You first need to uninstall the current installation of ssdtools and then install the dev. version.

Installation Instructions

Remove current version

detach("package:ssdtools", unload = TRUE)
remove.packages("ssdtools")

Install development version

remotes::install_github("bcgov/ssdtools@v1.0.6.9009")

After executing the remotes::install_github("bcgov/ssdtools@v1.0.6.9009") command you will probably get a message such as:

These packages have more recent versions available.
It is recommended to update all of them.
Which would you like to update?

 1: All                                        
 2: CRAN packages only                         
 3: None                                       
 4: Rcpp        (1.0.11    -> 1.0.12   ) [CRAN]
 5: ....
 6: ....
 
 Enter one or more numbers, or an empty line to skip updates:

I suggest you choose Option#1 (All).

Let the process complete - there’s a lot of output (computer jargon) and it might take a while. The process may well terminate with errors.

For example:

Error in TMB::compile(file = paste0(tmb_name, ".cpp"), PKG_CXXFLAGS = tmb_flags,  : 
  Compilation failed
  .
  .
  .
Please re-install 'TMB' from source using install.packages('TMB', type = 'source')

In this example, the installation failed because of a problem with the existing TMB package installation. Follow the suggestion and re-install TMB: install.packages('TMB', type = 'source')

Then try the ssdtools installation again remotes::install_github("bcgov/ssdtools@v1.0.6.9009")

Example

The syntax of some of the ssdtools commands has changed so I recommend consulting the help - particularly for the command ssd_hc().

We’ll use the csiro nickel data set found in the ssddata package.

library(ssdtools)
library(ssddata)
head(csiro_nickel_fresh)  #  look at first 6 rows of the data
## # A tibble: 6 × 6
##    Conc Domain    Group                   Notes   Species          Test_endpoint
##   <dbl> <chr>     <chr>                   <chr>   <chr>            <chr>        
## 1  1.4  temperate Mollusc (snail)         none    Lymnaea stagnal… EC10         
## 2  1.52 temperate Crustacean (water flea) geomean Ceriodaphnia du… EC10/LOEC    
## 3  3.63 temperate Crustacean (water flea) geomean Alona affinis    LC50         
## 4  8.49 temperate Crustacean (water flea) geomean Ceriodaphnia qu… EC10         
## 5 11.0  temperate Crustacean (water flea) geomean Peracantha trun… EC10         
## 6 12    temperate Crustacean (water flea) geomean Daphnia magna    EC10
# We'll fits the default ANZECC set of distributions
fit<-ssd_fit_bcanz(data=csiro_nickel_fresh)
summary(fit)
## Distribution 'gamma'
##   scale 59.4824
##   shape 0.786083
## 
## Distribution 'lgumbel'
##   locationlog 2.43819
##   scalelog 1.28726
## 
## Distribution 'llogis'
##   locationlog 3.09974
##   scalelog 0.730068
## 
## Distribution 'lnorm'
##   meanlog 3.08823
##   sdlog 1.2833
## 
## Distribution 'lnorm_lnorm'
##   meanlog1 0.377467
##   meanlog2 3.2692
##   pmix 0.0625861
##   sdlog1 0.0411175
##   sdlog2 1.11059
## 
## Distribution 'weibull'
##   scale 41.4129
##   shape 0.822934
## 
## Parameters estimated from 31 rows of data.
# examine the goodness-of-fit statistics
ssd_gof(fit)
## # A tibble: 6 × 9
##   dist           ad     ks    cvm   aic  aicc   bic delta weight
##   <chr>       <dbl>  <dbl>  <dbl> <dbl> <dbl> <dbl> <dbl>  <dbl>
## 1 gamma       0.633 0.136  0.113   303.  304.  306.  4.18  0.04 
## 2 lgumbel     0.691 0.136  0.0964  304.  305.  307.  5.53  0.021
## 3 llogis      0.145 0.0678 0.0178  299.  300.  302.  0.55  0.247
## 4 lnorm       0.173 0.0854 0.0211  299.  299.  302.  0     0.326
## 5 lnorm_lnorm 0.175 0.0803 0.0276  297.  300.  304.  0.19  0.296
## 6 weibull     0.448 0.114  0.0720  302.  302.  305.  3.06  0.07
autoplot(fit)

# get HC values and confidence intervals. Use 1,000 bootstrap samples
ssd_hc(fit,ci=TRUE,multi_est = TRUE,multi_ci=TRUE,percent=c(1,5,10,20),nboot=1000)
## Warning: One or more pboot values less than 0.99 (decrease min_pboot with
## caution).
## # A tibble: 4 × 11
##   dist    percent   est    se   lcl   ucl    wt method     nboot pboot samples  
##   <chr>     <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <chr>      <dbl> <dbl> <I<list>>
## 1 average       1 0.899    NA    NA    NA     1 parametric  1000 0.886 <dbl [0]>
## 2 average       5 2.03     NA    NA    NA     1 parametric  1000 0.886 <dbl [0]>
## 3 average      10 4.02     NA    NA    NA     1 parametric  1000 0.886 <dbl [0]>
## 4 average      20 7.72     NA    NA    NA     1 parametric  1000 0.886 <dbl [0]>