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The D-Link Cloud Camera and record and store video and images to D-Links cloud system, which you can access on your computer or iPhone. Even if a spy camera does not have a compatible app, many Wi-Fi cameras stream videos on a secure website, which you can access on the iPhone's Safari browser as well. Instead of using a fake plant, clock, or a standard CCTV camera as your spy device, you can also use your actual iPhone as a hidden recording device.

Through many available applications, such as Presence by People Power, you can transmit a live feed to other iOS or Android devices. Top Secret Recorder Pro records both audio and video straight from the iPhone while hiding the recording application from others. With this app, users who pick up your iPhone find the home screen or the password screen, unaware that the app is still recording.

Regardless of the use, it is possible to transform your iPhone into a fully functional spy cam that collects high-definition video, stores both video and audio, and maintains the highest levels of secrecy. Choosing the right apps and camera is central to your secrecy, and you can discover hundreds of various iPhone accessories and other pieces of spy gear on eBay. There are many reasons why people get involved in spying activities. Perhaps, you are a parent, and you care about your kids, you need to keep watching their actions when you are not with them.

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If you are an intelligence investigator, then spying around without being noticed is one of your primary roles. Indeed, there are countless reasons for spying, but the results will largely depend on the camera you are using. If you are using an iPhone, then you need to install specialized camera apps to achieve this objective.

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Well, there is a countless number of spy camera apps for iPhone, and this can be a challenge to many users. If this has been one of your concerns, then worry no more as we enlist seven best spy camera apps for iPhone is If you are planning to spy during the evening, then you probably need a spy camera app with the ability to capture images and record video under low light intensity.

Indeed, Night Version Camera app for iPhone is a perfect choice.

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Description sf there re dtse entries in D whose nmes oninide with the model prmetersD they will e overwrittenF. Input arguments M model E wodel ojet in whih the numer of premeteristions will e hngedF N numeri E xew numer of prmeteristionsF. Output arguments M model E wodel ojet with newly ssigned prmeters ndGor stedy sttesF Assigned ellstr Inf E vist of tully ssigned prmeter nmesD vriles nmes stedy sttesAD std devitionsD nd rossEorreltionsY Inf indites tht ll vlues hs een ssigned from nother model ojetF. Output arguments M model E wodel ojet with updted de nitions of utoexogenised vrileGshok pirsF.

Description henever you set the utoexogenised vrileGshok pirsD the previously ssigned pirs re reE movedD nd repled with the new ones in AF sn other wordsD the new pirs re not dded to the existing onesD the reple themF. Input arguments M model E olved model ojetF Inp strut ell E snput dt on whih the fx trends will e omputedF Range numeri E hte rnge on whih the fx trends will e omputedF. Input arguments Obj model tseries e e pee sstte E yne of the ss ojetsF Cmt hr E ser omment tht will e tthed to the ojetF.

Input arguments M model E wodel ojet whose equtions will e lter evluted y lling lhsmrhs P F D strut E snput dtse with oservtions on mesurement vrilesD trnsition vrilesD nd shoks on whih lhsmrhs P will e evlutedF Range numeri E hte rnge on whih lhsmrhs P will e evlutedF. Input arguments M model E wodel ojet whose likelihood funtion will e di'erentitedF D ell strut E snput dt from whih mesurement vriles will e tkenF Range numeri E hte rnge on whih the likelihood funtion will e evlutedF UW. Output arguments S strut E htse with shok reponse derivtives stowed in multivrite time seriesF.

Input arguments M model kwmodel E wodel or kwmodel ojet for whih the empty dtse will e retedF. Output arguments D strut E htse with n empty tseries ojet for eh vrile nd eh shokD nd n empty rry for eh prmeterF. Description sn the input prmeter dtseD ED you n provide the following four spei tions for eh prmeterX E. Input arguments M model E wodel ojet on whih urrent prmeteristion the system priors will e evlutedF S systempriors E ystem priors ojetsF.

Output arguments P numeri E winus log of system prior densityF C numeri E gontriutions of individul prios to the overll system prior densityF X numeri E lue of eh system property for whih prior hs een de ned in the system priors ojetD SF.


