
R version 3.6.2 Patched (2020-02-12 r77795) -- "Dark and Stormy Night"
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> ## For examples skipped in testing because they need recommended packages.
> 
> ## This is skipped entirely on a Unix-alike if recommended packages are,
> ## so for Windows
> if(!require("MASS")) q()
Loading required package: MASS
> 
> pdf("reg-examples-3.pdf", encoding = "ISOLatin1.enc")
> 
> ## From datasets
> if(require("survival")) {
+   model3 <- clogit(case ~ spontaneous+induced+strata(stratum), data = infert)
+   print(summary(model3))
+   detach("package:survival", unload = TRUE)  # survival (conflicts)
+ }
Loading required package: survival
Call:
coxph(formula = Surv(rep(1, 248L), case) ~ spontaneous + induced + 
    strata(stratum), data = infert, method = "exact")

  n= 248, number of events= 83 

              coef exp(coef) se(coef)     z Pr(>|z|)    
spontaneous 1.9859    7.2854   0.3524 5.635 1.75e-08 ***
induced     1.4090    4.0919   0.3607 3.906 9.38e-05 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

            exp(coef) exp(-coef) lower .95 upper .95
spontaneous     7.285     0.1373     3.651    14.536
induced         4.092     0.2444     2.018     8.298

Concordance= 0.776  (se = 0.044 )
Likelihood ratio test= 53.15  on 2 df,   p=3e-12
Wald test            = 31.84  on 2 df,   p=1e-07
Score (logrank) test = 48.44  on 2 df,   p=3e-11

> 
> 
> ## From grDevices
> x1  <- matrix(rnorm(1e3), ncol = 2)
> x2  <- matrix(rnorm(1e3, mean = 3, sd = 1.5), ncol = 2)
> x   <- rbind(x1, x2)
> 
> dcols <- densCols(x)
> graphics::plot(x, col = dcols, pch = 20, main = "n = 1000")
> 
> 
> ## From graphics:
> ## A largish data set
> set.seed(123)
> n <- 10000
> x1  <- matrix(rnorm(n), ncol = 2)
> x2  <- matrix(rnorm(n, mean = 3, sd = 1.5), ncol = 2)
> x   <- rbind(x1, x2)
> 
> oldpar <- par(mfrow = c(2, 2))
> smoothScatter(x, nrpoints = 0)
> smoothScatter(x)
> 
> ## a different color scheme:
> Lab.palette <- colorRampPalette(c("blue", "orange", "red"), space = "Lab")
> smoothScatter(x, colramp = Lab.palette)
> 
> ## somewhat similar, using identical smoothing computations,
> ## but considerably *less* efficient for really large data:
> plot(x, col = densCols(x), pch = 20)
> 
> ## use with pairs:
> par(mfrow = c(1, 1))
> y <- matrix(rnorm(40000), ncol = 4) + 3*rnorm(10000)
> y[, c(2,4)] <-  -y[, c(2,4)]
> pairs(y, panel = function(...) smoothScatter(..., nrpoints = 0, add = TRUE))
> 
> par(oldpar)
> 
> 
> ## From stats
> # alias.Rd
> op <- options(contrasts = c("contr.helmert", "contr.poly"))
> npk.aov <- aov(yield ~ block + N*P*K, npk)
> alias(npk.aov)
Model :
yield ~ block + N * P * K

Complete :
         (Intercept) block1 block2 block3 block4 block5 N1    P1    K1    N1:P1
N1:P1:K1     0           1    1/3    1/6  -3/10   -1/5      0     0     0     0
         N1:K1 P1:K1
N1:P1:K1     0     0

