version 1.1-10

DESCRIPTION

@@ -1,8 +1,8 @@
 Package: actuar
 Type: Package
-Title: Actuarial Functions
-Version: 1.1-9
-Date: 2015-07-07
+Title: Actuarial Functions and Heavy Tailed Distributions
+Version: 1.1-10
+Date: 2015-07-21
 Author: Vincent Goulet, Sébastien Auclair, Christophe Dutang, Xavier Milhaud, Tommy Ouellet, Louis-Philippe Pouliot, Mathieu Pigeon
 Maintainer: Vincent Goulet <[email protected]>
 Description: Various actuarial science functionalities, mostly in the
@@ -18,6 +18,6 @@ Encoding: UTF-8
 LazyLoad: yes
 LazyData: yes
 NeedsCompilation: yes
-Packaged: 2015-07-07 04:39:21 UTC; vincent
+Packaged: 2015-07-21 18:31:27 UTC; vincent
 Repository: CRAN
-Date/Publication: 2015-07-07 17:47:30
+Date/Publication: 2015-07-22 05:39:47

MD5

@@ -1,4 +1,4 @@
-6c98f190abfb9eaf689ba24b22453671 *DESCRIPTION
+ee202b747521d14c0800420ac6225de7 *DESCRIPTION
 f01ac1c1dbf0ba850ba9315e9543424d *NAMESPACE
 2b47500be0644b64b6c9ab14f2356ac3 *R/BetaMoments.R
 6d215e392a1b2b70f856cd1e49f6d99e *R/Burr.R
@@ -33,7 +33,7 @@ feff1a882dfe5ad292b24e7a46c6a031 *R/UniformSupp.R
 6a6c80d4e9625d690d7edf2797539245 *R/adjCoef.R
 89be0e9ee9c02d0cb5556c59242e2872 *R/aggregateDist.R
 6003719eff1166bf3f15a1a549d35d7f *R/bstraub.R
-0e4a3f079778ea08f0a1098131eed38c *R/cm.R
+b439289d45a44970215a98b0c8b379c3 *R/cm.R
 a998c2678650069ddf59ba3de5cb08e7 *R/coverage.R
 c32846ed69862a886e61b7b3541a5d32 *R/discretize.R
 db6e55d7e0892f18b8e1a3a59c349346 *R/elev.R
@@ -43,7 +43,7 @@ ad486fab27687ecdc47a75a88869f35f *R/emm.R
 4f6f6db6a6319a9c7069e4627fe4d285 *R/hache.R
 a7a0d7117b87cd6711d35f067735b625 *R/hache.barycenter.R
 773e8ce24128753da4e171b20f930ba0 *R/hache.origin.R
-e439b2ad83846d7de0a61f3a3653e820 *R/hierarc.R
+0710727f28e9ebcc9e93fb591223ab58 *R/hierarc.R
 edb051fc0253083324a5ec69089bf8ac *R/hist.grouped.data.R
 ceec1a5dbf244493dd8152dc4d9ee7c2 *R/mde.R
 d0a7c15fcdcc673220f8fed86d87a3df *R/mean.grouped.data.R
@@ -68,25 +68,25 @@ bba63751759ba972e59c0f726bec5793 *demo/credibility.R
 f14c0b1400f7661fc5c6196ed3cb9fc9 *demo/risk.R
 b80efa324a92b9837bac5dc570703220 *demo/simulation.R
 72cebb6e34e6fc91c3f49e94076e49b3 *inst/CITATION
-eac9e715aded68614c76bf5ae6cceeb6 *inst/NEWS.Rd
+1be3a720a5cb8d727f3ce4bbccc26667 *inst/NEWS.Rd
 e9e230719c1ce2dd97480f868a788a4a *inst/doc/actuar.R
 1e75afa48b6e13b943ee62e8734c6d26 *inst/doc/actuar.Rnw
-4bee2c3275abbe42f1bef5668f92230d *inst/doc/actuar.pdf
+99e60a0ba98b7b20a8d8d83a33c971d4 *inst/doc/actuar.pdf
 1b331713221e1e60f3ed2c76587f91d4 *inst/doc/coverage.R
 78bbb13165709f6fa51a91828f2e47c7 *inst/doc/coverage.Rnw
-3f5f36466d35e832ce87be6c9a2a96be *inst/doc/coverage.pdf
+d56b7d6a8121eb110148c2a12f07d928 *inst/doc/coverage.pdf
 095316e8b92f8ece50442242a605b0d9 *inst/doc/credibility.R
 6a56532d23580a11645794c329bbc6f7 *inst/doc/credibility.Rnw
-caf03d026b2d3f98172006d8a62a5833 *inst/doc/credibility.pdf
+2903cd1c6c31813b60906d6767b266ea *inst/doc/credibility.pdf
 e98c50c714c07c8f99344e672a4fd8ae *inst/doc/lossdist.R
 1ff63cbe4a73147451f7807ebc26d704 *inst/doc/lossdist.Rnw
-09095757912edc6c3e79c445c72d288a *inst/doc/lossdist.pdf
+9003beacb4b6a3606b1224cb0fb80913 *inst/doc/lossdist.pdf
 09ed3972808681d3f39d9d4ca1ef5680 *inst/doc/risk.R
 030c28e042ad6fb45a3b63ec4a26e5ba *inst/doc/risk.Rnw
-f0ccb55fe1b437c6361b595b05e7c4ca *inst/doc/risk.pdf
+b5454a93dd4296842ef33ba4b92d63e8 *inst/doc/risk.pdf
 5262b731901dd15c792ad62c7eaf81a9 *inst/doc/simulation.R
 51441d46d497f553be2a62dc9a92fe6e *inst/doc/simulation.Rnw
-4b6d0db334f4a020b37f2f589026b95c *inst/doc/simulation.pdf
+9c62c737e6f23ced9ccd73fb00d9c61e *inst/doc/simulation.pdf
 a58f13e70d228d4dc1d35fe01bd1df36 *inst/po/fr/LC_MESSAGES/R-actuar.mo
 dd531e3860bcbe7a2f7d747b6f9894f4 *inst/po/fr/LC_MESSAGES/actuar.mo
 4920e65f829fae0596babf70fc453816 *man/BetaMoments.Rd