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Input arguments m model E wodel ojet whose solution will e expndedF k numeri E xumer of periods hedD tCkD up to whih the solution for ntiipted shoks will e expndedF VW. Input arguments M model E wodel ojet whose rryEround mE les written in underlying the model leA will e sved on the diskF. Description ee the ss model lnguge keyword!

Input arguments M model E wodel ojet for whih the deomposition will e omputedF Range numeri E heomposition dte rnge with the rst dte eign the rst forest periodF NPer numeri E xumer of periods for whih the deomposition will e omputedF. Output arguments X nmedmt numeri E erry with the solute ontriutions of individul shoks to totl vrine of eh vrilesF Y nmedmt numeri E erry with the reltive ontriutions of individul shoks to totl vrine of eh vrilesF List ellstr E vist of vriles in rows of the X n Y rrysD nd shoks in olumns of the X nd Y rrysF A strut E htse with the solute ontriutions onverted to time seriesF B strut E htse with the reltive ontriutions onverted to time seriesF.

Input arguments M model E wodel ojet for whih the frequeny response funtion will e omputedF Freq numeri E etor of frequenies for whih the response funtion will e omputedF. Output arguments F numeri E erry with frequeny responses of trnsition vriles in rowsA to mesurement vriles in olumnsAF List ell E vist of trnsition vriles in rows of the F mtrixD nd list of mesurement vriles in olumns of the F mtrixF. Input arguments M model E wodel ojet in whih the equtions will e serhed forF Label hr E iqution lel tht will e serhed forF Rexp hr E egulr expressions tht will e mthed ginst eqution lelsF.

Input arguments M model E olved model ojetF NPer numeri E vength of the hypothetil rnge for whih the pisher informtion will e omputedF List ellstr E vist of prmeters with respet to whih the likelihood funtion will e di'erentitedF. Output arguments F numeri E epproximtion of the pisher informtion mtrixF FF numeri E gontriutions of individul frequenies to the totl pisher informtion mtrixF Delta numeri E uroneker delt y whih the ontriutions in Fi need to e multiplied to sum up to FF Freq numeri E etor of frequenies t whih the pisher informtion mtrix is evlutedF.

Valid queries to model objects felow is the tegorised list of model properties nd ttriutes tht n e queriedGessed y the get funtionF xote tht letter y is used in vrious ontexts to denote mesurement vriles or equtionsD x trnsition vriles or equtionsD e shoksD p prmetersD g exogenous vrilesD d deterministi trend equtionsD l dynmi linksD nd r reporting equtionsF he property nmes re se insensitiveF. Input arguments M1D M2 model E gomptile model ojets tht will e ominedY the input models must e sed on the sme model leF.

Output arguments M model E yutput model ojet tht omines the input model ojets s multiple prmE eteristionsF. Input arguments M model E wodel ojet for whih the initil ondition responses will e simultedF Range numeri E hte rnge with the rst dte eing the shok dteF NPer numeri E xumer of periodsF. Output arguments W numeri E erry with frequeny responses of trnsition vriles in rowsA to shoks in olumnsAF List ell E vist of trnsition vriles in rows of the W mtrixD nd list of shoks in olumns of the W mtrixF.

Description he funtion ompres the nmes of ll vrilesD shoksD nd prmetersD nd the omposition of the stteEspe vetorsF. Output arguments flag true false E rue for vriles delred s logEliner in nonEliner modelF. Output arguments Flag true false E rue for input strings tht re vlid nmes in the model ojet MF. Output arguments Flag true false E rue if t lest one NaN vlue exists in the queried tegoryF List ellstr E vist of prmeters if lled with parametersA or vriles if lled with variablesA tht re ssigned xx in t lest one prmeteristionD or equtions if lled with derivativesA tht produe n xx derivtive in t lest one prmeteristionF.

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Output arguments flag true false E rue for eh prmeteristion for whih stle unique solution hs een found nd exists urrently in the model ojetF. Input arguments M model E wodel ojetF Expn hr E ext string with n expression desriing omintion of trnsition vriles to e testedF. Description hen djusting the men ndGor std devs of shoksD you n use rel nd imginry numers ot distinguish etween ntiipted nd unntiipted shoksX ny shok entered s n imginry numer is treted s n ntiipted hnge in the men of the shok distriutionY ny std dev of shok entered s n imginry numer indites tht the shok will e treted s ntiipted when onditioning the forest on the reduedEform tunesF the sme shok or its std dev n hve oth the rel nd the imginry prtF.

