = CA1 ~ CA2 | Management, groups=Manure)
#
# ## The interface puts data as first parameter
# ntbt_ordicloud(dune.env, ord, form = CA2 ~ CA3*CA1, groups = Manure)
# ntbt_ordisplom(dune.env, ord, form = ~ . | Management, groups=Manure)
# ntbt_ordixyplot(dune.env, ord, form = CA1 ~ CA2 | Management, groups=Manure)
#
# ## so it can be used easily in a pipeline.
# dune.env %>%
# ntbt_ordicloud(ord, form = CA2 ~ CA3*CA1, groups = Manure)
# dune.env %>%
# ntbt_ordisplom(ord, form = ~ . | Management, groups=Manure)
# dune.env %>%
# ntbt_ordixyplot(ord, form = CA1 ~ CA2 | Management, groups=Manure)
# ## End(Not run)
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