R/cm.R

@@ -111,7 +111,7 @@ cm <- function(formula, data, ratios, weights, subset,
  ## == DISPATCH TO APPROPRIATE CALCULATION FUNCTION ==
  ##
  ## Buhlmann-Straub models are handled by bstraub(), regression
- ## models by hache() and hierarcahical models by hierarc().
+ ## models by hache() and hierarchical models by hierarc().
  if (nlevels < 2) # one-dimensional model
  {
  ## One-dimensional models accept only "unbiased" and

R/hierarc.R

@@ -16,11 +16,11 @@ hierarc <- function(ratios, weights, classification,
  ## numbers, whereas 'classification' must be a matrix of integers
  ## giving the affiliation of each entity in the portfolio.
  nlevels <- ncol(classification) # number of levels
- nlevels1p <- nlevels + 1  # frequently used
+ nlevels1p <- nlevels + 1L # frequently used
 
  ## To symmetrize further calculations, bind a column of ones
  ## representing the affiliation to the global portfolio.
- classification <- cbind(pf = 1, classification)
+ classification <- cbind(pf = 1L, classification)
 
  ## If weights are not specified, use equal weights.
  if (missing(weights))
@@ -51,20 +51,25 @@ hierarc <- function(ratios, weights, classification,
  ## of nodes at one level [2] according to the number of nodes at
  ## the level below [c(3, 2)].
  ##
+ ## 0. Initialization
+ fnodes <- nnodes <- vector("list", nlevels)
+
  ## 1. Calculation of the number of nodes: the main idea is to
- ## create a unique ID for each node by pasting together the
- ## elements in the rows of 'classification'. This is not required for the
- ## lowest level (the entities), though, since they are known to
- ## all be different.
+ ## create a unique factor for each node using interaction()
+ ## recursively on the columns of 'classification'. We can do
+ ## something simpler for the lowest level (the entities), since we
+ ## know the combinations of indexes to all be different at this
+ ## level.
  fx <- vector("list", nlevels1p)
  fx[[nlevels1p]] <- factor(classification[, nlevels1p]) # entity level
 