D strut E snput dtse with oservtions on mesurement vrilesD trnsition vrilesD nd shoks on whih the disrepnies will e evlutedF Range numeri E hte rnge on whih the disrepnies will e evlutedF. Output arguments Q numeri E xumeri rry with disrepnies etween the vr nd r for eh model equtionF. Description he funtion lhsmrhs evlutes the disrepny etween the vr nd the r in eh model equtionY eh led is repled with the tul oservtion supplied in the input dtF he funtion lhsmrhs does not work for models with referenes to stedy stte vlues P55 F he rst syntxD with the rry YXE preEuilt in ll to data4lhsmrhs P78 is omputtionlly muh more e0ient if you need to evlute the vrEr disrepnies repetedly for di'erent prmeteristionsF IIS.

Input arguments M model E wodel ojet on whih the likelihood of the input dt will e evlutedF D strut ell E snput dtse or dtpk from whih the mesurement vriles will e tkenF Range numeri E hte rngeF. Input arguments M model E wodel on whih the filter or forecast funtion hs een runF D strut E trut or dtse returned from the filter or forecast funtionF. Input arguments fname hr ellstr E xme sA of the model le sA tht will loded nd onverted to new model ojetF m model E ixisting model ojet tht will e reuilt s if from model leF.

Example 1 ed in model ode le nmed my. Example 2 ed in model ode le nmed my. Plotting options ee help on grfun. Description sn the output dtseD DD eh prmeter is IEyEQ ell rryF he rst ell is vetor of prmeter vlues t whih the lol ehviour ws investigtedF he seond ell is mtrix with two olumn vetorsX the vlues of the overll minimised ojetive funtion s set up in the estimate P82 funtionAD nd the vlues of the dt likelihood omponentF he third ell is vetor of four numersX the prmeter estimteD the vlue of the ojetive funtion t optimumD the lower ound nd the upper oundF.

Output arguments OMG numeri E govrine mtrix of shoks or residuls sed on the urrently ssigned std devitions nd rossEorreltion oe0ientsF M model kwmodel E wodel or kwmodel ojet with new vlues for std devitions nd rossEorr oe0ients sed on the input ovrine mtrixF. Input arguments M model E wodel on whose ovrine mtries the popoltion regression will e sedF Lhs hr ellstr E vhs vriles in the regressionY eh of the vriles must e prt of the stteEspe vetorF Rhs hr ellstr E hs vriles in the regressionY eh of the vriles must e prt of the stteEspe vetorD or must refer to lrger lg of trnsition vrile present in the stteEspe vetorF.

Description opultion regressions lulted y this funtion re lwys entredF his mens the regressions re lwys lulted s if estimted on oservtions with their unondionl mens the stedyEstte levelsA removed from themF he vhs nd hs vriles tht re logEvriles must inlude log Input arguments M model E wodel ojet with reporting equtionsF D strut E snput dtse tht will e used to evlute the reporting equtionsF Range numeri E hte rnge on whih the reporting equtions will e evlutedF.

Description hen you use wild ootstrp for resmpling the initil onditionD the results re sed on n ssumption tht the men of the initil ondition is the symptoti men implied y the model iFeF the stedy stteAF. Output arguments in non-linear simulations Flag ell empty E gell rry with exit gs for nonElinerised simultionsF AddF ell empty E gell rry of tseries with nl ddEftors dded to rstEorder pproxiE mte equtions to mke nonEliner equtions holdF Discrep ell empty E gell rry of tseries with nl disrepnies etween vr nd r in equtions ermrked for nonEliner simultions y douleEequl signF. Input arguments m model E wodel ojets whose solution mtries will e onverted to single preisionF.

