Changes in version 0.0.1.9010                      

  - Add chunk facilites zaro_chunk(), zaro_chunks(), zaro_chunk_apply(),
    zarr_chunk_info().

  - Fix problem with Arrow decompression (logic was incorrect and hidden
    in a try/catch).

  - Checks now in place for source strings.

  - Optional validation by reading metadata on connect with zaro() (off
    by default).

  - Use parallel read with mirai settings for zaro_read.

  - Add helpers for common cases.

  - Improved print for meta group.

  - Fixed egregious error on how to use VSIFile hidden within
    tryCatch().

  - Add C style orientation (still upside down for ximage).

Core architecture

  - Arrow-backed Zarr reader for R. Implements the Zarr specification
    directly using Arrow C++ for filesystem access, compression codecs,
    and byte buffers. No dependency on zarr-python or any Zarr-specific
    C/Rust library.

  - S7 class ZaroMeta for parsed array and group metadata, with a
    uniform representation across Zarr V2 and V3.

  - Store abstraction layer with ArrowStore (local, S3, GCS via Arrow
    FileSystem), VSIStore (HTTP and fallback S3 via gdalraster),
    ReferenceStore (Kerchunk JSON references), and VirtualiZarrStore
    (Parquet manifest reference stores). Designed for future backends
    (Icechunk) without changing the read pipeline.

  - Codec pipeline normalised across V2 and V3. V2's single compressor
    plus filters list is mapped to V3-style codec chains so the decode
    path is uniform.

Zarr V2 support

  - Parse .zarray, .zgroup, and .zattrs metadata.

  - Consolidated metadata via .zmetadata — a single HTTP request
    retrieves metadata for all arrays in a V2 store.

  - Numpy dtype string parsing (<f4, >i2, |u1, etc.) with endianness
    detection.

  - _ARRAY_DIMENSIONS convention for dimension names (xarray
    compatibility).

  - dimension_separator handling (dot-separated and slash-separated
    chunk keys).

  - fill_value coercion from JSON strings ("NaN", "Infinity") to R
    numeric values.

  - sanitize_json() handles Python-flavoured JSON with bare NaN,
    Infinity, and -Infinity values that are invalid in strict JSON.

Zarr V3 support

  - Parse zarr.json metadata including codecs, dimension names, fill
    values, and chunk grid configuration.

  - Consolidated V3 metadata (single zarr.json with embedded node
    metadata).

VirtualiZarr Parquet reference stores

  - New VirtualiZarrStore backend for directory-layout Parquet reference
    stores as produced by VirtualiZarr. These provide Zarr-style access
    to existing archival files (NetCDF, HDF5, GRIB) without
    reformatting.

  - Metadata layer (.zmetadata) fetched via curl — GDAL-free.

  - Chunk manifests (refs.N.parq shards) loaded via
    arrow::read_parquet() with lazy per-variable loading and
    environment-based caching.

  - C-order linearization maps chunk grid indices to manifest row
    positions for stores without explicit chunk key columns.

  - Inline data support via the raw column in manifests (used by
    coordinate variables whose data is stored directly in the Parquet
    rather than as byte-range references).

  - Byte-range reads into source files via byte_range_read() supporting
    Arrow S3/GCS, gdalraster VSI, and curl for HTTP — the full chain is
    GDAL-free when using curl.

  - URI scheme: virtualizarr://https://example.com/dataset.parq or
    virtualizarr:///local/path/dataset.parq.

  - Tested against BRAN2023 ocean temperature (5479 time steps, 51 depth
    levels, 1500x3600 spatial grid, 104 manifest shards, NCI THREDDS
    byte-range serving).

Cloud access

  - S3 stores via Arrow S3FileSystem with automatic fallback to
    gdalraster /vsis3/ on 301 region redirects.

  - Anonymous access propagation (anonymous = TRUE maps to
    AWS_NO_SIGN_REQUEST for both Arrow and GDAL backends).

  - GCS stores via Arrow GcsFileSystem.

  - HTTP/HTTPS stores via gdalraster /vsicurl/.

Reading

  - Hyperslab reads with start/count indexing (0-based, following Zarr
    convention).

  - Chunk coverage computation and assembly across chunk boundaries.

  - Blosc (lz4, zstd, zlib, snappy), gzip, and zstd decompression via
    Arrow codecs.

  - Safety coercion of output array type when fill value would otherwise
    produce a character array.

Diagnostics

  - Verbose messaging throughout ([zaro] ...), on by default for
    discoverability. Silence with verbose = FALSE or suppressMessages().

  - Reports store backend, metadata format detected,
    dtype/shape/chunks/codecs, dimension names, chunk counts during
    reads.

Tested against

  - AODN cloud-optimised Zarr stores (S3, ap-southeast-2, Zarr V2,
    blosc/lz4 compression, consolidated .zmetadata).

  - BRAN2023 ocean temperature via VirtualiZarr Parquet references (NCI
    THREDDS byte-range reads, shuffle+zlib compression, int16 with
    scale/offset packing).