version 0.1.1

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: MLZ
 Title: Mean Length-Based Estimators of Mortality using TMB
-Version: 0.1.0
+Version: 0.1.1
 Authors@R: c(person("Quang", "Huynh", email = "q.huynh@oceans.ubc.ca", role = c("aut", "cre")),
   person("Todd", "Gedamke", rol = "ctb"), person("Amy", "Then", rol = "ctb"))
 Maintainer: Quang Huynh <q.huynh@oceans.ubc.ca>
@@ -16,9 +16,9 @@ Suggests: knitr, rmarkdown
 VignetteBuilder: knitr
 LinkingTo: TMB, RcppEigen
 NeedsCompilation: yes
-Packaged: 2018-05-25 17:21:55 UTC; Quang Huynh
+Packaged: 2018-06-11 20:13:46 UTC; qhuynh
 Author: Quang Huynh [aut, cre],
   Todd Gedamke [ctb],
   Amy Then [ctb]
 Repository: CRAN
-Date/Publication: 2018-05-27 16:34:47 UTC
+Date/Publication: 2018-06-12 11:29:34 UTC

MD5

@@ -1,14 +1,14 @@
-a2bfbd5da3f81d1c4bed190d5a9f9d47 *DESCRIPTION
+db88f280098c9b516e15c78710e67ae3 *DESCRIPTION
 bd7b960b2ee2342aaec4f321f6552744 *NAMESPACE
-737465c1192a238eadf58a9b54d56099 *R/compare_models.R
-5ed4f9241521b338c8e65507203ee6bf *R/data_document.R
-a3eb6ad1d483fa89b6d054c2477201cd *R/data_process.R
-e057c1fb14d9db30ba366b1a4b99017e *R/estimation.R
+40458cbe2cb1a732448375f0f78444e2 *R/compare_models.R
+4a8acb0898d0ecbb620ffd2e6ce1455b *R/data_document.R
+b956432b23ec81da3e35d9651c06715d *R/data_process.R
+b1f60b696693c7dd765f953e6d9e32eb *R/estimation.R
 7bfbc7515b12d01d9865b891d014a7f1 *R/internal.R
 8ebaa83110642299a0f3082a400aa7f6 *R/internal_misc.R
-f0e96c14b186dbfeb2d750258215e1a5 *R/methods.R
-635309fad397a9fff68bdf66b96d6c90 *R/profile.R
-0e9f1c06e6acd9d71ae55055951e9001 *R/sensitivity.R
+777b4d1eec3871790ac32e7361ccbad2 *R/methods.R
+6141e7563204fd91c8295fa763c9b538 *R/profile.R
+912b8c7dccaabefcbb13bc7ec27d4e2c *R/sensitivity.R
 fb7d415b92cf7f3f7133eb7df2acd2fa *README.md
 d0442b41afed0f4264784a5fa2e6b6fd *build/vignette.rds
 6fc1d38997fe7471562ff5c5b6529afa *data/Goosefish.rda
@@ -26,27 +26,27 @@ d465768704f11e6424f4d7cc8b06a848 *inst/include/MSM1S.h
 a6678ea34d3acdd3d5f89cbc27508c8d *inst/include/MSM23.h
 2a529262feeaf80b141a8d710e199dd3 *inst/include/functions.h
 566a5c9c0afe7c37a49265145177ed1c *man/Goosefish.Rd
-829c0c6e77aad5933276d71db6e60d3f *man/ML.Rd
-128eda608ea95ea79749bb49c5076dbb *man/MLCR.Rd
+5453eef399eb01017515755a2c2ee2c7 *man/ML.Rd
+f2304d106b2d81daa040bd011f673759 *man/MLCR.Rd
 51dde9b4e7c80ffb204aa1e85d32fe0b *man/MLZ_data-class.Rd
-1b35dbd1596a27e8fffd273b0becb9dc *man/MLZ_model-class.Rd
-ec4b2e9492457afd7242d9b482c26dbe *man/MLeffort.Rd
-130a035db98552a0a6571844643840e7 *man/MLmulti.Rd