Input arguments M model E rmterised model ojetF xonEliner models must lso hve stedy stte vlues ssignedF. Description he ss solver uses n ordered or generlised hurA deomposition to integrte out future expettionsF he my very rrelyA fil for numeril resonsF ss inludes two pthes to hndle the some of the filuresX ixP pth umEofEiigenluesExerEwoAD nd n iPgP pth iigenvluesEooEgloseEoEwpAF he ixP pthX he model ontins two or more unit rootsD nd the lgorithm inE terprets some of them inorretly s pirs of eigenvlues tht sum up urtely to PD ut with one of them signi ntly elow I nd the other signi ntly ove IF ss reples the entries on the digonl of one of the ftor mtries with numers tht evlute to two unit rootsF he iPgP pthX he reEordering of thq mtries fils with wrning Reordering failed because some eigenvalues are too close to swap.

Input arguments M model E wodel ojet whose shok responses will e simultedF Range numeri E imultion dte rnge with the rst dte eing the shok dteF NPer numeri E xumer of simultion periodsF. Input arguments M model E wodel ojet for whih the sstte dtse will e retedF Range numeri E sntended simultion rngeY the stedyEstte or lnedEgrowthEpth dtse will e reted on rnge tht lso utomtilly inludes ll the neessry lgsF NCol numeri E xumer of olumns for eh vrileY the input rgument NCol n e only used with singleEprmeteristion modelsF. Input arguments m model E wodel ojet whose std devitions will e reEsledF factor numeri E ptor y whih ll the model std devitions will e reEsledF.

Syntax for assigning parameter values or steady-state values M. Syntax for assigning std deviations or cross-correlations of shocks M. Input arguments M model system t E wodel or system t ojet tht will e ssigned new prmeteristions or new prmeter vlues or new stedyEstte vluesF IRQ. Output arguments M model system t E wodel or system t ojet with newly ssigned or deleted prmeterE istionsD or with newly ssigned prmetersD or stedyEstte vluesF. Syntax to retrieve a std deviation or a cross-correlation of shocks M.

Input arguments M model E wodel ojet for whih the we representtion will e omputedF P numeri E yrder up to whih the we will e evlutedF. Output arguments Phi nmedmt numeri E we mtriesF List ell E vist of mesurement nd trnsition vriles in the rows of the Phi mtrixD nd list of shoks in the olumns of the Phi mtrixF. Input arguments M model E wodel ojet for whih the zero dtse will e retedF Range numeri E sntended simultion rngeY the zero dtse will e reted on rnge tht lso utomtilly inludes ll the neessry lgsF NCol numeri E xumer of olumns for eh vrileY the input rgument NCol n e only used on models with one prmeteristionF.

Setting up simulation plans autoexogenise P E ixogenise vriles nd utomtilly endogenise orresponding shoksF condition P E gondition forest upon the spei ed vriles t the spei ed dtesF endogenise P E indogenise shoks or reEendogenise vriles t the spei ed dtesF exogenise P E ixogenise vriles or reEexogenise shoks t the spei ed dtesF nonlinearise P E elet equtions for simultion in n ext nonEliner modeF. Input arguments P pln E imultion plnF List ellstr hr E vist of vriles tht will e exogenisedY these vriles must hve their orresponding shoks ssignedD see!

Output arguments P pln E imultion pln with new informtion on exogenised vriles nd endogenised shoks inludedF. Input arguments P pln E imultion plnF List ellstr hr E vist of vriles upon whih forest will e onditionedF Dates numeri E htes t whih the forest will e onditioned upon the spei ed vrilesF. Description sf you supply lso the seond input rgumentD the input dtse DD oth the dtes nd the reE spetive vlues will e reported for exogenised nd onditioning dt pointsD nd the vlues will e heked for the presene of xxs with wrning should there e found nyAF. Output arguments N numeri E xumer of onditioning dt pointsY eh vrile t eh dte ounts s one dt pointF.

Output arguments N numeri E xumer of exogenised dt pointsY eh vrile t eh dte ounts s one dt pointF. Output arguments P pln E imultion pln with informtion on ext nonEliner simultion inludedF. Input arguments M model E wodel ojet tht will e simulted sujet to this simultion plnF Range numeri E imultion rngeY this rnge must extly orrespond to the rnge on whih the model will e simultedF. Description ou need to use simultion pln ojet to set up the following types of more omplex simultions or foretsX.

