(/ X3 +COMPACT_MANIFEST 000644 003174 0 013475 0 ustar 00root wheel {"name":"R-lava-single-standard","origin:version":"1.7.2.1","comment":"Latent Variable Models","maintainer":"CRAN Automaton [cran@ironwolf.systems]","www":"https://kkholst.github.io/lava/","abi":"Linux:2.6.32:amd64rch":"lx86:prefix":"/raven","flatsize":3484138,"licenselogic":"","desc":" \n\nA general implementation of Structural Equ with l\nvs (MLE, 2SLS, and composite likelihood estimators)both\ncontinuous, censoredordinal outcomes (HolstBudtz-Joergensen\n(2013) <doi:/>). Mixture mand non-linear \n (2020\n93biostatisticskxy08293//The package also\nprovides methods for graph explor(d-sepa, back-door criterion),\nsimul\ninfluence functiona broad range of al.ps":{"R-future.apply:{11.0"},"R-numDeriv2016.8.1.1progressr0.14SQUAREM2imary4.3nls},"categories":["cran"]} +21730232,"file/lib/R/libraryCITATION":"1$77ffae7ba815145bfbf54cf219cd561752ec1ac0c00ef8f74f5611f08c428aa4",DESCRIPde8c034fd0672d6351e4c7584996c7964bfca0527a47fbfcd6656d34b4a622aaINDEXf38d67aeb8d3fe918d0a93573a2e0bbce483c57fca33e81294af4c2793d54f4dMeta/Rd.rds2a321de00c154c30ef2f695435889feccfd2e5605c044a52a909f37528114f01datafc852274e11716de79670e3d4ea4fb964d04a5da410b572fb344c2c4742ccf9emo6df0cc5caea224ec4e98011fce58f1e233cb8930f6277af59dfd535d98e7e26bfeatures8802ec1faae0365bf8c361b54fbe4f35867ba23d00e5154240fc7116dcahsearchbd9e13f58d90643e910ecec6cec2d4b4a278543fc12d39071812c411057b85flink1d7ccfb9a01986394480c34724dfffb0dcf63fd010c7ffaf1e61655ea2c867b9nsInf139ad6040d973c608402119ada848e1cfd055a38062f47752f03ac6985c7d38cf59acfa3f39f163f27b8bdbe87da86af2072335ca5481e5ac1ade5cfd1d5913avignett3e4a408ce7337176c074896d8ee57973cbb89f7b6738519ad4485d555962f967NAMESPACE209f7a0fe9d3b6430c65e99da6b0910109df2a8ebc3d4b8d5aa998d5eef46bNEWS.md0957101a26b1f13e767718cb333bcd5e131199e148fed123bdf1cdf7d0fa4d0R570ca456b280cdeb201ef5ebdf22dc8f80092e2c0c68e33c7f73340e420f3759.rdbbb798ecdcc33894c3d07a4bcc94a6100c641fb9eb061d0c3e853715ee6cx572d7199982ed9783ba58e0f4edd8c2a849814eb4bf120544f1599150f16a8e6data/bmd.rdaa5bee95d63ba7b9fcdeacdd905328367825fad571c03393aebc23864b160f82i3c5be7533e56ecfe40f664f1ac288fe622816ac7f5e0fcddad259cd2f054a1cris7d53d504df87250317bd0d9c204513e01ac1841aa9f7efabbfaadb280calciumd8a5fa3eb0be01a4cb4d24eaeb3583b87cff7caf81b2b4ea28822ac6f05ad28ahubble2afb56b63f2c79ac53671fc651634641178964b28a2bc2f05842efd10bdd09cf20832b1fc9afbfee43ef1b83f4f3d10ed11f46b5235251b89a94944dcea7fff8bindoorenv32118479e76052fa72e0983e2e2a9ec63241ef6d5533ebc566bc538e43fd12demissingbed5a0f8160e1e37290dde614623c0c25abafac78fbc39e3c23768c026bd117nl6196157282555c72282cdca607a2f25d5287593fa601f2f6b9c589f1835c943se51cf1d00d2fc6173be2b4808b14bfa4280c49de04949a22b759fee3b282e0sem121614535aae05b70bf9d710e56dcad5abd71712c1d5661a66c3421875f3333fserotoninc45f78c6eee162cffe1f2dd0d3f34a53c92f93b7d3150aaf7074da2d560db7f810b1ef094c106081fd957395fd9a362ead1bbe129