boostrap VS adcomp

I've got something very similar! https://github.com/toasterrepairman/boostrap/tree/main/nix

It's also interesting to hear comments from the Nix detractors here. There's ample room to criticize the language and even the design philosophy behind the package manager, but it's a shame that most people never give it a "proper shot". Maybe that's the fault of Nix maintainers though.

Nvidia works excellent in my experience, Nix provides the best Wayland/Nvidia experience I've had out of any distro. I'm using a 3070ti, but you should be able to steal most of my configuration if you want a similar setup: https://github.com/toasterrepairman/boostrap/blob/main/nix/n...

Are any of your R users TMB users? Because I ran into that in multiple setups (Guix SD and Guix on foreign distros) where only a specific ordering worked even when using guix shell --pure. This was back in the R 4.1.2 days. In theory it should be impossible and yet I ran into it. No I was not mixing packages installed via. install.packages.

Right now with the latest version of Guix and R 4.2.1 TMB is not usable. Try running:

"guix shell --container r r-tmb make gcc-toolchain gfortran-toolchain"

then try running the linreg.R (with the corresponding cpp file, or any of the examples) example from https://github.com/kaskr/adcomp/tree/master/tmb_examples

There is no free lunch:).

I remember spending a summer using Template Model Builder (TMB), which is a useful R/C++ automatic differentiation (AD) framework, for working with accelerated failure time models. For these models, the survival to time T given covariates X is defined by S(t|X) = P(T>t|X) = S_0(t exp(-beta^T X)) for baseline survival S_0(t). I wanted to use splines for the baseline survival and then use AD for gradients and random effects. Unfortunately, after implementing the splines in template C++, I found a web page entitled "Things you should NOT do in TMB" (https://github.com/kaskr/adcomp/wiki/Things-you-should-NOT-d...) - which included using if statements that are based on coefficients. In this case, the splines for S_0 depend on beta, which is this specific excluded case:(. An older framework (ADMB) did not have this constraint, but dissemination of code was more difficult. Finally, PyTorch did not have an implementation of B-splines or an implementation for Laplace's approximation. Returning to my opening comment, there is no free lunch.