(/ X+COMPACT_MANIFEST 000644 003221 0 013466 0 ustar 00root wheel {"name":"R-sandwich-single-standard","origin:version":"3.0.1","comment":"Robust Covariance Matrix Estimators","maintainer":"CRAN Auton [cran@ironwolf.systems]","www":"none","abi":"Solaris:10:amd64","arch":"sx86:prefix":"/raven","flatsize":1754014,"licenselogic":"","des \n\nObject-oriented software for model-rcme.\nStarting out from the basic Eicker-Huber-White \nmethods include: heteroscedasticity-consistent (HC)s for\ncross-section data; and autocorrelation\n(HA time series (such as Andrews' kernel HAC,\nNewey-West,WEAVE); clustered(one-way and\nmulti-way); panel-ct;\nouter-product-of-gradients() bootstrap\n. All are applicable to (generalized) linea\nos fitted by lm()gbut can also be adapted to other classes\nthrough S3. Detailsbe found in Zeileis et al. (2020) <doi:/>,\n(200411.i10\nand 66.i09.ps":{"gcc9-libs:{gcc99.3.0"},"R-zoo1.8.9complet4.1.1"}},"categories":["cran"]} +161132324,"file/lib/R/library//CITATION":"1$be936cafe8e5e617f5695b72281c7bef03d24d162a684176c717c5bb102c2f9a",DESCRIPf08c3dc7c131b7f8e40f2e0adb5b23e80ff47e46de1ff2b81b88d70472273c92INDEX2aa4251f8bb1e4ed66290209e941622e4d45636a23da9c538ba5cf472e182f38Meta/Rd.rdsa122b5b604263bc6e09572d44d419c78979ab025e5666f7d207432a615ea85e6datae01387687e26038cf1c82aa7bfc18e1eb4dd6f8903265bca276a34d585419bddfeatures426a126f42f2c4f49b26cd1f39a5b7f1d2f3492cab64d3640dd6e6c7714f2f0ahsearchbd23e6fba00b91884046de3c9971dd23355fe00bc7fab73bce6421438de2e7a8link0dd1fc687fa01b472b8514183ac0ce11a147121dcff4c363e2cf17b60290c8d5nsInfobbe65574d4c10bffb9eb7b9440f76847880db20c40d235c4416140d5d827156package79ffd17fa45d67c1e05dca1a9df50c6eed6568277fc73d28dcabbf8589715703vignettd953044d6c2d33a4371f2944a361a92a444a592584c93635af2e4a34fe88da7cNAMESPACEdf386d5d1a4f6805167036111a2fc9471840e0aee848fefd730c85606e457eeNEWS.mdf1218775210d68ae9136e4e9a3b33c5c2534d7c8782b0d5c59e579bec8119097R570ca456b280cdeb201ef5ebdf22dc8f80092e2c0c68e33c7f73340e420f3759.rdb5daa1107dd598d16d1d7ddce64c9e9e9932e2814e4be01cdad2793855eb296dx98dd5540fe73e43eb7319504579301ba7c16b870d7a7a74e6a08b3b5f6f715f1data/InstInnov.rda1dd34751be33f4d7688c7072048eb0c46f5946115a0627bc358a348d8db15da6vestment3baae1aaaa7bac4cac08ca1d55f103f5e22e7d3c9509bd1d76dd55106780bfe0PetersenCL1a766cab43effcce21782be9c4a170e1798e76e41caebcd6d646a11958d4719ublicSchools2ae40eda52847a1b365dba1824fb2237cd281add455b6f90ebf49cfca1577a91oc/index.html05c471a1cae14fa86ac8b80469e60e1e129d343cdc9a37cb9fa410511d7bd185-CL.R8d29c37bb95e401a82b4f2c7f3dc26b2cd11e5db0cc4c69b98b56d5b7cc7fe1cnwc3c161ae8e705d1c672cc477e0a567b225925aa4f13fc21dfa1869a70e6ac902pdf4039ec93e41a9f749316f4fc1fbfe750c02958c33ef5496af7aea79c49b7c9acOOP40d90f2f92c08ab197c0fd67f0a2129980cc67d02397fe75c93f8740ab68a5b9c63c0f4c27cdf15bed863203d620201f7fcaaae146571ae630e65e8a80451ace