I am getting the following message when trying to install the gcamland using the code specified. can anyone help at the soonest possible,

> devtools::install_github("jgcri/gcamland")
Downloading GitHub repo jgcri/gcamland@HEAD
v  checking for file 'C:\Users\Rajiv\AppData\Local\Temp\RtmpGEKU0u\remotes47841db127af\JGCRI-gcamland-be3d382/DESCRIPTION' ...
-  preparing 'gcamland':
v  checking DESCRIPTION meta-information ... 
-  checking for LF line-endings in source and make files and shell scripts (1.4s)
-  checking for empty or unneeded directories
   Removed empty directory 'gcamland/outputs'
   Removed empty directory 'gcamland/vignettes/outputs'
     NB: this package now depends on R (>= 3.5.0)
     WARNING: Added dependency on R >= 3.5.0 because serialized objects in
     serialize/load version 3 cannot be read in older versions of R.
     File(s) containing such objects:
       'gcamland/tests/testthat/data/bayes-scenario-info.rds'
-  building 'gcamland_1.1.1.tar.gz'
   
Installing package into ‘C:/GCAM/GCAM_V6/input/gcamdata/renv/library/R-4.1/x86_64-w64-mingw32’
(as ‘lib’ is unspecified)
* installing *source* package 'gcamland' ...
** using staged installation
** R
** data
*** moving datasets to lazyload DB
** inst
** byte-compile and prepare package for lazy loading
Error in vec_assign(x = col, i = missing, value = value, x_arg = col_arg,  : 
  Can't convert `replace$ha` <double> to match type of `data$ha` <character>.
Error: unable to load R code in package 'gcamland'
Execution halted
ERROR: lazy loading failed for package 'gcamland'
* removing 'C:/GCAM/GCAM_V6/input/gcamdata/renv/library/R-4.1/x86_64-w64-mingw32/gcamland'
Warning messages:
1: In untar2(tarfile, files, list, exdir, restore_times) :
  skipping pax global extended headers
2: In untar2(tarfile, files, list, exdir, restore_times) :
  skipping pax global extended headers
3: In i.p(...) :
  installation of package ‘C:/Users/Rajiv/AppData/Local/Temp/RtmpGEKU0u/file4784682d6fab/gcamland_1.1.1.tar.gz’ had non-zero exit status

error.txt

Thank you

Allow the model to include "new" land types in future periods

Currently, this model deals with data by reading/writing csv files. Need to exchange data in a better way.

Implement alternate logit option, which will be used in lowest nest

Ensure that we can test different forms of forming expectations about yield and price

Package currently assumes that you read in consistent data (e.g., all of the right years). Need to add checks for this.

Currently, I've hard-coded the relevant data and parameters in a constants file. We need to switch this to read in data, ideally using the files generated by the gcamdata system.

To match behavior in C++ GCAM, we'll need to calculate the cost of fertilizer from inputs and fertilizer prices. Need to add this capability into the model.

We have added a vignette going through the workflow of objective function analysis. We should thoroughly update the manual in gcamland.R, including some of that information, once objective functions are finalized and automated testing in place

Figure out how to output information, possibly graph things too.

AgProductionTechnology should read in tech change and update yields.

Figure out how to read in protected land information.

Write now each variable can only have a single value. We can overwrite it if it is re-calculated, but we'd ideally store data that changes over time as a time series.

Good afternoon,

I just received your submission to the Journal of Open Research Software from Dr. Kate Calvin. When attempting to run the code using the documentation in R, I kept encountering errors. However, using the instructions within paper I just received (page 5, Installing and Running the Package) it ran successfully!

So it looks like the documentation in the R package is a bit behind; I was unable to locate the documentation files to edit them for you, so I figured I should give you a heads up.

Thanks for all of the work you've put into this!

Peter

Implement the UCT option

currently only have automated in testing for the bayesian analysis functions; once settled on what we want the objective function analysis to look like, implement automated testing for that (and adjust the functions if testing reveals issues)

We need to be able to have data-driven nesting structures

The code is currently organized the way that the GCAM C++ code is organized. However, that makes it difficult to find/track the equations and calculations. May be worth restructuring the code to make it easier for users to follow/understand.

Do I need a system to store periods & years?

The model is pretty quick with a single AEZ, but running lots of AEZs slows it down. Need to do some profiling and improvement of performance.

Test to ensure it works

Right now when running samples we round our sample values of linear.years to the nearest integer. In the long run that is going to create some problems for doing inference with these samples. We can (relatively) easily define the model for fractional values by interpreting a fractional number of years as a weighted average of the two surrounding integer values. Thus, linear.years==3.4 is a 40% weighting of a 3-year model averaged with a 60% weighting of a 4-year model. This will allow us to avoid having to do inference with a mixture of discrete and continuous parameters.

Currently, code only exists in LandLeaf, not in UnmanagedLandLeaf.