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Getting information about system priors detail P E hisply detils of system priorsF isempty P E rue if the system priors ojet is emptyF length P E xumer or priors imposed in system priors ojetF. Input arugments S systempriors E ystem priors ojetF Expr hr E ixpression tht de nes vlue for whih prior density will e de nedY see hesription for system properties tht n e referred to in the expressionF PriorFunc funtionhndle empty E puntion hndle returning the log of prior densityY emptyD []D mens uniform priorF.

Input arguments M model E wodel ojet on whose system properties the priors will e imposedF. Evaluating posterior density arwm P E edptive rndomEwlk wetropolis posterior simultorF eval P E ivlute posterior density t spei ed pointsF. Output arguments X numeri E he vlue of log posterior density evluted t PY xFfF the returned vlue is log posteriorD nd not minus log posteriorF L numeri E gontriution of dt likelihood to log posteriorF PP numeri E gontriution of prmeter priors to log posteriorF SrfP numeri E gontriution of shok response funtion priors to log posteriorF FrfP numeri E gontriution of frequeny response funtion priors to log posteriorF.

Getting function handles for univariate distributions normal P E grete funtion proportionl to log of norml distriutionF lognormal P E grete funtion proportionl to log of logEnorml distriutionF beta P E grete funtion proportionl to log of et distriutionF gamma P E grete funtion proportionl to log of gmm distriutionF invgamma P E grete funtion proportionl to log of invEgmm distriutionF uniform P E grete funtion proportionl to log of uniform distriutionF. Calling the logdist function handles he funtion hndles F reted y the logdist pkge funtions n e lled the following wysX qet vlue proportionl to the logEdensity of the respetive distriution t prtiulr pointY this ll is used within the posterior simultor P X y a p xA qet the density of the respetive distriution t prtiulr pointX y a p xDpdf9A qet the hrteristis of the distriution!

Output arguments F funtionhndle E puntion hndle returning vlue proportionl to the log of the et densityF. Output arguments F funtionhndle E puntion hndle returning vlue proportionl to the log of the gmm densityF. Output arguments F funtionhndle E puntion hndle returning vlue proportionl to the log of the invEgmm densityF. Output arguments F funtionhndle E puntion hndle returning vlue proportionl to the log of the logEnorml densityF. Output arguments F funtionhndle E puntion hndle returning vlue proportionl to the log of the norml densityF.

Output arguments F funtionhndle E rndle to funtion returning vlue tht is proportionl to the log of the uniform densityF.

Input parameters! Equations and assignments! Variables with steady state restricted to be positive! Syntax for a system of equations to be solved symbolically! Syntax for block of assignments! Input arguments S sstte E stte ojet uilt on stedyEstte leF Fname hr empty E pilenme of the ompiled mE le funtionY if not spei ed or empty the originl stedyEstte lenme will e used with n 9Fm9 extensionF.

Manipulating named matrices select P E elet sumtrix y referring to row nmes nd olumn nmesF transpose P E rnspose eh pge of mtrix with nmes rows nd olumnsF. Getting row and column names rownames P E xmes of rows in nmedmt ojetF colnames P E xmes of olumns in nmedmt ojetF. Sample characteristics cutoff nmedmtGuto' E ell opertors nd funtions ville for stndrd wtl mtries nd rrys iFeF doule ojetsA re lso ville for nmedmt ojetsF.

Input arguments X nmedmt E e nmedmt ojet n numeri rry with nmed rows nd olumnsA returned y some of the model funtionsF. Description xmedmt ojets re used y some of the ss funtions to preserve the nmes of vriles tht relte to individul rows nd olumnsD suh s in IWI. Stochastic properties acf P E eutoovrine nd utoorreltion funtions for e vrilesF fmse P E porest men squre error mtriesF vma P E wtries desriing the we representtion of e proessF xsf P E ower spetrum nd spetrl density funtions for e vrilesF. Input arguments V e E e ojet in whih the numer of premeteristions will e hngedF N numeri E xew numer of prmeteristionsF.

