Package: h5lite 2.2.0.0

Daniel P. Smith

h5lite: Simplified 'HDF5' Interface

A user-friendly interface for the Hierarchical Data Format 5 ('HDF5') library designed to "just work." It bundles the necessary system libraries to ensure easy installation on all platforms. Features smart defaults that automatically map R objects (vectors, matrices, data frames) to efficient 'HDF5' types, removing the need to manage low-level details like dataspaces or property lists. Uses the 'HDF5' library developed by The HDF Group <https://www.hdfgroup.org/>.

Authors:Daniel P. Smith [aut, cre], Alkek Center for Metagenomics and Microbiome Research [cph, fnd]

h5lite_2.2.0.0.tar.gz
h5lite_2.2.0.0.zip(r-4.7-x86_64)h5lite_2.2.0.0.zip(r-4.7-arm64)h5lite_2.2.0.0.zip(r-4.6-x86_64)h5lite_2.2.0.0.zip(r-4.6-arm64)
h5lite_2.2.0.0.tar.gz(r-4.7-arm64)h5lite_2.2.0.0.tar.gz(r-4.7-x86_64)h5lite_2.2.0.0.tar.gz(r-4.6-arm64)h5lite_2.2.0.0.tar.gz(r-4.6-x86_64)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
h5lite/json (API)

# Install 'h5lite' in R:
install.packages('h5lite', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/cmmr/h5lite/issues

Pkgdown/docs site:https://cmmr.github.io

On CRAN:

Conda:

4.76 score 26 scripts 361 downloads 21 exports 1 dependencies

Last updated from:755a95dffb. Checks:9 OK, 1 FAIL. Indexed: no.

TargetResultTimeFilesSyslog
linux-devel-arm64OK91
linux-devel-x86_64OK122
source / vignettesOK196
linux-release-arm64OK91
linux-release-x86_64OK120
windows-devel-arm64OK218
windows-devel-x86_64OK111
windows-release-arm64OK219
windows-release-x86_64OK106
wasm-releaseFAIL102

Exports:h5_attr_namesh5_classh5_compressionh5_create_fileh5_create_grouph5_deleteh5_dimh5_existsh5_inspecth5_is_dataseth5_is_grouph5_lengthh5_lsh5_moveh5_namesh5_openh5_readh5_strh5_threadsh5_typeofh5_write

Dependencies:hdf5lib

Atomic Vectors
Basic Usage | Scalars vs. 1D Arrays | Numeric and Logical Data | Automatic Type Selection | Handling Missing Values (NA) | Forcing Specific Types | Character Vectors (Strings) | Variable-Length | Fixed-Length | Compression | 64-bit Integers
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Last update: 2026-10-31
Started: 2026-01-29

Compression
Compression by Default | What Gets Compressed | Choosing a Codec | Gzip | Zstandard and LZ4 | Blosc2 and Bitshuffle | Szip | Legacy Codecs | Comparing Codecs on Your Own Data | Sharing Files with Other Software | Lossy Compression | ZFP | Scale-Offset: int_packing and float_rounding | Blosc2 Truncate Precision | The Filter Pipeline | Shuffling | Blosc2 Delta | Checksums | Rules and Automatic Adjustments | Chunking | Inspecting the Results | Compression String Reference
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Data Frames
Basic Usage | Customizing Column Types | Row Names | Compression and Partial Reading
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Data Types
Supported Data Types | Dimensions: Scalars, Vectors, and Arrays | Numeric Data | 64-bit Integers (integer64) | Double (Numeric) Data | Logical Data | Character Data | Variable-Length: | Fixed-Length: | Dates and Times (POSIXt) | Complex Data | Raw Data | Factors | Lists | Data Frames | NULL | Data Types and Compression
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Get Started with h5lite
HDF5 for R Users | Basic Usage | Writing Numeric Data | Reading Data | Data Type Mapping | Complex Data Structures | Lists as Groups | Data Frames as Compound Datasets | Attributes | File Inspection | Compression | Reading Part of a Dataset | Learn More
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Matrices and Arrays
Writing Matrices | Writing N-Dimensional Arrays | Dimension Names (dimnames) | Dimension Ordering (Row-Major vs. Column-Major) | How h5lite handles it | Interoperability with Python | Chunking and Compression | Partial Reading
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Partial Reading
Why Partial Reading Matters | The HDF5 Storage Model: Chunking and Compression | Designing for Partial Reading | Memory Efficiency of start and count | The "Smart" start Parameter | Single-Value Examples | Dimension Simplification (Exact vs. Range Indexing) | Drilling Down: Multi-Value start and N-Dimensional Arrays
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Organization
The Hierarchical Model | Creating Groups | Implicit Creation (Recommended) | Explicit Creation | Using Lists as Groups | Inspecting Structure | Moving and Renaming | Deleting Objects
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Attributes In-Depth
Writing Attributes | 1. Explicit Writing | 2. Implicit Writing (R Attributes) | Reading Attributes | 1. Accessing Specific Attributes | 2. Reading with the Dataset | Managing Attributes | Listing Attributes | Deleting Attributes | Important Limitations | 1. No Dimension Scales (Loss of Names) | 2. No Attributes on Attributes (Nesting) | Controlling Attribute Types | Customizing Storage Type | Customizing Read Type | Special Note: Dimensions
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Object-Oriented Interface
Basic Usage | Navigation and Working Directories | Reference Semantics | Closing the Handle | Method Reference
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Parallel Processing
The Safety Rule: Always Lock | Recommended Strategy: File Locking with flock
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