I'm an renv novice, so apologies if this is an obvious question. When running renv::restore, I get the message * The library is already synchronized with the lockfile.. Yet RStudio tells me that I don't have recipes, tfdatasets and two others. I know that I have recipes installed globally, but it doesn't look as though it appears in the file renv.lock.

Does the lockfile simply need to be updated?

Hello,

I'm working on individual claims reserving and your work is incredible ! Thank you so much for sharing your code.

I came across an error and I can't fix it (is the problem coming from the new version of tfprobabilty or tensorflow or something else ?)
It's related to this lines of code (model.R) :

paid_out <- out_sequence %>%
    layer_dense_variational(units = 4,
                            make_posterior_fn = posterior_mean_field,
                            make_prior_fn = prior,
                            kl_weight = 1 / n_rows,
                            activation = "linear") %>%
    layer_distribution_lambda(
      function(x) {
       

        d <- tfd_mixture(
          cat = tfd_categorical(logits = x[,,1:2]),
          components = list(
            tfd_transformed_distribution(
              tfd_log_normal(x[,,3], 1e-3 + ln_scale_bound * k_sigmoid(scale_c * x[,,4])),
              tfb_affine_scalar(shift = -1e-3)
            ),
            tfd_deterministic(loc = k_zeros_like(x[,,3]))
          )
        )
      },
      name = "paid_out_"
    )

Am getting this error :

Warning message in tfb_affine_scalar(shift = -0.001):
“tfb_affine_scalar() is deprecated, please use tfb_shift(shift)(tfb_scale(scale)) instead”
Error in py_call_impl(callable, dots$args, dots$keywords): RuntimeError: Exception encountered when calling layer "paid_out_" (type DistributionLambda).

Evaluation error: TypeError: got an unexpected keyword argument 'use_static_graph'

Detailed traceback:

File "/miniconda/envs/r-reticulate/lib/python3.8/site-packages/decorator.py", line 231, in fun
args, kw = fix(args, kw, sig)
File "/miniconda/envs/r-reticulate/lib/python3.8/site-packages/decorator.py", line 203, in fix
ba = sig.bind(*args, **kwargs)
File "/miniconda/envs/r-reticulate/lib/python3.8/inspect.py", line 3037, in bind
return self._bind(args, kwargs)
File "/miniconda/envs/r-reticulate/lib/python3.8/inspect.py", line 3026, in _bind
raise TypeError(

Call arguments received:

• inputs=tf.Tensor(shape=(None, 11, 4), dtype=float32)
• args=<class 'inspect._empty'>
• kwargs={'training': 'None'}

Thank you so much !

In requirements.txt, line 14: pkg-resources==0.0.0

I do not believe this to be a valid PyPI package number.

To the original author(s), please ensure that package numbers are valid in order for users to build the project.

I'll try to create a PR to correct this and any other package version issues soon.

Thank you! :D

After addressing the renv issue #14, I'm now having an issue with piggyback::download(). It worked earlier, but I'm not getting the error:

Error in df[update, ] : incorrect number of dimensions
In addition: Warning message:
In get_token() : Using default public GITHUB_TOKEN.
                     Please set your own token

Is the repo missing a .pbattributes file?

could not find function "layer_distribution_lambda"
file model.R
I use keras 2.13.0
tensorflow 2.14.0.9000
trims

@PirateGrunt added you as admin, not sure if that's enough to transfer ownership but worth a shot (so I don't need to obtain unnecessary permissions on the cas org)

I tried to build the repo and received an error, stating that PyPI cannot find the version of opt-einsum at 3.0.0.