Output arguments V e E yutput e ojet tht omines the input e ojets s multiple prmeterE istionsF. Description sn most sesD you don9t hve to run the funtion infocrit s it is lled from within estimate immeditely fter new prmeteristion is retedF. Input arguments V e E e ojet to whih forest instruments will e ddedF Def hr ellstr E he nition string for the new onditioning instrumentsF Name hr E xme of the new onditiong instrumentF Exprn hr E ixpression de ning the new onditiong instrumentF Vec numeri E etor of oe0ents to omine the e vriles to rete the new ondiE tioning instrumentF.

D or horizontl ontentionD [V1,V2]F. Description linD iFeF nonEpnelD e sed ojets re reted y lling the onstrutor with one input rgumentX the list of vrilesF nel e sed ojets re reted y lling the onstrutor with two input rgumentsX the list of vrilesD nd the nmes of groups of dtF. Output arguments N numeri E xumer of dt points periodsA tted when estimting the e ojetF. Input arguments V e swr E istimted e from whih the tested residuls were otinedF Data tseries E e residulsD or e output dt inluding residulsD to e tested for utoorreltionF H numeri E est horizonY must e greter thn the order of the tested eF.

Description sn most sesD you don9t hve to run the funtion schur s it is lled from within estimate immeditely fter new prmeteristion is retedF. Description his funtion retes new empty e ojetF st is usully followed y the estimate P funtion to estimte the e prmeters on dtF. Input arguments V e E e ojet for whih the we mtries will e omputedF N numeri E yrder up to whih the we mtries will e omputedF. Output arguments X numeri E porest error vrine deomposition into solute ontriutions of residulsY solute ontriutions sum up to the totl vrineF Y numeri E porest error vrine deomposition into reltive ontriutions of residulsY reltive ontriutions sum up to 1F XX tseries E X onverted to tseries ojetF YY tseries E Y onverted to tseries ojetF.

Output arguments value F F F E lue of the queried propertyF ell properties essile through the get funtion in e ojets re lso essile in e ojetsF. Input arguments A e E e ojet with multiple prmeteristions tht will e sortedF Data tseries strut empty E e dt endogenous vriles nd struturl shoksAY the struturl shoks will e reEordered ording to the e prmeteristionsF ou n leve the input rgument Data emptyF SortBy hr E ext string tht will e evluted to ompute the riterion y whih the prmeteristions will e sortedY see hesription for how to write SortByF PQR.

Description he individul prmeteristions within the e ojet A re sorted y the sum squred distnes of seleted shok responses to the respetive medin reponsesF pormllyD the following riterion is evluted for eh prmeteristion. Example ort the prmeteristions y squred distne to medin of shok responses of ll vriles to the rst shok in the rst four periodsF he prmeteristion tht is losest to the medin responses. Constructing dummy observations covmat P E govrine mtrix prior dummy oservtions for fesF litterman P E vittermn9s prior dummy oservtions for fesF sumofcoeff P E hon et l sumEofEoe0ient prior dummy oservtions for fesF uncmean P E nonditionlEmen dummy or ims9 initil dummyA oservtions for fesF user P E serEsupplied prior dummy oservtions for fesF.

Input arguments C numeri E rior ovrine mtrix of residulsY if C is vetor it will e onverted to digonl mtrixF Rep numeri E he numer of times the dummy oservtions will e repetedF. Filtering and forecasting filter P E eEestimte the ftors y ulmn ltering the dt tking pee oe0ients s givenF forecast P E porest pee ftors nd oservlesF. Description his funtion retes new empty pee ojetF st is usully followed y the estimte P funtion to estimte the pee prmeters on dtF. Computing special dates daily dates only datbom P E feginning of month for the spei ed dily dteF datboq P E feginning of qurter for the spei ed dily dteF datboy P E feginning of yer for the spei ed dily dteF dateom P E ind of month for the spei ed dily dteF dateoq P E ind of qurter for the spei ed dily dteF dateoy P E ind of yer for the spei ed dily dteF.

Converting dates clp2dat P E gonvert text in system lipord to dtesF dat2char P E gonvert dtes to hrter rryF dat2charlist P E gonvert dtes to ommEseprted listF dat2clp P E gonvert dtes to text nd pste to system lipordF dat2dec P E gonvert dtes to their deiml representtionsF dat2str P E gonvert ss dtes to ell rry of stringsF dat2ypf P E gonvert ss seril dte numer to yerD period nd frequenyF dec2dat P E gonvert deiml numers to ss seril dte numersF str2dat P E gonvert strings to ss seril dte numersF.

