anvl 0.2.0

Breaking Changes

• The package was renamed from anvil to anvl to avoid a conflict
  with the Bioconductor package AnVIL.
• AnvilTensor/nv_tensor were renamed to AnvlArray and nv_array to be
  more in line with R's array().
  Also, nv_aten() was renamed to nv_aval().
• Subsetting with list() (e.g. x[list(1, 3)]) is no longer supported.
  Use array() to wrap the indices instead, e.g. x[array(c(1L, 3L))].
  This mirrors the input convention used everywhere else in the package.
• Removed debug mode.
• Remove NSE support for nvl_if. It now requires passing 0-argument
  closures as true and false arguments.
• Primitives renamed from nvl_* to prim_*.
  The underlying primitive object containing the rules and metadata
  is now part of the JitPrimitive function via the primitive attribute.

New Features

• Better composability:
  jit()ted functions can now be used in other jit()-calls.
  This is the mechanism underlying the new eager mode.
• Eager mode was added:
  This means, you can now do nv_add(1, nv_array(1:2)) and it will
  actually perform the computation and not only do type inference.
• An experimental {quickr} backend was added
  It only runs on CPU for now and supports a subset of available operations.
  You can enable it via the backend argument in jit() and
  nv_array() or via the anvl.default_backend option.
• New primitives:
  • nvl_cholesky() to compute the Cholesky decomposition of a matrix.
  • nvl_triangular_solve() to solve a system of linear equations with a triangular matrix.
• New API functions (+ corresponding R generic implementations):
  • nv_diag() to create a diagonal matrix from a 1-D tensor.
  • nv_eye() to create an identity matrix.
  • nv_solve() to solve a system of linear equations.
  • nv_cholesky() to compute the Cholesky decomposition of a matrix.
  • nv_device() constructs a backend-specific device object (e.g. nv_device("cpu"))
    that can be passed as device to array constructors like nv_fill() or nv_iota().
  • nv_crossprod() and nv_tcrossprod() for matrix cross-products.
  • nv_outer() for the outer product.
  • nv_extract_diag() to extract the diagonal of a matrix.
  • nv_trace() to compute the trace of a matrix.
  • nv_tril() and nv_triu() to extract lower/upper triangular parts.
  • nv_squeeze() and nv_unsqueeze() to drop or add length-1 dimensions.
  • nv_log2() and nv_log10().
  • nv_is_infinite() and nv_is_nan().
  • nv_sd() and nv_var() for standard deviation and variance.
• jit() now accepts integer positions for the static argument.
• New S3 methods dim(), nrow(), ncol(), and length() for anvl arrays.
• Printing tensors via nv_print() now also works on GPUs.
• R vectors of length 1 and arrays are now auto-converted when being passed
  to jitted functions.
• Improved device handling in jit()

Performance

• Many operations are now done asynchronously, which improves performance,
  especially on GPUs.

Bug Fixes

• +-Inf/NaN are correctly created for f64 when inlined into the XLA exectuable (#182).
  This caused wrong results with e.g. nv_reduce_max() when working with f64.
• Corrected argument checks in nv_iota().
• Fix check that wrt arguments in gradient() must be floats.
• nv_subset() and nv_subset_assign() now error on trailing-comma subscripts (#273).

Documentation

• New vignette on implementing Gaussian Processes.
• New vignette on implementing Metropolis-Hastings sampling.

Platform support and installation

• An installation guide was added.
• Linux on ARM is now supported (CPU only).
• To use the CUDA backend, it is now possible to install the cuda12.8
  package (see installation guide), which only requires a compatible CUDA
  driver.