fc0ec744323d030da0twined38714475dbd31a3dcfd06c2641fedffc0d7001cca32e61a863c22demo/.R27d2e7257748b70cb0aaec5bcba46ab76e4dd7b15d10982b00eb93b96e7846finferencecffca0d4b52853fa33af76b1445487adb118daf058ed7e6281362a46986d8c1530f86e02d717d024e12d3775cd533b57ec81b21340236559a3ee7ead3b5d65b82bc88d9ac8460520371ea7b7aaa39ca5ed116c5f0242d3c02a7c6d037a93ef6ddf893bb03705f368b45ffd016ea8d883664dc659beeba0f493059f64eaeafa9oc/correb62572ad00561250cdb9197ad9f596b85f38df40fe6ef1ed31dbd2cbaeecb04c853d4138183c4045152fabc74b73387860d77e61e8cb1219e006899f18ca36html94c7447fcab0aa9e42276f017e640c5e85b3935e352f086bbfcb7d64e4d87index7c9f02feea33db50e6e5e34594f7e61d1a4dc30ae2a23c21b45b670c3non8012bf4c26efc88f6a0fd8696848402d054f019724a2ec496367399e9e3ba4752ebaf3a677f1a26e57731a949c16fc4e0707d356cc02d59b4e126ea162c76569467ba5034bcbf3160476252e7c8269dfade495c1a2b104a24097685980f2help/AnInde2ad83e63df7b17c221a345c2ea3e0cf8895523d3050b5e82859178977a7e4707aliasc8a52f38b4b67aff0c02a35d5fa6ab338677d268ad63a7918fe3253f1d123afigures/gof1-1.pngf3622f375bb403103f2765459c3863e3d56b8db3a2a0e7d2a10fe85321c13e0elvmb8821cb6d64f53fe3498704e9773081eedd14a58f3da93d5a521975c6a0016d6medic73ef87f9b7c81a8735d2865561b8f91c2e4175424e1c4af8ff54ee668f86c58nli3e83d634c4d10a82cf8f4490451c769750bf46076041db6aceede853f520b174simres1324505c0c8d51875d85c721149ad81a51237e2b5607d8b0770d69042ce971fd602316e8c31ff5ed68ba8914c85e464c5abedf89d1d3bb09f06bd7573eaaf29x8b2106fab42ca70bebffd56755bd959c99293ac41098beda986fc630dfd4a5eapathadb4c2b1d29924f1b7529d82b1c4a0bf6a13a98bd1b3e71e438d0ce249a26a52tml/00I882bc6e3e2c902b7d6f2c2ced8c7a66ec548bdfbc47cb03f9512d5ee8a9d48tests-al61d7a743dd2c5205f6f7a038a8592853b2ae2b6928893dc5b3e1fbb6d45eae14thatconstraia4f9d399a1235e5ec4d9d1b2c95b0fb9af0345bf16768f026b265e5f9a27b7b1e_default2c96bb0971aaef00145ca5f08163ebdfcbed53dcbd3ad137505e1952204bb79b363e095c7d94924c0a02f97a7e84de1a5375d214cc13fcfa1e0a99821eab04c780b99f3064e3c0e9bc2dab33cbada3e3442376e69f7e4553165a21fc7217d020317026532fd0263ff88adf8a87a96e820ac04f8dc6fe9037578a67bmisce396b2964ed4f37dfb0cd7ab8cde13cafb6dedd0ba82a1c6e8f4066e6bc05d936c7e62497fb3d014f87d08038ecf9daf4e3e45d0273a56624e9093dc08ultigroup8250712eefd57fd6e2fba103a2d37e949d3121dfda9cd211234a0f597ab747cplod89ce61e4b83e8e7ad701373ffa74a5a36a14f92e2d1f09d93b261672aa32bbsima0d249dc4375f22075be3dd81a7b12f0debc5bec37eb86f963905d15c01ecab"} 036 14473520061 017046author1 <- "Klaus K. Esben"
yea2013
journalCompual S"
titleL:"
doi"
volum28
numbe4
pages385-1452"
textvpaste(, " (", , "). ",
, ". ", ", ", ,")", pp. doi: ", doi", sep="")
22202BA two-stageproceduresem22122676-6912222222
citHeader("To cite 'lava' in publics use:")
bibentry(bibtype="Article = =year = = = = =doi =extV = )
222222222 462422P:
Type:
Title
:
Date: 2023-02-25
As@R: c(person(", "", email="klaus@it", role=c("aut", "cre")),
Brice", "Ozenne = "ctb"Thomas", "Gerds): [aut, cre],
[ctb
M<>
Descript
<>).