Date comparison datcmp P E gompre two ss seril dte numersF datdiff P E xumer of periods etween two dtes with hek for dte frequenyF rngcmp P E gompre two ss dte rngesF. Output arguments D numeri E ss seril dte numers sed on the urrent ontent of the system lipord onverted y the str2dat P funtionF. Output arguments C hr E ghrter rry representing the input dtesY eh line of the rry represents one dte from DF.

Input arguments D numeri E ss seril dte numers tht will e onverted to hrter rry nd psted to the system lipordF. Output arguments C hr E ghrter rry representing the input dtes psted to the system lipordY eh line of the rry represents one dte from DF. Output arguments Boq numeri E hily seril dte numer for the rst dy of the sme qurter s DF. Input arguments startdate numeri E ss seril dte numer representing the strtdteF enddate numeri E ss seril dte numer representing the enddteY startdate nd enddate must e the sme frequenyF increment numeri E xumer of periods spei to eh frequenyA etween the dtes in the dte vetorF.

Description ou n use the olon opertor to rete ontinuous dte rnges euse the ss seril dte numers re designed so tht whtever the frequeny two onseutive dtes re represented y numers tht di'er extly y oneF. Input arguments dec numeri E heiml numers representing dtesF freq freq E hte frequenyF. Description en ss dte rnge is distint from vetor of dtes in tht only the rst nd the lst dtes mtterF yftenD dte rnges re ontext sensitiveF sn tht seD you n use -Inf for the strt dte mening the erliest possile dte in the given ontextA nd Inf for the end dte mening the ltest possile dte in the given ontextAD or simply Inf for the whole rnge mening from the erliest possile dte to the ltest possile dte in the given ontextAF.

Getting information about tseries objects daily P E glendr view of dily tseries ojetF enddate P E hte of the lst ville oservtion in tseries ojetF freq P E prequeny of tseries ojetF get P E uery tseries ojet propertyF length P E vength of tseries ojetF ndims P E xumer of dimensions in tseries ojet dtF size P E ize of tseries ojet dtF startdate P E hte of the rst ville oservtion in tseries ojetF yearly P E hisply tseries ojet one full yer per rowF. Referencing tseries objects subsasgn P E usripted ssignment for tseries ojetsF subsref P E usripted referene funtion for tseries ojetsF.

Estimation and sample characteristics xote tht most of the smple hrteristis re listed ove in the wths nd sttistis funtions nd opertiors setionF acf P E mple utoovrine nd utoorreltion funtionsF hpdi P E righest proility density intervlF chowlin P E ghowEvin distriution of lowEfrequeny oservtions over higherEfrequeny periodsF regress P E yrdinry or weighted lestEsqure regressionF. Manipulating tseries objects empty P E impty tseries ojet preserving its size in Pnd nd higher dimensionsF permute P E ermute dimensions of tseries ojetF redate P E ghnge time dimension of tseries ojetF reshape P E eshpe size of time series in Pnd nd higher dimensionsF resize P E glip tseries ojet down to spei ed dte rngeF sort P E ort tseries olumns y spei ed riterionF.

Converting tseries objects convert P E gonvert tseries ojet to di'erent frequenyF double P E eturn tseries oservtions s douleEpreision numeri rryF doubledata P E gonvert tseries oservtions to doule preisionF single P E eturn tseries oservtions s singleEpreision numeri rryF singledata P E gonvert tseries oservtions to single preisionF PVP.

Other tseries functions apct P E ennulised perent rte of hngeF bsxfun P E tndrd fpx implemented for tseries ojetsF cumsumk P E gumultive sum with kEperiod lepF destdise P E hestndrdise tseries ojet y pplying spei ed stndrd devition nd men to itF diff P E pirst di'ereneF interp P E snterpolte missing oservtionsF normalise P E xormlise or reseA dt to prtiulr dteF pct P E erent rte of hngeF round P E ound tseries dt to spei ed numer of deimlsF rmse P E gompute wi for given oservtions nd preditionsF stdise P E tndrdise tseries dt y sutrting men nd dividing y std devitionF windex P E imple weighted or hivisi indexF wmean P E eighted verge of time series oservtionsF.

