Parents (Total: 50772)

The searched file hash is included in 50772 parent files which include package known and seen by metalookup. A sample is included below:

Key	Value
FileSize	3951520
MD5	FD13684087C34948AC976DAE24B40A9B
PackageDescription	Subclonal copy number and LOH prediction from whole genome sequencing Hidden Markov model to segment and predict regions of subclonal copy number alterations (CNA) and loss of heterozygosity (LOH), and estimate cellular prevalence of clonal clusters in tumour whole genome sequencing data.
PackageMaintainer	Debian R Packages Maintainers <r-pkg-team@alioth-lists.debian.net>
PackageName	r-bioc-titancna
PackageSection	gnu-r
PackageVersion	1.28.0-2
SHA-1	0003F184315C3FE37229EEC2DBDCBE7C922871AE
SHA-256	0B8E04A60BC54400B3BC6B108B465D7B44A645CD6E51FEB3DC0010F664ADB793

Key	Value
MD5	733FB062F5D732F3AA27A56A1B4B1E43
PackageArch	i586
PackageDescription	This package provides an Rcmdr "plug-in" based on the time series functions. Contributors: G. Jay Kerns, John Fox, and Richard Heiberger.
PackageName	R-RcmdrPlugin.epack
PackageRelease	2.719
PackageVersion	1.2.5
SHA-1	00055353D103351AF898182ED6CE2D566AD306A0
SHA-256	92300CED467C1317DBB94A09B3EB5ED5F105AE4B2385C1B11360E2E8BE3887D0

Key	Value
MD5	FCAD640A2759F9B6EB42E5FB9189CF52
PackageArch	i586
PackageDescription	A minimal, unifying API for scripts and packages to report progress updates from anywhere including when using parallel processing. The package is designed such that the developer can to focus on what progress should be reported on without having to worry about how to present it. The end user has full control of how, where, and when to render these progress updates, e.g. in the terminal using utils::txtProgressBar() or progress::progress_bar(), in a graphical user interface using utils::winProgressBar(), tcltk::tkProgressBar() or shiny::withProgress(), via the speakers using beep::beepr(), or on a file system via the size of a file. Anyone can add additional, customized, progression handlers. The 'progressr' package uses R's condition framework for signaling progress updated. Because of this, progress can be reported from almost anywhere in R, e.g. from classical for and while loops, from map-reduce APIs like the lapply() family of functions, 'purrr', 'plyr', and 'foreach'. It will also work with parallel processing via the 'future' framework, e.g. future.apply::future_lapply(), furrr::future_map(), and 'foreach' with 'doFuture'. The package is compatible with Shiny applications.
PackageName	R-progressr
PackageRelease	1.27
PackageVersion	0.6.0
SHA-1	0005B925FFF82B126F38E6F98CAD3EEFE1975561
SHA-256	BB88235F254ED974367268A7D95945CDA7AB9A189EA7B0E3E74F2AA0485C47C5

Key	Value
MD5	5FE2371EC1FEB00EE9362CAED4CD5D6C
PackageArch	x86_64
PackageDescription	Functions for testing if the covariance structure of 2-dimensional data (e.g. samples of surfaces X_i = X_i(s,t)) is separable, i.e. if covariance(X) = C_1 x C_2. A complete descriptions of the implemented tests can be found in the paper Aston, John A. D.; Pigoli, Davide; Tavakoli, Shahin. Tests for separability in nonparametric covariance operators of random surfaces. Ann. Statist. 45 (2017), no. 4, 1431--1461. <doi:10.1214/16-AOS1495> <https://projecteuclid.org/euclid.aos/1498636862> <arXiv:1505.02023>.
PackageMaintainer	https://www.suse.com/
PackageName	R-covsep
PackageRelease	lp154.2.1
PackageVersion	1.1.0
SHA-1	0007A6C182BEBC16515D77D50E935418FDC369F1
SHA-256	E5E02933498AEB520ADA9AB7D5E7908DAC678B1451BCD2AD965E235F878FDF41