2d62de6c401c398b72d4fd46b463a7cafec406b4e34d294c0ffd4daea2039ae847c6b26c5ed7cb09d1b0f2674757f2a676d1cfb8adf954093aa63e0f18620023eb28f071aab6c18d55eeeac10372560f0a4a6c6a0ab9db7eec02a799551ce4cd427a4465dd7a4cc7d705b29470ef67c88dbe8ca7a733ff15dfce6308imbf83bd71575c542596249b6c0b9e069bb21f644f3c4a8b895455839c6f4f223c8c7288e751f9f048704e665740a9dce9db0c35995ae527f1f76aefhelp/AnIndefaedea94810bc85fd111d97054a5fefe195f95be1ddc3d49f495e0be84c6037aliaseae4d11e177ed2ef39fa551a5d28eb3e023d41b453d63754c646f1ef99b1d8afigures/README.svgcbabe0c036558fcbcf0f5cafb5e1e285b462fabca60ac2ff6816e82a7f8094pathfe4ec387e1dab3279842b93eb8aedb55ebe1688a307556e9d88c6c0dd6c0aa964baefa5b01affdc79c9e7f2b5cdc6189efe91d0ef1808318441721ce8293887x0f64682c28bbbfcc57e195004b553e309a7a57a7746c1be30b43bb5b4etml/00Icbea1f29ddfaad62c79aa8146e51f139bd8572be77d50afff1d89c24ca6ddfd3R.csf4849ab1757247808d54da2c371e992364371c942ad7d629dae8d62327ed068tests/Examples-Ex.Rout.save5a0a45a80029ed7881a045e90f6399e7c9d47cd43a6f3478b9e67d46f391e48evcov8d3a789209506e0d583acd06ece84616bab52a88d3ef32428c81803b96e4335666bb8b2cc231c4d94375791d598faed109f5b2ada724fa698c0d7f87c95dd5bPC99173a36f30539fa1618d0e6cf17f7502c4061686a0f573c80a42daa23be53d4227ead482ff2356685dcf75eff98b29af4c2486d2b77c8903cae7f85fd044deL5cf646c7dc6bd2413208a13545487ecb47de17767e144cb1a5c40627df89531451b160ee967cb2ea42cadadde5cde247133780d0f1c0478971c1ca555f0"} 621 14112614412 017715bibentry("Article",
title= "Various Versatile V: An OImplement of CCin {R}authorc(person(given = "Achim", family = "email.@R-project.org",
ORCID = "0000-0003-0918-3766"))SusanneK\\\"oll"),
NathanielGrahamnpg1@.comjournal= "Jof Statistical Syear= "2020",
volume95number1pages --36doi
head= "To cin ps use:"
)
Econometric Compuwith {HC}{HAC}0411017Ifss, pleasecitS6696",
than 422620270P:
V: 3.0-1
Date: 2021-05-18
Title
As@R: role"aut", "cre"), ThomasLumleyt.l@auckland.ac.nz"),
ctbgmailoell))
Descript <><> <>.
Depends: R (>= 3.0.0)
Imports: stats, utils, zoo
Suggests: AER, car, geepack, lattice, lmtMASS,wayvcovparallel, pcse, plm, pscl, scatterplot3d,4, strucchangesurvival
L: GPL-2 |3
URL: .R-Forge./
BugRecontact
NeedsCompi: no
d 02:32:36 UTC; z: [aut, cre] ( ],
[ctb
M<>
Repository: CRAN9:50:0
Built: R ; ;8-29 05:20:10; unix 0066353 and Institutional Ownership
US Data
Wes HACion
's SimulatedAssessing S Errors
US Expendi
bread Bfores
estfun Extract Empirng Funs
isoacf Isotonic
kweightsKW
lrvLong-Run ofMean
meaA Simplt
Makingand
vcovBS () B vcovHAC H-OPGOPGPPCP for-basededAdaptiv 227520535Ws4v䚴Mw0@soiHkKUDS[r@Y[-a> Iohەe-mW;ه^SSJ H zt3`*p-.
3LGg
ӘpX5xg1u\rc.
g
{U`2_I̗7##Ѩ h f;%p29*'#G"xM`}҈a_ $7%nץoEEXWD4dBjHVAd 4ٚmXyin$i[m
OFx6h?}^ŭC=h/5B$UOOG@JU(S|`SN
''f
u+}18|3a,#t<MNĈx9g\sj|bAgJnˏʶNXׂP$%b8iXƮ?;͘`v悅sK7S6]N5Ml6tX(>$_2&! bT˚75/#D9T렩ڗ'emY,$
%kp:{`Y9xEy(l6xQD:[ AEaO#z-ݛk {h0EدyMC#
MfE~y