> options(op)  # reset
> 
> # as.hclust.Rd
> if(require("cluster", quietly = TRUE)) {# is a recommended package
+   set.seed(123)
+   x <- matrix(rnorm(30), ncol = 3)
+   hc <- hclust(dist(x), method = "complete")
+   ag <- agnes(x, method = "complete")
+   hcag <- as.hclust(ag)
+   ## The dendrograms order slightly differently:
+   op <- par(mfrow = c(1,2))
+   plot(hc) ;  mtext("hclust", side = 1)
+   plot(hcag); mtext("agnes",  side = 1)
+   detach("package:cluster")
+ }
> 
> # confint.Rd
> counts <- c(18,17,15,20,10,20,25,13,12)
> outcome <- gl(3, 1, 9); treatment <- gl(3, 3)
> glm.D93 <- glm(counts ~ outcome + treatment, family = poisson())
> confint(glm.D93)
Waiting for profiling to be done...
                 2.5 %      97.5 %
(Intercept)  2.6958215  3.36655581
outcome2    -0.8577018 -0.06255840
outcome3    -0.6753696  0.08244089
treatment2  -0.3932548  0.39325483
treatment3  -0.3932548  0.39325483
> confint.default(glm.D93)  # based on asymptotic normality}
                 2.5 %      97.5 %
(Intercept)  2.7095672  3.37947764
outcome2    -0.8505027 -0.05800787
outcome3    -0.6707552  0.08478093
treatment2  -0.3919928  0.39199279
treatment3  -0.3919928  0.39199279
> 
> # contrasts.Rd
> utils::example(factor)

factor> (ff <- factor(substring("statistics", 1:10, 1:10), levels = letters))
 [1] s t a t i s t i c s
Levels: a b c d e f g h i j k l m n o p q r s t u v w x y z

factor> as.integer(ff)      # the internal codes
 [1] 19 20  1 20  9 19 20  9  3 19

factor> (f. <- factor(ff))  # drops the levels that do not occur
 [1] s t a t i s t i c s
Levels: a c i s t

factor> ff[, drop = TRUE]   # the same, more transparently
 [1] s t a t i s t i c s
Levels: a c i s t

factor> factor(letters[1:20], labels = "letter")
 [1] letter1  letter2  letter3  letter4  letter5  letter6  letter7  letter8 
 [9] letter9  letter10 letter11 letter12 letter13 letter14 letter15 letter16
[17] letter17 letter18 letter19 letter20
20 Levels: letter1 letter2 letter3 letter4 letter5 letter6 letter7 ... letter20

factor> class(ordered(4:1)) # "ordered", inheriting from "factor"
[1] "ordered" "factor" 

factor> z <- factor(LETTERS[3:1], ordered = TRUE)

factor> ## and "relational" methods work:
factor> stopifnot(sort(z)[c(1,3)] == range(z), min(z) < max(z))

factor> ## Don't show: 
factor> of <- ordered(ff)

factor> stopifnot(identical(range(of, rev(of)), of[3:2]),
factor+ 	  identical(max(of), of[2]))

factor> ## End(Don't show)
factor> 
factor> ## suppose you want "NA" as a level, and to allow missing values.
factor> (x <- factor(c(1, 2, NA), exclude = NULL))
[1] 1    2    <NA>
Levels: 1 2 <NA>

factor> is.na(x)[2] <- TRUE

factor> x  # [1] 1    <NA> <NA>
[1] 1    <NA> <NA>
Levels: 1 2 <NA>

factor> is.na(x)
[1] FALSE  TRUE FALSE

factor> # [1] FALSE  TRUE FALSE
factor> 
factor> ## More rational, since R 3.4.0 :
factor> factor(c(1:2, NA), exclude =  "" ) # keeps <NA> , as
[1] 1    2    <NA>
Levels: 1 2 <NA>

factor> factor(c(1:2, NA), exclude = NULL) # always did
[1] 1    2    <NA>
Levels: 1 2 <NA>

factor> ## exclude = <character>
factor> z # ordered levels 'A < B < C'
[1] C B A
Levels: A < B < C

factor> factor(z, exclude = "C") # does exclude
[1] <NA> B    A   
Levels: A < B

factor> factor(z, exclude = "B") # ditto
[1] C    <NA> A   
Levels: A < C

factor> ## Now, labels maybe duplicated:
factor> ## factor() with duplicated labels allowing to "merge levels"
factor> x <- c("Man", "Male", "Man", "Lady", "Female")

factor> ## Map from 4 different values to only two levels:
factor> (xf <- factor(x, levels = c("Male", "Man" , "Lady",   "Female"),
factor+                  labels = c("Male", "Male", "Female", "Female")))
[1] Male   Male   Male   Female Female
Levels: Male Female

factor> #> [1] Male   Male   Male   Female Female
factor> #> Levels: Male Female
factor> 
factor> ## Using addNA()
factor> Month <- airquality$Month

factor> table(addNA(Month))