URL:
BugReportscomissues
L: GPL-3
LazyLoad: yes
Depends: R (>= 3.0)
Im, grDevices,ics,,s, survival, , utils
Suggests: KernSmooth, Matrix, Rviz, table, ellipse, fieldsgeepack, knitr, bookdown, rmari(>= 0.6)lavaS2, lme4s1.1), nlme, optimx, polycorquantreg, rgl, R.rsp40), ed2), (>=0.11), visNetwork, zoo
VBuilder:
ByteCompileEncoding: UTF-8
RoxygenNote: 7.2.3
Needs: no
d 19:47:09 UTC;.
Reory: CRAN/P7 08:12:30
Built: R ; ;8-30 01:49:37; unix 30175115 016507* Eand
** building
addvar Addto () object<--
adjMat Extract adjancey m from/
ancestorsof nodes
baptize Label ecancel Remove asso betweencaDefine predic-
children or parentAdts to-
concAdd fix -
descendantsdbecoefShow parameter names
distribuof-
edgeLisedge listedgelabelson'lvm'endogenousex/deventTiman observed time to afixso Cs in measuridentifiability)
i(fornlinterceptFix mea-intvenrm. -
kil -
vm Initialize newmakeCreate random data
manife
merg Ms (lvm,e, ...)
ple nodecoloSet coloursas-
addiof thparFixparpoGenericfinindece
parspath thwayPlo diagram
Rangeofslopedom nent-
reregrooroosinksinkubse
timede Time-denttransformv
backCheck
bootstraCalculatea lvmlosed.testC
confinfitdlimitcontastces
comparS Performs L-, WaldcorelikCconfprednformal ion
eing coeffici
cv Cross-valid
ds inquivaleIy candideff; cindirect, total
-
.AggregandEa)
gosummarieGOFgkgammaKruskal-Gfotigency tGIC i.i.d. de)iid - (obsolete)
iC
IV Instru (2SLS)
kappaCohens
opSet global for.errTor()PsVing
mom-specifanomiprobie
mvnmultimsee 'two')reOpartialcs
p.cMadjustment
p Polychoriclot.For orline plot
Pin
profilP
residual
riskcom'OR,'Diff','Ratio')
Dif-
OR -
odd-
expitlogtigolscheffeSbands (lm)
fit
stackStackngs
sisimWrappercl
as.Cx as 'sistartvaluesStarer.-012 SEM)CVSEMs
wkWeighted k-means algorithm
zibRbdataunknownf unaedUt
%++% Concatenperator
%niMat (x not in y) opo'%in%'-
blockdiaComb to By Apply a FData Frame Split by Fa
clickponwa -
C RGBr
across difcommFindsuniqucsfoldfold-
curlbrackeExpandAlloffFasterlargevia rgl
f(/it visualiz)
getSAS Read SAS output (ODSMpl
ks2e bdensity
PD Dose responsecolsSelect(s)actively
bconfbAddplot_reg-
devcoorReturns d-innd-
diagtdiagnosticfor 2x2imOrganize sevagldata)
Inverslized i
orvertscii suiorgedesignexpCons
pdfcpdf to r
procformulProcesmula
rev/ 're'-surfacV
offor fsispaghettu longitutos string TracetrTrim of (leading/trailing/all) white spaces
vec vecwrapWrap vector
x2NA to/NA
NA2x -
na.pas Handle by setNA to zeroDs
MAddmechanism toN
gaussianLog-npoisso PthresholdT(e.g., Probit)
Bfamily / Bernoulli-
p-
gLog-chisqChi-squaredstudenS-tuniformU or discrete)weibullWidqtegers 1:n
onesFixed szeroonnsta
binaryBbet BetMvnGM2G3-
coxWCox-baseline)
coxExGompertz.vlmaalenAalenvheavytaiHeavy-tailrodds-produ(riskcerrve risk)toPnoneDetermin(fat its-)atasets
bmLBone MiD(Wide)
Data
Hd2Examp)
seSEM)
S2Twnarch126277664\s7r-].)Q$٤:\E=:QV}\3]g%O?:N7إD9JRm.zh4{+++VV}rm
~l,-3.l"%%+dv;YZѴ73Uzȅt֦T%CMAghhd$GV`uo %"F|2lCZ[5(.XS~.(B0puQRԈhŏ39Ox