- fnodes <- nnodes <- vector("list", nlevels)
-
- for (i in nlevels:1)
+ for (i in nlevels:1L)
  {
- fx[[i]] <- factor(apply(classification[, seq.int(i), drop = FALSE],
- 1, paste, collapse = ""))
+ ## Function 'interaction' expects its arguments separately or
+ ## as a list, hence the lapply() below.
+ fx[[i]] <- as.integer(interaction(lapply(seq.int(i),
+ function(j) classification[, j]),
+ drop = TRUE))
  ## 'as.vector' below is used to get rid of names
  nnodes[[i]] <- as.vector(sapply(split(fx[[i + 1]], fx[[i]]),
  function(x) length(unique(x))))
@@ -85,8 +90,8 @@ hierarc <- function(ratios, weights, classification,
  ## summary made using these factors will be sorted. This done, it
  ## is possible to use the command above for the upper levels.
  fnodes[[nlevels]] <- as.integer(fx[[nlevels]])
- fnodes[-nlevels] <-
- lapply(nnodes[-nlevels], function(x) rep(seq_along(x), x))
+ fnodes[-nlevels] <- lapply(nnodes[-nlevels],
+   function(x) rep(seq_along(x), x))
 
  ## === PER ENTITY SUMMARIES ===
  ##
@@ -113,7 +118,7 @@ hierarc <- function(ratios, weights, classification,
  ## above.
  eff.nnodes <- vector("list", nlevels)
  w <- weights.s
- for (i in nlevels:1)
+ for (i in nlevels:1L)
  {
  eff.nnodes[[i]] <- tapply(w, fnodes[[i]], function(x) sum(x > 0))
  w <- tapply(w, fnodes[[i]], sum) # running totals
@@ -135,12 +140,12 @@ hierarc <- function(ratios, weights, classification,
  ## (effective) number of "sectors" - 1
  ##
  ## The 1 neither is included in 'eff.nnodes'.
- denoms <- diff(c(1, sapply(eff.nnodes, sum), sum(!is.na(ratios))))
+ denoms <- diff(c(1L, sapply(eff.nnodes, sum), sum(!is.na(ratios))))
 
  ## Final sanity checks
  if (any(!denoms))
  stop("there must be at least two nodes at every level")
- if (ncol(ratios) < 2)
+ if (ncol(ratios) < 2L)
  stop("there must be at least one node with more than one period of experience")
 
  ## === ESTIMATION OF s^2 ===
@@ -189,33 +194,33 @@ hierarc <- function(ratios, weights, classification,
  else # Ohlsson
  bexp <- expression(b[i] <- sum(bi, na.rm = TRUE) / sum(ci, na.rm = TRUE))
 
- for (i in nlevels:1)
+ for (i in nlevels:1L)
  {
  ## Total weight of the level as per the rule above.
- tweights[[i]] <- as.vector(tapply(tweights[[i + 1]], fnodes[[i]], sum))
+ tweights[[i]] <- as.vector(tapply(tweights[[i + 1L]], fnodes[[i]], sum))
 
  ## Calculation of the weighted averages of the level. Before
  ## the between variance is estimated, these use the total
  ## weights calculated above.
  wmeans[[i]] <-
  ifelse(tweights[[i]] > 0,
- as.vector(tapply(tweights[[i + 1]] * wmeans[[i + 1]],
+ as.vector(tapply(tweights[[i + 1L]] * wmeans[[i + 1L]],
  fnodes[[i]],
  sum) / tweights[[i]]),
  0)
 
  ## Latest non-zero between variance estimate -- the one used
  ## in the estimator and in the credibility factors.
- between <- b[b != 0][1]
+ between <- b[b != 0][1L]
 
  ## Calculation of the per node variance estimate.
- bi <- as.vector(tapply(tweights[[i + 1]] *
- (wmeans[[i + 1]] - wmeans[[i]][fnodes[[i]]])^2,
+ bi <- as.vector(tapply(tweights[[i + 1L]] *
+ (wmeans[[i + 1L]] - wmeans[[i]][fnodes[[i]]])^2,
  fnodes[[i]],
  sum)) -
  (eff.nnodes[[i]] - 1) * between
  ci <- tweights[[i]] -
- as.vector(tapply(tweights[[i + 1]]^2, fnodes[[i]], sum)) / tweights[[i]]
+ as.vector(tapply(tweights[[i + 1L]]^2, fnodes[[i]], sum)) / tweights[[i]]
 