+70762f0b28ac4c3e4a19aad2d33a576c *man/MLZ_model-class.Rd
+4e558fe154d3f83986820e994c21dd7b *man/MLeffort.Rd
+ce0b0d4372ab1e4efb50cd056d7757ca *man/MLmulti.Rd
 457a5417f5668da705683b6a654a790d *man/MuttonSnapper.Rd
 d7eaa40f07c00520e418b12df0c6add8 *man/Nephrops.Rd
 94d8bc6de6b4cdba2cf29378e5ef7a07 *man/PRSnapper.Rd
-6fb93e1a695923594456ed249b6f0e0f *man/SilkSnapper.Rd
-6385bd46be6ffb6078f987019c9aace9 *man/bin_length.Rd
-ac202d9bfa288488c95a4eaa1c27ac4d *man/calc_ML.Rd
-04d17841f4e4551dc098f20ceb2d4cb2 *man/compare_models.Rd
-36db446899f91a56100ef188739f16d4 *man/modal_length.Rd
-efb010b7533f9c3224ac7fe480f78c09 *man/plot-MLZ_data-method.Rd
-76274a033a3512810256cf87f980dec0 *man/plot-MLZ_model-method.Rd
-8955fcd70f7819c418676ab88713b665 *man/profile_ML.Rd
-9d36de24def67ac35214b77c5ae448dc *man/profile_MLCR.Rd
-d66c6044b011e884373666e15b8368b0 *man/profile_MLmulti.Rd
-ca53e6a32a1c268832ea5d76f799c378 *man/sensitivity_Lc.Rd
+cd5fe95478822f9a6529f275e6ccfc4a *man/SilkSnapper.Rd
+361b547958f7cddeb616a1278a99cc4a *man/bin_length.Rd
+b1dc13eda0629e54e1b10039e936add7 *man/calc_ML.Rd
+429ac4199fae7babb434c35dadf4414a *man/compare_models.Rd
+55035f52905f5b11745766bb251b07a5 *man/modal_length.Rd
+5d6ebe4523b7b188956241be014a354b *man/plot-MLZ_data-method.Rd
+234925402657d752b6171cfd21add595 *man/plot-MLZ_model-method.Rd
+289e0f88495c137b3bb1abe3ad6e7c10 *man/profile_ML.Rd
+fa08698f8988f98090f5ce002e154e94 *man/profile_MLCR.Rd
+1f25227fb8469ccb249f34fff791a5ba *man/profile_MLmulti.Rd
+088af9a14c4535b8ec24ab67875a85e1 *man/sensitivity_Lc.Rd
 5633222b73797e32834522c5b935a430 *man/summary-MLZ_data-method.Rd
-a158a78441758dab98d0d4b2d268947f *man/summary-MLZ_model-method.Rd
+ca835f2d5e1e8ff904a81d5c7b3d89e6 *man/summary-MLZ_model-method.Rd
 50496dbd034f885c421cd9f1021d7d9a *src/MLZ.cpp
 d48d29b5eb3a75ebcd5f87b0896a41a0 *vignettes/MLZ.Rmd

R/compare_models.R

@@ -10,6 +10,7 @@
 #' will be used.
 #'
 #' @examples
+#' \dontrun{
 #' data(Goosefish)
 #' goose <- ML(Goosefish, ncp = 0)
 #' goose1 <- ML(Goosefish, ncp = 1)
@@ -24,7 +25,7 @@
 #' msm3 <- MLmulti(PRSnapper, ncp = 1, model = "MSM3")
 #'
 #' compare_models(list(ssm, msm1, msm2, msm3))
-#'
+#' }
 #' @export
 compare_models <- function(MLZ_model.list, figure = TRUE, color = NULL) {
   old_par <- par(no.readonly = TRUE)

R/data_document.R

@@ -77,7 +77,8 @@
 #' Length-Based Mortality Estimator. Marine and Coastal Fisheries.
 #'
 #' @examples
+#' \dontrun{
 #' data(SilkSnapper)
 #' new("MLZ_data", Len_df = SilkSnapper)
-#'
+#' }
 "SilkSnapper"

