R (>= 2.4.0), methods, filehash, DBI, RSQLite
Simple key-value database using SQLite as the backend

R (>= 2.4.1), methods, filehash (>= 1.0), DBI, RSQLite

R (>= 2.9.0), methods, filehash (>= 1.0), DBI, RSQLite

R (>= 2.14.0), methods, filehash (>= 1.0), DBI, RSQLite

Simple key-value database using SQLite as the back end.

R (>= 2.4.1), methods, filehash (>= 0.9), DBI, RSQLite

Place an exclusive or shared lock on a file. It uses 'LockFile'
on Windows and 'fcntl' locks on Unix-like systems.

File-Backed Matrix Class with Convenient Read and Write Access
Interface for working with large matrices stored in files, not in computer memory.
Supports multiple data types (double, integer, logical and raw) of different sizes (e.g. 4, 2, or 1 byte integers).
Access to parts of the matrix is done by indexing, exactly as with usual R matrices.
Supports very large matrices (tested on 1 terabyte matrix), allowing for more than 2^32 rows or columns.
Cross-platform as the package has R code only, no C/C++.

Interface for working with large matrices stored in files,
not in computer memory. Supports multiple non-character
data types (double, integer, logical and raw) of
various sizes (e.g. 8 and 4 byte real values).
Access to parts of the matrix is done by indexing,
exactly as with usual R matrices.
Supports very large matrices.
Tested on multi-terabyte matrices.
Allows for more than 2^32 rows or columns.
Allows for quick addition of extra columns to a filematrix.
Cross-platform as the package has R code only.

Create descriptive file names with ease. New file names are
automatically (but optionally) time stamped and placed in date stamped
directories. Streamline your analysis pipeline with input and output file
names that have informative tags and proper file extensions.

Chunk Processing or Split-Apply-Combine on Delimited Files(CSV
Etc)
Perform chunk processing or split-apply-combine on data in a
delimited file(example: CSV) across multiple cores of a single machine with
low memory footprint. These functions are a convenient wrapper over the
versatile package 'datadr'.
person("KS", "Srikanth", email = "sri.teach@gmail.com",
role = c("aut", "cre"))
datadr (>= 0.8.6), assertthat (>= 0.1), parallel