The call '(lazy e)' means the same as e, but lazy has a magical strictness property: it is lazy in its first argument, even though its semantics is strict.

Convert a lazy ST computation into a strict one.

The CSV format is defined by RFC 4180. These efficient lazy parsers (String and ByteString variants) can report all CSV formatting errors, whilst also returning all the valid data, so the user can choose whether to continue, to show warnings, or to halt on error. Valid fields retain information about their original location in the input, so a secondary parser from textual fields to typed values can give intelligent error messages.
Version 0.5

The library provides some basic but useful lazy IO functions. Keep in mind that lazy IO is generally discouraged. Perhaps a coroutine library (e.g. pipes) will better suit your needs.
Version 0.1.0

This package built on standard array package adds support for lazy monolithic arrays. Such arrays are lazy not only in their values, but in their indexes as well. Read the paper "Efficient Graph Algorithms Using Lazy Monolithic Arrays" (http://citeseer.ist.psu.edu/95126.html) for further details.
Version 0.1.3

lazyBufferOp is the BufferOp definition over ByteStrings, the non-strict kind.

Check the invariant lazily.

Run IO actions lazily while respecting their order. Running a value of the LazyIO monad in the IO monad is like starting a thread which is however driven by its output. That is, the LazyIO action is only executed as far as necessary in order to provide the required data.
Version 0.0.3.2

Lazy SmallCheck is a library for exhaustive, demand-driven testing of Haskell programs. It is based on the idea that if a property holds for a partially-defined input then it must also hold for all fully-defined refinements of the that input. Compared to ``eager'' input generation as in SmallCheck, Lazy SmallCheck may require significantly fewer test-cases to verify a property for all inputs up to a given depth.
Version 0.6

See the source of Numeric.LazySplines.Examples for usage.
Version 0.1

This module presents an identical interface to Control.Monad.ST, except that the monad delays evaluation of state operations until a value depending on them is required.

Mutable references in the lazy ST monad.

Lazy RWS monad.
Inspired by the paper *Functional Programming with Overloading and Higher-Order Polymorphism*, Mark P Jones (http://web.cecs.pdx.edu/~mpj/) Advanced School of Functional Programming, 1995.

Lazy state monads.
This module is inspired by the paper *Functional Programming with Overloading and Higher-Order Polymorphism*, Mark P Jones (http://web.cecs.pdx.edu/~mpj/) Advanced School of Functional Programming, 1995.

A monad transformer that combines ReaderT, WriterT and StateT. This version is lazy; for a strict version, see Control.Monad.Trans.RWS.Strict, which has the same interface.

Lazy state monads, passing an updatable state through a computation. See below for examples.
In this version, sequencing of computations is lazy. For a strict version, see Control.Monad.Trans.State.Strict, which has the same interface.
Some computations may not require the full power of state transformers:
* For a read-only state, see Control.Monad.Trans.Reader.
* To accumulate a value without using it on the way, see Control.Monad.Trans.Writer.

The lazy WriterT monad transformer, which adds collection of outputs (such as a count or string output) to a given monad.
This version builds its output lazily; for a strict version, see Control.Monad.Trans.Writer.Strict, which has the same interface.
This monad transformer provides only limited access to the output during the computation. For more general access, use Control.Monad.Trans.State instead.

Lazy writer monads.
Inspired by the paper *Functional Programming with Overloading and Higher-Order Polymorphism*, Mark P Jones (http://web.cecs.pdx.edu/~mpj/pubs/springschool.html) Advanced School of Functional Programming, 1995.

An efficient implementation of maps from integer keys to values (dictionaries).
API of this module is strict in the keys, but lazy in the values. If you need value-strict maps, use Data.IntMap.Strict instead. The IntMap type itself is shared between the lazy and strict modules, meaning that the same IntMap value can be passed to functions in both modules (although that is rarely needed).
These modules are intended to be imported qualified, to avoid name clashes with Prelude functions, e.g.
> import Data.IntMap.Lazy (IntMap)
> import qualified Data.IntMap.Lazy as IntMap
The implementation is based on *big-endian patricia trees*. This data structure performs especially well on binary operations like union and intersection. However, my benchmarks show that it is also (much) faster on insertions and deletions when compared to a generic size-balanced map implementation (see Data.Map).
* Chris Okasaki and Andy Gill, "*Fast Mergeable Integer Maps*", Workshop on ML, September 1998, pages 77-86, http://citeseer.ist.psu.edu/okasaki98fast.html
* D.R. Morrison, "/PATRICIA -- Practical Algorithm To Retrieve Information Coded In Alphanumeric/", Journal of the ACM, 15(4), October 1968, pages 514-534.
Operation comments contain the operation time complexity in the Big-O notation http://en.wikipedia.org/wiki/Big_O_notation. Many operations have a worst-case complexity of *O(min(n,W))*. This means that the operation can become linear in the number of elements with a maximum of *W* -- the number of bits in an Int (32 or 64).

An efficient implementation of ordered maps from keys to values (dictionaries).
API of this module is strict in the keys, but lazy in the values. If you need value-strict maps, use Data.Map.Strict instead. The Map type itself is shared between the lazy and strict modules, meaning that the same Map value can be passed to functions in both modules (although that is rarely needed).
These modules are intended to be imported qualified, to avoid name clashes with Prelude functions, e.g.
> import qualified Data.Map.Lazy as Map
The implementation of Map is based on *size balanced* binary trees (or trees of *bounded balance*) as described by:
* Stephen Adams, "*Efficient sets: a balancing act*", Journal of Functional Programming 3(4):553-562, October 1993, http://www.swiss.ai.mit.edu/~adams/BB/.
* J. Nievergelt and E.M. Reingold, "*Binary search trees of bounded balance*", SIAM journal of computing 2(1), March 1973.
Note that the implementation is *left-biased* -- the elements of a first argument are always preferred to the second, for example in union or insert.
Operation comments contain the operation time complexity in the Big-O notation (http://en.wikipedia.org/wiki/Big_O_notation).