Key	Value
MD5	27DEBB80C913C5021FE40D299BADE8E5
PackageArch	x86_64
PackageDescription	Provides methods for high-throughput adaptive immune receptor repertoire sequencing (AIRR-Seq; Rep-Seq) analysis. In particular, immunoglobulin (Ig) sequence lineage reconstruction, lineage topology analysis, diversity profiling, amino acid property analysis and gene usage. Citations: Gupta and Vander Heiden, et al (2017) <doi:10.1093/bioinformatics/btv359>, Stern, Yaari and Vander Heiden, et al (2014) <doi:10.1126/scitranslmed.3008879>.
PackageName	R-alakazam
PackageRelease	3.35
PackageVersion	1.0.2
SHA-1	0007FBFF83F49F4FB6F1EE69E816F2EC221CC26C
SHA-256	CA7168D4F6EE1323F4456EA15F7D84920D592618BD901DF35AF1E44BC3C6C38B

Key	Value
MD5	F078F652E6AF31C382FB8EC10CB61FFE
PackageArch	aarch64
PackageDescription	Obtain the native stack trace and fuse it with R's stack trace for easier debugging of R packages with native code.
PackageMaintainer	Fedora Project
PackageName	R-winch
PackageRelease	1.fc34
PackageVersion	0.0.5
SHA-1	0008488B3C2C9DF4FA6AB4F71551AEC8C48A514D
SHA-256	C3F1F36E4CA892EAF2BBB50D81497C1906391B561ACA0771D9A83159B853FDE0

Key	Value
MD5	48D6F0EF1AE8E561F7F5B6540BBAC1D1
PackageArch	x86_64
PackageDescription	Provides methods for estimating parameter-dependent network centrality measures with linear-in-means models. Both non linear least squares and maximum likelihood estimators are implemented. The methods allow for both link and node heterogeneity in network effects, endogenous network formation and the presence of unconnected nodes. The routines also compare the explanatory power of parameter-dependent network centrality measures with those of standard measures of network centrality. Benefits and features of the 'econet' package are illustrated using data from Battaglini and Patacchini (2018) and Battaglini, Patacchini, and Leone Sciabolazza (2020). For additional details, see the vignette.
PackageName	R-econet
PackageRelease	lp154.2.1
PackageVersion	0.1.94
SHA-1	000972A1F806D67429B5F164A146276B75851B14
SHA-256	A5E5EC71E68CAE815B5759BD8E3B356C74736597C2E9F5DAD2DE93F9D71BAF7A

Key	Value
MD5	96F101A9268EC58EDF785558BA149048
PackageArch	x86_64
PackageDescription	Implementation of the algorithm introduced in Shah, R. D. (2016) <http://www.jmlr.org/papers/volume17/13-515/13-515.pdf>. Data with thousands of predictors can be handled. The algorithm performs sequential Lasso fits on design matrices containing increasing sets of candidate interactions. Previous fits are used to greatly speed up subsequent fits so the algorithm is very efficient.
PackageMaintainer	https://www.suse.com/
PackageName	R-LassoBacktracking
PackageRelease	lp154.2.1
PackageVersion	0.1.2
SHA-1	00098FB3933A291D2F4962355806959F77BE7F37
SHA-256	FC538C14B1F6B3ACD1229797AF7E7E6C258E52476758EA74BC4C3FCFA39A3858

Key	Value
MD5	91661B8739218822AC5208C8CD93EF6B
PackageArch	x86_64
PackageDescription	We provide a collection of statistical hypothesis testing procedures ranging from classical to modern methods for non-trivial settings such as high-dimensional scenario. For the general treatment of statistical hypothesis testing, see the book by Lehmann and Romano (2005) <doi:10.1007/0-387-27605-X>.
PackageName	R-SHT
PackageRelease	lp154.1.1
PackageVersion	0.1.5
SHA-1	000B4C5237DD39D467EFFA07A0D170E86945EEF9
SHA-256	129082E104FE54A036B46F08A7A65F13DA6980540B4E3B7C03E88436440E1D5A

Key	Value
MD5	1447BE44EDC955B78990944F93FB1627
PackageArch	x86_64
PackageDescription	To help you access, transform, analyze, and visualize 'ForestGEO' data, we developed a collection of R packages (<https://forestgeo.github.io/fgeo/>). This package, in particular, helps you to easily import, filter, and modify 'ForestGEO' data. To learn more about 'ForestGEO' visit <https://forestgeo.si.edu/>.
PackageName	R-fgeo.tool
PackageRelease	1.21
PackageVersion	1.2.7
SHA-1	000BF6EDC3C19CD85D0AC87A1934A03853BB98E8
SHA-256	E53924DD4F319ED1997AC3D198E03AC79DB4A2C16957CDC821205405CD6A6A42