R/data_process.R

@@ -14,14 +14,15 @@
 #' Length frequency matrix from \code{Len_df} are created by using \code{\link[graphics]{hist}} function.
 #' 
 #' @examples
+#' \dontrun{
 #' data(Nephrops)
 #' modal_length(Nephrops)
 #' 
 #' data(SilkSnapper)
 #' new.dataset <- new("MLZ_data", Year = 1983:2013, Len_df = SilkSnapper)
 #' modal_length(new.dataset)
 #' modal_length(new.dataset, breaks = seq(80, 830, 10))
-#' 
+#' }
 #' @references 
 #' Hordyk, A. Ono, K., Sainsbury, K., Loneragan, N., and Prince, J. 2015. Some explorations of the 
 #' life history ratios to describe length composition, spawning-per-recruit, and the 
@@ -94,10 +95,12 @@ modal_length <- function(MLZ_data, length.slot = c("Len_df", "Len_matrix"), brea
 #' (whether mean lengths are calculated).
 #' @return An object of class \code{\linkS4class{MLZ_data}} to fill slots \code{MeanLength}, \code{ss}.
 #' @examples
+#' \dontrun{
 #' data(Nephrops)
 #' Nephrops <- calc_ML(Nephrops, sample.size = FALSE)
 #' Nephrops@MeanLength
 #' plot(Nephrops)
+#' }
 #' @export
 calc_ML <- function(MLZ_data, length.slot = c("Len_df", "Len_matrix"), sample.size = TRUE) {
   length.slot <- match.arg(length.slot)
@@ -145,12 +148,13 @@ calc_ML <- function(MLZ_data, length.slot = c("Len_df", "Len_matrix"), sample.si
 #' 
 #' @return A list with length bins, years, and frequency matrix.
 #' @examples
+#' \dontrun{
 #' data(SilkSnapper)
 #' Silk.matrix <- bin_length(SilkSnapper, breaks = seq(80, 830, 10))
 #' Silk.matrix <- bin_length(SilkSnapper)
 #' new.dataset <- new("MLZ_data", Year = Silk.matrix$Year, Len_bins = Silk.matrix$Len_bins,
 #' Len_matrix = Silk.matrix$Len_matrix)
-#'  
+#' } 
 #' @export
 bin_length <- function(df, breaks = NULL) {
   df <- as.data.frame(df)