   5    6    7    8    9 <NA> 
  31   30   31   31   30    0 

factor> table(addNA(Month, ifany = TRUE))

 5  6  7  8  9 
31 30 31 31 30 
> fff <- ff[, drop = TRUE]  # reduce to 5 levels.
> contrasts(fff) <- contr.sum(5)[, 1:2]; contrasts(fff)
  [,1] [,2]       [,3]       [,4]
a    1    0 -0.2471257  0.2688164
c    0    1 -0.2471257  0.2688164
i    0    0 -0.1498721 -0.8817814
s    0    0  0.8912491  0.0753323
t   -1   -1 -0.2471257  0.2688164
> 
> ## using sparse contrasts: % useful, once model.matrix() works with these :
> ffs <- fff
> contrasts(ffs) <- contr.sum(5, sparse = TRUE)[, 1:2]; contrasts(ffs)
  [,1] [,2]       [,3]       [,4]
a    1    0 -0.2471257  0.2688164
c    0    1 -0.2471257  0.2688164
i    0    0 -0.1498721 -0.8817814
s    0    0  0.8912491  0.0753323
t   -1   -1 -0.2471257  0.2688164
> stopifnot(all.equal(ffs, fff))
> contrasts(ffs) <- contr.sum(5, sparse = TRUE); contrasts(ffs)
5 x 4 sparse Matrix of class "dgCMatrix"
             
a  1  .  .  .
c  .  1  .  .
i  .  .  1  .
s  .  .  .  1
t -1 -1 -1 -1
> 
> # glm.Rd
> utils::data(anorexia, package = "MASS")
> 
> anorex.1 <- glm(Postwt ~ Prewt + Treat + offset(Prewt),
+                 family = gaussian, data = anorexia)
> summary(anorex.1)

Call:
glm(formula = Postwt ~ Prewt + Treat + offset(Prewt), family = gaussian, 
    data = anorexia)

Deviance Residuals: 
     Min        1Q    Median        3Q       Max  
-14.1083   -4.2773   -0.5484    5.4838   15.2922  

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept)  49.7711    13.3910   3.717 0.000410 ***
Prewt        -0.5655     0.1612  -3.509 0.000803 ***
TreatCont    -4.0971     1.8935  -2.164 0.033999 *  
TreatFT       4.5631     2.1333   2.139 0.036035 *  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for gaussian family taken to be 48.69504)

    Null deviance: 4525.4  on 71  degrees of freedom
Residual deviance: 3311.3  on 68  degrees of freedom
AIC: 489.97

Number of Fisher Scoring iterations: 2

> 
> # logLik.Rd
> utils::data(Orthodont, package = "nlme")
> fm1 <- lm(distance ~ Sex * age, Orthodont)
> logLik(fm1)
'log Lik.' -239.1209 (df=5)
> logLik(fm1, REML = TRUE)
'log Lik.' -241.7796 (df=5)
> 
> # nls.Rd
> od <- options(digits=5)
> ## The muscle dataset in MASS is from an experiment on muscle
> ## contraction on 21 animals.  The observed variables are Strip
> ## (identifier of muscle), Conc (Cacl concentration) and Length
> ## (resulting length of muscle section).
> utils::data(muscle, package = "MASS")
> 
> ## The non linear model considered is
> ##       Length = alpha + beta*exp(-Conc/theta) + error
> ## where theta is constant but alpha and beta may vary with Strip.
> 
> with(muscle, table(Strip)) # 2, 3 or 4 obs per strip
Strip
S01 S02 S03 S04 S05 S06 S07 S08 S09 S10 S11 S12 S13 S14 S15 S16 S17 S18 S19 S20 
  4   4   4   3   3   3   2   2   2   2   3   2   2   2   2   4   4   3   3   3 
S21 
  3 
> 
> ## We first use the plinear algorithm to fit an overall model,
> ## ignoring that alpha and beta might vary with Strip.
> 
> musc.1 <- nls(Length ~ cbind(1, exp(-Conc/th)), muscle,
+               start = list(th = 1), algorithm = "plinear")
> ## IGNORE_RDIFF_BEGIN
> summary(musc.1)