Input arguments a numeri E rndle to xes in whih the grph will e plottedY if not spei edD the urrent xes will usedF range numeri E hte rngeY if not spei ed the entire rnge of the input tseries ojet will e plottedF x tseries E snput tseries ojet whose olumns will e ploted s n re grphF. Output arguments X tseries E yutput dt with new oservtions reted y running n utoregressive proess desried y A nd ZF.

Example he following two lines rete n utoregressive proess onstruted from normlly distriuted residulsD. Output arguments h numeri E rndles to the rs plottedF range numeri E etully plotted dte rngeF. Input arguments Ax numeri E rndle to xes in whih the grph will e plottedY if not spei edD the urrent xes will usedF Range numeri E hte rngeY if not spei ed the entire rnge of the input tseries ojet will e plottedF X tseries E snput tseries ojet whose olumns will e ploted s ontriution r grphF.

Input arguments Y1 tseries E vowEfrequeny input tseries ojet tht will e distriuted over higherEfrequeny oservtionsF X2 tseries E series ojet with regressors used to distriute the input dtF range numeri E vowEfrequeny dte rnge on whih the distriution will e omputedF. Description he funtion hndle tht you pss in through the method9 option when you ggregte the dt onvert higher frequeny to lower frequenyA should ehve like the uiltEin funtions meanD sum etF sn other wordsD it is expeted to ept two input rgumentsX the dt to e ggregtedD the dimension long whih the ggregtion is lultedF he funtion will e lled with the seond input rgument set to ID s the dt re proessed en lok olumnwiseF sf this ll filsD convert will ttempt to ll the funtion with just one input rgumentD the dtD ut this is not sfe option under some irumstnes sine dimension mismth my ourF.


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Input arguments X tseries E series ojet with indeterminte frequeny whose dte tiks will e interpreted s wtl seril dte numersF. Input arguments x tseries E series ojet whose oservtions will e returned s douleEpreision numeri rryF. Output arguments y numeri E houleEpreision numeri rry with the input tseries oservtions in olumnsF. Input arguments x tseries E series ojet whose oservtions will e e onverted to doule preisionF.

Output arguments x tseries E impty tseries ojet with the Pnd nd higher dimensions the sme size s the input tseries ojetD nd omments preservedF. Output arguments d numeri E ss seril dte numer representing the dte of the lst oservtion ville in the input tseriesF. Input arguments x tseries E snput tseries ojet tht will e trnsformedF range numeri snf E hte rngeF. Output arguments y numeri E pourier trnsform with dt orgnised in olumnsF range numeri E etully used dte rngeF freq numeri E prequenies orresponding to pp vetor elementsF per numeri E eriodiities orresponding to pp vetor elementsF.

Output arguments int tseries E yutput tseries ojet with two olumnsD iFeF lower ounds nd upper ounds for eh periodF. Output arguments n numeri E xumer of periods from the rst to the lst ville oservtion in the input tseries ojetF. Input arguments X tseries E series ojet on whose oservtions the funtion will e ppliedF Range numeri snf E nge on whih the moving funtion will e ppliedY Inf mens the entire rnge on whih the time series is de nedF.

Input arguments x tseries E snput tseries ojet tht will e normlisedF normdate numeri strt9 end9 nnstrt9 nnend9 E hte reltive to whih the input dt will e normlisedY if not spei edD nnstrt9 the rst dte for whih ll olumns hve n oservtionA will e usedF.


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  • Input arguments X tseries E series ojet whose dimensionsD exept the rst timeA dimensionD will e rerrnged in the order spei ed y the vetor orderF Order numeri E xew order of dimensionsY euse the time dimension nnot e permutedD order 1 must e lwys 1F. Input arguments a numeri E rndle to xes in whih the grph will e plottedY if not spei edD the urrent xes will usedF range numeri E hte rngeY if not spei ed the entire rnge of the input tseries ojet will e plottedF x tseries E snput tseries ojet whose olumns will e ploted s line grphF.

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