  ## The final estimate is the average of all the per node estimates.
  eval(bexp)
@@ -227,11 +232,11 @@ hierarc <- function(ratios, weights, classification,
  #if (max(bu[i], 0)) # don't compute negative factors!
  if (b[i])
  {
- cred[[i]] <- 1/(1 + between/(b[i] * tweights[[i + 1]]))
+ cred[[i]] <- 1/(1 + between/(b[i] * tweights[[i + 1L]]))
  tweights[[i]] <- as.vector(tapply(cred[[i]], fnodes[[i]], sum))
  wmeans[[i]] <-
  ifelse(tweights[[i]] > 0,
- as.vector(tapply(cred[[i]] * wmeans[[i + 1]],
+ as.vector(tapply(cred[[i]] * wmeans[[i + 1L]],
  fnodes[[i]],
  sum) / tweights[[i]]),
  0)
@@ -254,8 +259,8 @@ hierarc <- function(ratios, weights, classification,
  ## the current level.
  if (method == "iterative")
  {
- b <- pmax(b, 0) # truncation for starting values
- if (any(head(b, -1) > 0)) # at least one non-zero starting value
+ b <- pmax(b, 0)  # truncation for starting values
+ if (any(head(b, -1L) > 0)) # at least one non-zero starting value
  .External("actuar_do_hierarc", cred, tweights, wmeans, fnodes, denoms,
  b, tol, maxit, echo)
  }
@@ -267,7 +272,7 @@ hierarc <- function(ratios, weights, classification,
  iterative = if (method == "iterative") b,
  cred = cred,
  nodes = nnodes,
- classification = classification[, -1],
+ classification = classification[, -1L],
  ordering = fnodes),
  class = "hierarc",
  model = "hierarchical")
@@ -298,7 +303,7 @@ predict.hierarc <- function(object, levels = NULL, newdata, ...)
  res <- vector("list", n)
 
  ## First level credibility premiums
- res[[1]] <- means[[1]] + cred[[1]] * (means[[2]] - means[[1]])
+ res[[1L]] <- means[[1L]] + cred[[1L]] * (means[[2L]] - means[[1L]])
 
  for (i in seq(2, length.out = n - 1))
  {

inst/NEWS.Rd

@@ -2,11 +2,22 @@
 \title{actuar News}
 \encoding{UTF-8}
 
+\section{CHANGES IN VERSION 1.1-10}{
+ \subsection{BUG FIX}{
+ \itemize{
+ \item Results of 'cm' for hierarchical models would get
+ incorrectly sorted when there were 10 nodes or more at a given
+ level. Thanks to Dylan Wienke \email{dwienke2@gmail.com} for the
+ catch.
+ }
+ }
+}
+
 \section{CHANGES IN VERSION 1.1-9}{
  \subsection{MAINTENANCE}{
  \itemize{
  \item Functions 'head' and 'tail' explicitly imported from package
- utils in NAMESPACE to obey a new requirement of R 3.3.x.
+ utils in NAMESPACE as per a new requirement of R 3.3.x.
  }
  }
 }
@@ -26,7 +37,7 @@
  \subsection{BUG FIX}{
  \itemize{
  \item panjer() result was wrong for the "logarithmic" type of
- frequency distribution. Thanks to <mmclaramunt@ub.edu> for the
+ frequency distribution. Thanks to \email{mmclaramunt@ub.edu} for the
  catch.
  }
  }
@@ -75,7 +86,7 @@
  GeneralizedHyperbolic on Solaris.
  \item Wrong result given by levinvGauss() because the upper tail
  of the normal distribution was used in the calculation instead of
- the lower tail. Thanks to Dan Murphy <chiefmurphy@gmail.com> for
+ the lower tail. Thanks to Dan Murphy \email{chiefmurphy@gmail.com} for
  the heads up.
  }
  }