Formula: Length ~ cbind(1, exp(-Conc/th))

Parameters:
      Estimate Std. Error t value Pr(>|t|)    
th       0.608      0.115    5.31  1.9e-06 ***
.lin1   28.963      1.230   23.55  < 2e-16 ***
.lin2  -34.227      3.793   -9.02  1.4e-12 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 4.67 on 57 degrees of freedom

Number of iterations to convergence: 5 
Achieved convergence tolerance: 9.32e-07

> ## IGNORE_RDIFF_END
> 
> ## Then we use nls' indexing feature for parameters in non-linear
> ## models to use the conventional algorithm to fit a model in which
> ## alpha and beta vary with Strip.  The starting values are provided
> ## by the previously fitted model.
> ## Note that with indexed parameters, the starting values must be
> ## given in a list (with names):
> b <- coef(musc.1)
> musc.2 <- nls(Length ~ a[Strip] + b[Strip]*exp(-Conc/th), muscle,
+               start = list(a = rep(b[2], 21), b = rep(b[3], 21), th = b[1]))
> ## IGNORE_RDIFF_BEGIN
> summary(musc.2)

Formula: Length ~ a[Strip] + b[Strip] * exp(-Conc/th)

Parameters:
    Estimate Std. Error t value Pr(>|t|)    
a1    23.454      0.796   29.46  5.0e-16 ***
a2    28.302      0.793   35.70  < 2e-16 ***
a3    30.801      1.716   17.95  1.7e-12 ***
a4    25.921      3.016    8.60  1.4e-07 ***
a5    23.201      2.891    8.02  3.5e-07 ***
a6    20.120      2.435    8.26  2.3e-07 ***
a7    33.595      1.682   19.98  3.0e-13 ***
a8    39.053      3.753   10.41  8.6e-09 ***
a9    32.137      3.318    9.69  2.5e-08 ***
a10   40.005      3.336   11.99  1.0e-09 ***
a11   36.190      3.109   11.64  1.6e-09 ***
a12   36.911      1.839   20.07  2.8e-13 ***
a13   30.635      1.700   18.02  1.6e-12 ***
a14   34.312      3.495    9.82  2.0e-08 ***
a15   38.395      3.375   11.38  2.3e-09 ***
a16   31.226      0.886   35.26  < 2e-16 ***
a17   31.230      0.821   38.02  < 2e-16 ***
a18   19.998      1.011   19.78  3.6e-13 ***
a19   37.095      1.071   34.65  < 2e-16 ***
a20   32.594      1.121   29.07  6.2e-16 ***
a21   30.376      1.057   28.74  7.5e-16 ***
b1   -27.300      6.873   -3.97  0.00099 ***
b2   -26.270      6.754   -3.89  0.00118 ** 
b3   -30.901      2.270  -13.61  1.4e-10 ***
b4   -32.238      3.810   -8.46  1.7e-07 ***
b5   -29.941      3.773   -7.94  4.1e-07 ***
b6   -20.622      3.647   -5.65  2.9e-05 ***
b7   -19.625      8.085   -2.43  0.02661 *  
b8   -45.780      4.113  -11.13  3.2e-09 ***
b9   -31.345      6.352   -4.93  0.00013 ***
b10  -38.599      3.955   -9.76  2.2e-08 ***
b11  -33.921      3.839   -8.84  9.2e-08 ***
b12  -38.268      8.992   -4.26  0.00053 ***
b13  -22.568      8.194   -2.75  0.01355 *  
b14  -36.167      6.358   -5.69  2.7e-05 ***
b15  -32.952      6.354   -5.19  7.4e-05 ***
b16  -47.207      9.540   -4.95  0.00012 ***
b17  -33.875      7.688   -4.41  0.00039 ***
b18  -15.896      6.222   -2.55  0.02051 *  
b19  -28.969      7.235   -4.00  0.00092 ***
b20  -36.917      8.033   -4.60  0.00026 ***
b21  -26.508      7.012   -3.78  0.00149 ** 
th     0.797      0.127    6.30  8.0e-06 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.11 on 17 degrees of freedom