R/estimation.R

@@ -29,11 +29,12 @@
 #' Transactions of the American Fisheries Society 135:476-487.
 #' @return An object of class \code{\linkS4class{MLZ_model}}.
 #' @examples
+#' \dontrun{
 #' data(Goosefish)
-#' ML(Goosefish, ncp = 2)
-#' ML(Goosefish, ncp = 2, start = list(Z = c(0.1, 0.3, 0.5), yearZ = c(1978, 1988)))
-#' ML(Goosefish, ncp = 2, grid.search = TRUE)
-#'
+#' res <- ML(Goosefish, ncp = 2)
+#' res <- ML(Goosefish, ncp = 2, start = list(Z = c(0.1, 0.3, 0.5), yearZ = c(1978, 1988)))
+#' res <- ML(Goosefish, ncp = 2, grid.search = TRUE)
+#' }
 #' @seealso \code{\link{profile_ML}}
 #' @export
 ML <- function(MLZ_data, ncp, start = NULL, grid.search = TRUE, 
@@ -160,9 +161,10 @@ ML <- function(MLZ_data, ncp, start = NULL, grid.search = TRUE,
 #' Otherwise, the starting values for \code{yearZ} evenly divide the time series.
 #'
 #' @examples
+#' \dontrun{
 #' data(MuttonSnapper)
 #' MLCR(MuttonSnapper, ncp = 2, CPUE.type = "WPUE", grid.search = TRUE)
-#'
+#' }
 #' @seealso \code{\link{profile_MLCR}}
 #'
 #' @export
@@ -342,22 +344,24 @@ MLCR <- function(MLZ_data, ncp, CPUE.type = c(NA, "WPUE", "NPUE"), loglikeCPUE =
 #' @seealso \code{\link{profile_MLmulti}}
 #'
 #' @examples
+#' \dontrun{
 #' data(PRSnapper)
-#' MLmulti(PRSnapper, ncp = 0, start = list(Z = matrix(0.5, nrow = 1, ncol = 3)))
-#' MLmulti(PRSnapper, ncp = 1, model = "SSM")
+#' res_eq <- MLmulti(PRSnapper, ncp = 0, start = list(Z = matrix(0.5, nrow = 1, ncol = 3)))
+#' res_SSM <- MLmulti(PRSnapper, ncp = 1, model = "SSM")
 #'
 #' MSM1.start.Z <- matrix(0.5, nrow = 2, ncol = 3)
 #' MSM1.start.yearZ <- 1990
 #' start.list <- list(Z = MSM1.start.Z, yearZ = MSM1.start.yearZ)
-#' MLmulti(PRSnapper, ncp = 1, model = "MSM1", start = start.list, grid.search = FALSE)
+#' res_MSM1 <- MLmulti(PRSnapper, ncp = 1, model = "MSM1", start = start.list, grid.search = FALSE)
 #'
-#' MLmulti(PRSnapper, ncp = 1, model = "MSM2")
+#' res_MSM2 <- MLmulti(PRSnapper, ncp = 1, model = "MSM2")
 #'
 #' st.Z1 <- rep(0.5, 3)
 #' st.yearZ <- 1990
 #' st.delta <- 1
 #' start.list <- list(Z1 = st.Z1, yearZ = st.yearZ, delta = st.delta)
-#' MLmulti(PRSnapper, ncp = 1, model = "MSM3", start = start.list)
+#' resMSM3 <- MLmulti(PRSnapper, ncp = 1, model = "MSM3", start = start.list)
+#' }
 #' @export
 MLmulti <- function(MLZ.list, ncp, model = c("SSM", "MSM1", "MSM2", "MSM3"), start = NULL,
                     grid.search = TRUE, parallel = ifelse(ncp > 2, TRUE, FALSE), 
@@ -589,10 +593,12 @@ MLmulti <- function(MLZ.list, ncp, model = c("SSM", "MSM1", "MSM2", "MSM3"), sta
 #' @return An object of class \code{\linkS4class{MLZ_model}}.
 #'
 #' @examples
+#' \dontrun{
 #' data(Nephrops)
 #' Nephrops <- calc_ML(Nephrops, sample.size = FALSE)
-#' MLeffort(Nephrops, start = list(q = 0.1, M = 0.2),
-#'          n_age = 24, eff_init = Nephrops@Effort[1])
+#' res <- MLeffort(Nephrops, start = list(q = 0.1, M = 0.2),
+#'                 n_age = 24, eff_init = Nephrops@@Effort[1])
+#' }                
 #' @export
 MLeffort <- function(MLZ_data, start, n_age, estimate.M = TRUE, log.par = FALSE,
                      eff_init = 0, n_season = 1L, obs_season = 1L, timing = 0, figure = TRUE) {

