R Under development (unstable) (2024-09-15 r87152 ucrt) -- "Unsuffered Consequences"
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> 
>  #### copied from help page for function library
> 
>  pkg <- "aster"
>  library(pkg, character.only = TRUE)
> 
>  set.seed(42)
> 
>  #### copied from help page for function aster in package aster
> 
>  data(echinacea)
>  vars <- c("ld02", "ld03", "ld04", "fl02", "fl03", "fl04",
+      "hdct02", "hdct03", "hdct04")
>  redata <- reshape(echinacea, varying = list(vars), direction = "long",
+      timevar = "varb", times = as.factor(vars), v.names = "resp")
>  redata <- data.frame(redata, root = 1)
>  pred <- c(0, 1, 2, 1, 2, 3, 4, 5, 6)
>  fam <- c(1, 1, 1, 1, 1, 1, 3, 3, 3)
>  hdct <- grep("hdct", as.character(redata$varb))
>  hdct <- is.element(seq(along = redata$varb), hdct)
>  redata <- data.frame(redata, hdct = as.integer(hdct))
>  aout.foo <- aster(resp ~ varb + nsloc + ewloc + pop : hdct,
+      pred, fam, varb, id, root, data = redata, type = "conditional")
>  # summary(aout.foo)
> 
>  beta <- aout.foo$coefficients
>  dbeta <- rnorm(length(beta))
> 
>  pout <- predict(aout.foo, parm.type = "canonical",
+      model.type = "conditional", se.fit = TRUE)
>  theta <- pout$fit
>  dtheta <- as.vector(pout$gradient %*% dbeta)
> 
>  pout <- predict(aout.foo, parm.type = "canonical",
+      model.type = "unconditional", se.fit = TRUE)
>  phi <- pout$fit
>  dphi <- as.vector(pout$gradient %*% dbeta)
> 
>  pout <- predict(aout.foo, parm.type = "mean.value",
+      model.type = "unconditional", se.fit = TRUE)
>  mu <- pout$fit
>  dmu <- as.vector(pout$gradient %*% dbeta)
> 
>  phony <- matrix(1, nrow = nrow(aout.foo$x), ncol = ncol(aout.foo$x))
>  pout <- predict.aster(aout.foo, x = phony, root = aout.foo$root,
+      modmat = aout.foo$modmat, parm.type = "mean.value",
+      model.type = "conditional", se.fit = TRUE)
>  xi <- pout$fit
>  dxi <- as.vector(pout$gradient %*% dbeta)
> 
>  offset <- as.vector(aout.foo$origin)
>  modmat <- matrix(aout.foo$modmat, ncol = length(beta))
> 
>  #### copied from help page for function library
> 
>  detach(pos = match(paste("package", pkg, sep=":"), search()))
> 
>  #### end of stuff from old aster package
> 
>  rm(list = setdiff(ls(), c("beta", "theta", "phi", "xi", "mu", "tau",
+      "offset", "modmat", "dbeta", "dtheta", "dphi", "dxi", "dmu", "dtau")))
> 
>  library(aster2)
Loading required package: Matrix
Registered S3 methods overwritten by 'aster2':
  method              from 
  as.character.astfam aster
  print.astfam        aster
This is beta software.
Unless you need to do aster models with dependence groups,
    use package "aster" instead.
See help(aster2-package) for differences from package "aster"
    and examples.

> 
>  data(echinacea)
> 
>  #### saturated
> 
>  myphi <- transformSaturated(theta, echinacea, from = "theta", to = "phi")
>  all.equal(phi, myphi)
[1] TRUE
> 
>  mytheta <- transformSaturated(phi, echinacea, from = "phi", to = "theta")
>  all.equal(theta, mytheta)
[1] TRUE
> 
>  myxi <- transformSaturated(theta, echinacea, from = "theta", to = "xi")
>  all.equal(xi, myxi)
[1] TRUE
> 
>  mymu <- transformSaturated(xi, echinacea, from = "xi", to = "mu")
>  all.equal(mu, mymu)
[1] TRUE
> 
>  #### unconditional from == "beta"
> 
>  phi.foo <- transformConditional(beta, modmat, echinacea,
+      from = "beta", to = "phi", offset = offset)
>  all.equal(phi, phi.foo)
[1] TRUE
> 
>  theta.foo <- transformConditional(beta, modmat, echinacea,
+      from = "beta", to = "theta", offset = offset)
>  all.equal(theta, theta.foo)
[1] TRUE
> 
>  xi.foo <- transformConditional(beta, modmat, echinacea,
+      from = "beta", to = "xi", offset = offset)
>  all.equal(xi, xi.foo)
[1] TRUE
> 
>  mu.foo <- transformConditional(beta, modmat, echinacea,
+      from = "beta", to = "mu", offset = offset)
>  all.equal(mu, mu.foo)
[1] TRUE
> 
>  #### unconditional from == "beta" (differential)
> 
>  dphi.foo <- transformConditional(beta, modmat, echinacea,
+      from = "beta", to = "phi", offset = offset, differential = dbeta)
>  all.equal(dphi, dphi.foo)
[1] TRUE
> 
>  dtheta.foo <- transformConditional(beta, modmat, echinacea,
+      from = "beta", to = "theta", offset = offset, differential = dbeta)
>  all.equal(dtheta, dtheta.foo)
[1] TRUE
> 
>  dxi.foo <- transformConditional(beta, modmat, echinacea,
+      from = "beta", to = "xi", offset = offset, differential = dbeta)
>  all.equal(dxi, dxi.foo)
[1] TRUE
> 
>  dmu.foo <- transformConditional(beta, modmat, echinacea,
+      from = "beta", to = "mu", offset = offset, differential = dbeta)
>  all.equal(dmu, dmu.foo)
[1] TRUE
> 
> 
> proc.time()
   user  system elapsed 
   1.35    0.23    1.57