Number of iterations to convergence: 8 
Achieved convergence tolerance: 2.17e-06

> ## IGNORE_RDIFF_END
> options(od)
> 
> # princomp.Rd
> ## Robust:
> (pc.rob <- princomp(stackloss, covmat = MASS::cov.rob(stackloss)))
Call:
princomp(x = stackloss, covmat = MASS::cov.rob(stackloss))

Standard deviations:
   Comp.1    Comp.2    Comp.3    Comp.4 
7.8322873 4.0077676 1.9114016 0.7624211 

 4  variables and  21 observations.
> 
> # termplot.R
> library(MASS)
> hills.lm <- lm(log(time) ~ log(climb)+log(dist), data = hills)
> termplot(hills.lm, partial.resid = TRUE, smooth = panel.smooth,
+         terms = "log(dist)", main = "Original")
> termplot(hills.lm, transform.x = TRUE,
+          partial.resid = TRUE, smooth = panel.smooth,
+ 	 terms = "log(dist)", main = "Transformed")
> 
> # xtabs.Rd
> if(require("Matrix")) {
+  ## similar to "nlme"s  'ergoStool' :
+  d.ergo <- data.frame(Type = paste0("T", rep(1:4, 9*4)),
+                       Subj = gl(9, 4, 36*4))
+  print(xtabs(~ Type + Subj, data = d.ergo)) # 4 replicates each
+  set.seed(15) # a subset of cases:
+  print(xtabs(~ Type + Subj, data = d.ergo[sample(36, 10), ], sparse = TRUE))
+ 
+  ## Hypothetical two-level setup:
+  inner <- factor(sample(letters[1:25], 100, replace = TRUE))
+  inout <- factor(sample(LETTERS[1:5], 25, replace = TRUE))
+  fr <- data.frame(inner = inner, outer = inout[as.integer(inner)])
+  print(xtabs(~ inner + outer, fr, sparse = TRUE))
+ }
Loading required package: Matrix
    Subj
Type 1 2 3 4 5 6 7 8 9
  T1 4 4 4 4 4 4 4 4 4
  T2 4 4 4 4 4 4 4 4 4
  T3 4 4 4 4 4 4 4 4 4
  T4 4 4 4 4 4 4 4 4 4
4 x 9 sparse Matrix of class "dgCMatrix"
    Subj
Type 1 2 3 4 5 6 7 8 9
  T1 . 1 . . . 1 . . .
  T2 . . . . . . . 1 1
  T3 . . 1 1 1 . 1 1 .
  T4 . 1 . . . . . . .
25 x 4 sparse Matrix of class "dgCMatrix"
     outer
inner A B C E
    a . . . 3
    b . . . 6
    c . . 5 .
    d . 1 . .
    e . . 3 .
    f . . 5 .
    g . . 3 .
    h 6 . . .
    i 8 . . .
    j . 1 . .
    k . . . 6
    l . 2 . .
    m . . . 7
    n . . 3 .
    o . 3 . .
    p . 3 . .
    q . . . 7
    r . . . 2
    s . 6 . .
    t . . . 5
    u 4 . . .
    v . . 3 .
    w . 3 . .
    x . . 3 .
    y 2 . . .
> 
> ## From utils
> example(packageDescription)

pckgDs> ## No test: 
pckgDs> ##D packageDescription("stats")
pckgDs> ##D packageDescription("stats", fields = c("Package", "Version"))
pckgDs> ##D 
pckgDs> ##D packageDescription("stats", fields = "Version")
pckgDs> ##D packageDescription("stats", fields = "Version", drop = FALSE)
pckgDs> ##D 
pckgDs> ##D if(packageVersion("MASS") < "7.3.29")
pckgDs> ##D   message("you need to update 'MASS'")
pckgDs> ## End(No test)
pckgDs> pu <- packageDate("utils")

pckgDs> ## No test: 
pckgDs> ##D str(pu)
pckgDs> ## End(No test)
pckgDs> stopifnot(identical(pu, packageDate(desc = packageDescription("utils"))),
pckgDs+           identical(pu, packageDate("stats"))) # as "utils" and "stats" are

pckgDs>                                    # both 'base R' and "Built" at same time
pckgDs> 
pckgDs> 
pckgDs> 
> 
> 
> ## From splines
> library(splines)
> Matrix::drop0(zapsmall(6*splineDesign(knots = 1:40, x = 4:37, sparse = TRUE)))
34 x 36 sparse Matrix of class "dgCMatrix"
                                                                             
