58  Statistical Topics

ImportantDisclaimer

These packages (Note 1) are a one-person project undergoing rapid evolution. Backward compatibility (per Hadley Wickham) is provided as a courtesy rather than a guarantee.

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Table 58.1 summarizes the S3 generic functions, or the “pseudo” S3 methods, for the statistical functionalities implemented in these packages (Note 1) and/or covered in this Quarto book (Note 2), and where to find them.

Table 58.1: Index of Statistical Topics
Topic S3 Generic Function, or (Pseudo) S3 Method Where to Find
A
aggregate to (grouped) hyper data frame aggregate2hyper() (Table 18.1) from data.frame (Section 18.1), from groupedData (Section 24.1)
aggregation aggregate() of hyperframe (Section 26.4)
aggregate marks-statistics aggregate_marks() (Table 36.9) of ppp (Section 36.7)
append to (existing) marks `append_marks<-` (Table 36.11) of ppp (Section 36.8), of psp (Section 38.2), of tess (Section 42.2)
F
function-value, recommended, etc. keyval() (Table 20.3) of fv (Section 20.2)
function-value, replace with theoretical values .illegal2theo() (Table 20.5), .disrecommend2theo() (Table 20.6) of fv (Section 20.5.1)
function-value-tables from eligible marks Emark_(), Vmark_(), etc. (Table 36.21) of ppp (Section 36.13)
G
group-generic, Math Math() (Table 36.2, Table 36.3) of numeric marks in ppp (Section 36.3.1), psp (Section 38.1.1) and tess (Section 42.1.1)
group-generic, Summary Summary() (Table 36.4, Table 36.5) of numeric marks in ppp (Section 36.3.2), psp (Section 38.1.2) and tess (Section 42.1.2)
grouped hyper data frame, to create as.groupedHyperframe() (Table 26.3) from hyperframe (Section 26.5)
I
interpolation approxfun.*(), splinefun.*(), interpSpline_.fv() of fv (Section 20.6.1)
K
kernel density (Becker et al. 1988) estimates kerndens() (Table 33.2) of numeric vector (Section 33.1)
kernel density (Becker et al. 1988) of numeric marks density_marks() (Table 36.6) of ppp (Section 36.4)
kernel density (Becker et al. 1988) estimates of numeric marks kerndens() (Table 33.2) of ppp (Section 36.4)
\(k\)-means clustering (Hartigan and Wong 1979) kmeans.*() of ppp (Section 36.10)
L
length length() of hyperframe (Section 26.3)
Q
quantile of numeric marks quantile() (Table 36.8) of ppp (Section 36.5)
R
random re-labelling envelope residual rlabelRes() (Table 36.20) of ppp (Section 36.12)
\(r_\text{max}\), default .rmax() (Table 36.13) of ppp (Section 36.9), of fv (Section 20.4)
\(r_\text{max}\), legal of fv (Section 20.5)
S
smoothing loess.*(), ksmooth.*(), smooth.spline.*() of fv (Section 20.6.2)
split, by \(k\)-means clustering split() (Table 36.16) of pppkm (Section 36.10.2), of hyperframekm (Section 26.7.2)
split (default method) split.default() on anylist (Section 15.1)
superimpose superimpose() of hyperframe (Section 26.6)
T
Tjøstheim (1978)’s coefficient, pairwise pairwise_cor_spatial() (Table 36.17) of ppp (Section 36.11)
trapezoidal integration, (cumulative) average vertical height vtrapz(), cumvtrapz() (Table 11.1) theory (Section 11.2), of numeric vector (Section 33.2), of fv (Section 20.3, Figure 11.9)
trapezoidal integration, (cumulative) average vertical height, visualize visualize_vtrapz() (Table 11.2) gallery (Section 11.3), of numeric vector (Section 33.2), of function (Section 19.1), of fv (Section 20.3, Figure 11.9), of listof (Section 44.1)

Table 58.2 keeps track of the number of S3 generic functions, S3 methods and all other non-S3 functions in these package.

Listing 58.1: Table: Number of functions in these package
Code 
c('groupedHyperframe', 'groupedHyperframe.random', 'hyper.gam', 'maxEff') |>
  lapply(FUN = \(pkg) {
    ns = pkg |> getNamespace()
    nm = ns |> 
      as.list.environment() |>
      names()
    id_generic = nm |>
      vapply(FUN = sloop::is_s3_generic, env = ns, FUN.VALUE = NA)
    id_method = nm |>
      vapply(FUN = sloop::is_s3_method, env = ns, FUN.VALUE = NA)
    return(list(
      'Package' = sprintf(fmt = '**`%s`** (v%s)', pkg, packageVersion(pkg)),
      '`S3` Generic Function' = sum(id_generic),
      '`S3` Method' = sum(id_method),
      'non-`S3` Function' = sum(!id_generic & !id_method)
    ))
  }) |>
  c(list(f = c)) |>
  do.call(what = Map, args = _) |>
  as.data.frame.list(check.names = FALSE) |>
  within.data.frame(expr = {
    Total = `\`S3\` Generic Function` + `\`S3\` Method` + `non-\`S3\` Function`
  }) |>
  knitr::kable(
    caption = 'Number of `S3` generic functions, `S3` methods and non-`S3` functions in *these package*'
  )
Table 58.2: Number of S3 generic functions, S3 methods and non-S3 functions in these package
Package S3 Generic Function S3 Method non-S3 Function Total
groupedHyperframe (v0.4.0) 23 69 36 128
groupedHyperframe.random (v0.2.4) 0 0 3 3
hyper.gam (v0.2.4) 2 7 7 16
maxEff (v0.2.4) 1 14 6 21