R/methods.R

@@ -65,13 +65,14 @@ MLZ_data <- setClass("MLZ_data", slots = c(Stock = "character", Year = "vector",
 #' @slot length.units Unit of measurement for lengths, i.e. "cm" or "mm".
 #'
 #' @examples
+#' \dontrun{
 #' data(Goosefish)
 #' goose.model <- ML(Goosefish, ncp = 2, grid.search = FALSE, figure = FALSE)
 #' class(goose.model)
 #'
 #' summary(goose.model)
 #' plot(goose.model, residuals = FALSE)
-#'
+#' }
 #' @export
 setClass("MLZ_model", slots = c(Stock = "character", Model = "character", time.series = "data.frame",
                                 estimates = "matrix", negLL = "numeric",
@@ -106,10 +107,11 @@ setMethod("summary", signature(object = "MLZ_data"), function(object) {
 #'
 #' @param object An object of class \code{MLZ_model}.
 #' @examples
+#' \dontrun{
 #' data(Goosefish)
 #' goose.model <- ML(Goosefish, ncp = 2, grid.search = FALSE)
 #' summary(goose.model)
-#'
+#' }
 #' @export
 setMethod("summary", signature(object = "MLZ_model"), function(object)
   list(Stock = object@Stock, Model = object@Model, Estimates = round(object@estimates, 3)))
@@ -128,9 +130,10 @@ setMethod("summary", signature(object = "MLZ_model"), function(object)
 #' @param x An object of class \code{MLZ_data}.
 #' @param ggplot_layer Layers to add to ggplot2 plot.
 #' @examples
+#' \dontrun{
 #' data(Nephrops)
 #' plot(Nephrops)
-#'
+#' }
 #' @export
 setMethod("plot", signature(x = "MLZ_data"), function(x, ggplot_layer = NULL) {
   MLZ_data <- x
@@ -216,9 +219,11 @@ setMethod("plot", signature(x = "MLZ_data"), function(x, ggplot_layer = NULL) {
 #' @param x An object of class \code{MLZ_model}.
 #' @param residuals logical; whether a plot of residuals will also be produced.
 #' @examples
+#' \dontrun{
 #' data(Goosefish)
 #' goose.model <- ML(Goosefish, ncp = 2, grid.search = FALSE, figure = FALSE)
 #' plot(goose.model)
+#' }
 #' @export
 setMethod("plot", signature(x = "MLZ_model"), function(x, residuals = TRUE) {
   MLZ_model <- x

R/profile.R

@@ -15,10 +15,11 @@
 #' \code{ncp = 2}.
 #' @return A matrix of change points with the negative log-likelihood values.
 #' @examples
+#' \dontrun{
 #' data(Goosefish)
 #' profile_ML(Goosefish, ncp = 1)
 #' profile_ML(Goosefish, ncp = 2)
-#' 
+#' }
 #' @references 
 #' Gedamke, T. and Hoenig, J.M. 2006. Estimating mortality from mean length data in
 #' nonequilibrium situations, with application to the assessment of goosefish.
@@ -148,8 +149,10 @@ profile_ML <- function(MLZ_data, ncp, startZ = rep(0.5, ncp+1), min.time = 3,
 #' to a Nonequilibrium Length-Based Mortality Estimator. Marine and Coastal Fisheries 9:68-78.
 #'
 #' @examples
+#' \dontrun{
 #' data(MuttonSnapper)
 #' profile_MLCR(MuttonSnapper, ncp = 1, CPUE.type = 'WPUE')
+#' }
 #' @export
 profile_MLCR <- function(MLZ_data, ncp, CPUE.type = c(NA, "NPUE", "WPUE"), 
                          loglikeCPUE = c("normal", "lognormal"), startZ = rep(0.5, ncp+1), min.time = 3, 
@@ -277,9 +280,10 @@ profile_MLCR <- function(MLZ_data, ncp, CPUE.type = c(NA, "NPUE", "WPUE"),
 #' to a Nonequilibrium Length-Based Mortality Estimator. Marine and Coastal Fisheries 9:68-78.
 #'
 #' @examples
+#' \dontrun{
 #' data(PRSnapper)
 #' profile_MLmulti(PRSnapper, ncp = 1, model = "MSM1")
-#'
+#' }
 #' @export
 profile_MLmulti <- function(MLZ.list, ncp, model = c("SSM", "MSM1", "MSM2", "MSM3"), 
                             startZ1 = rep(0.5, length(MLZ.list)), 

R/sensitivity.R

@@ -17,6 +17,7 @@
 #' to a Nonequilibrium Length-Based Mortality Estimator. Marine and Coastal Fisheries 9:68-78.
 #'
 #' @examples
+#' \dontrun{
 #' data(SilkSnapper)
 #' new.dataset <- new("MLZ_data", Year = 1983:2013, Len_df = SilkSnapper, length.units = "mm",
 #' vbLinf = 794, vbK = 0.1)
@@ -28,7 +29,7 @@
 #' Lc.vec <- seq(240, 340, 5)
 #' 
 #' sensitivity_Lc(new.dataset, first.MLZmodel, Lc.vec)
-#' 
+#' }
 #' @seealso \code{\link{ML}}
 #' @export
 sensitivity_Lc <- function(MLZ_data, MLZ_model, Lc.vec, grid.search = FALSE, figure = TRUE) {