Result for 0DCD9E6556A7849D26AE7BFA10329D312CB675CB

Query result

Key Value
FileName./usr/lib/R/site-library/party/DESCRIPTION
FileSize2501
MD50BA37CE0101F625454C46465D76ED91C
SHA-10DCD9E6556A7849D26AE7BFA10329D312CB675CB
SHA-256E4518EFC5F23EFAE619CA6E851D171850966957229F0569AB67B69491AD2A770
SSDEEP48:ZrNi08rwt5YvjUbp99yBpGouP9UQJr95NxT1B1Ll4XnmSo6jm0:ZRi0ywjYbYvM8P9TJrfX1Z4Xmo/
TLSHT1585194027C216581778BE3193376A605B3AF61587DB9786C726C04BC1B2ED5C46FBB4C
hashlookup:parent-total1
hashlookup:trust55

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Parents (Total: 1)

The searched file hash is included in 1 parent files which include package known and seen by metalookup. A sample is included below:

Key Value
FileSize1091220
MD58210A3616740D6BFB29283C4D054FC82
PackageDescriptionGNU R laboratory for recursive partytioning A computational toolbox for recursive partitioning. The core of the package is ctree(), an implementation of conditional inference trees which embed tree-structured regression models into a well defined theory of conditional inference procedures. This non-parametric class of regression trees is applicable to all kinds of regression problems, including nominal, ordinal, numeric, censored as well as multivariate response variables and arbitrary measurement scales of the covariates. Based on conditional inference trees, cforest() provides an implementation of Breiman's random forests. The function mob() implements an algorithm for recursive partitioning based on parametric models (e.g. linear models, GLMs or survival regression) employing parameter instability tests for split selection. Extensible functionality for visualizing tree-structured regression models is available. The methods are described in Hothorn et al. (2006) <doi:10.1198/106186006X133933>, Zeileis et al. (2008) <doi:10.1198/106186008X319331> and Strobl et al. (2007) <doi:10.1186/1471-2105-8-25>.
PackageMaintainerUbuntu Developers <ubuntu-devel-discuss@lists.ubuntu.com>
PackageNamer-cran-party
PackageSectiongnu-r
PackageVersion1.3-3-1
SHA-1A23D4D5077825E0F3DB6D4B3A147B0E891A353B3
SHA-256389AB085C89FE5C560F22AD822A19F69201DD2F4ADEAEA874960A714FD9B19A9