 [1,] 1 4 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
 [2,] . 1 4 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
 [3,] . . 1 4 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
 [4,] . . . 1 4 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
 [5,] . . . . 1 4 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
 [6,] . . . . . 1 4 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . .
 [7,] . . . . . . 1 4 1 . . . . . . . . . . . . . . . . . . . . . . . . . . .
 [8,] . . . . . . . 1 4 1 . . . . . . . . . . . . . . . . . . . . . . . . . .
 [9,] . . . . . . . . 1 4 1 . . . . . . . . . . . . . . . . . . . . . . . . .
[10,] . . . . . . . . . 1 4 1 . . . . . . . . . . . . . . . . . . . . . . . .
[11,] . . . . . . . . . . 1 4 1 . . . . . . . . . . . . . . . . . . . . . . .
[12,] . . . . . . . . . . . 1 4 1 . . . . . . . . . . . . . . . . . . . . . .
[13,] . . . . . . . . . . . . 1 4 1 . . . . . . . . . . . . . . . . . . . . .
[14,] . . . . . . . . . . . . . 1 4 1 . . . . . . . . . . . . . . . . . . . .
[15,] . . . . . . . . . . . . . . 1 4 1 . . . . . . . . . . . . . . . . . . .
[16,] . . . . . . . . . . . . . . . 1 4 1 . . . . . . . . . . . . . . . . . .
[17,] . . . . . . . . . . . . . . . . 1 4 1 . . . . . . . . . . . . . . . . .
[18,] . . . . . . . . . . . . . . . . . 1 4 1 . . . . . . . . . . . . . . . .
[19,] . . . . . . . . . . . . . . . . . . 1 4 1 . . . . . . . . . . . . . . .
[20,] . . . . . . . . . . . . . . . . . . . 1 4 1 . . . . . . . . . . . . . .
[21,] . . . . . . . . . . . . . . . . . . . . 1 4 1 . . . . . . . . . . . . .
[22,] . . . . . . . . . . . . . . . . . . . . . 1 4 1 . . . . . . . . . . . .
[23,] . . . . . . . . . . . . . . . . . . . . . . 1 4 1 . . . . . . . . . . .
[24,] . . . . . . . . . . . . . . . . . . . . . . . 1 4 1 . . . . . . . . . .
[25,] . . . . . . . . . . . . . . . . . . . . . . . . 1 4 1 . . . . . . . . .
[26,] . . . . . . . . . . . . . . . . . . . . . . . . . 1 4 1 . . . . . . . .
[27,] . . . . . . . . . . . . . . . . . . . . . . . . . . 1 4 1 . . . . . . .
[28,] . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 4 1 . . . . . .
[29,] . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 4 1 . . . . .
[30,] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 4 1 . . . .
[31,] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 4 1 . . .
[32,] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 4 1 . .
[33,] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 4 1 .
[34,] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 4 1
> 
> 
> ## From tools
> 
> library(tools)
> ## there are few dependencies in a vanilla R installation:
> ## lattice may not be installed
> ## Avoid possibly large list from R_HOME/site-library, which --vanilla includes.
> dependsOnPkgs("lattice", lib.loc = .Library)
[1] "Matrix"   "nlme"     "mgcv"     "survival"
> 
> ## This may not be installed
> gridEx <- system.file("doc", "grid.Rnw", package = "grid")
> vignetteDepends(gridEx)
$Depends
[1] "lattice"

$Installed
[1] "lattice"

$Found
list()

$NotFound
character(0)

$R
[1] "R (>= 3.0.0)"

attr(,"class")
[1] "DependsList"
Warning messages:
1: 'vignetteDepends' is deprecated.
Use 'vignetteInfo()$depends or package_dependencies()' instead.
See help("Deprecated") 
2: 'getDepList' is deprecated.
Use 'dependsOnPkgs() or package_dependencies()' instead.
See help("Deprecated") 
3: 'package.dependencies' is deprecated.
Use 'package_dependencies' instead.
See help("Deprecated") 
>