Functions for Bayes inference

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Documentation for package ‘cbw’ version 1.0

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B C D E F G L M O P R S T

cbw-package Functions for Bayes inference

-- B --

betapanelsim betapanelsim generates beta in the gaussian panel model

-- C --

calculateprm calculateprm calculates the prm given prices on an equity asset
cbw Functions for Bayes inference

-- D --

dg1 dg1 calculates the log of the Gamma density
dig1 dig1 calculates the log of the Inverse-Gamma density
dinvpanelsim dinvpanelsim generates Dinv in the gaussian panel model
dmvn1 dmvn1 calculates the density of MVN on the log-scale
dmvt1 dmvt1 calculates the density of MVt on the log-scale
dt1 dt1 calculates the log density of the independent student t densities
dwish1 dwish1 density of the wishart on the log-scale

-- E --

extracttunabrand extracttunabrand extracts data from the tuna (bayesm package) data.frame

-- F --

ff3download ff3download downloads Fama-French weekly data
ff3mdownload ff3download downloads Fama-French monthly data
ffmommdownload ffmommdownload downloads Fama-French monthly momemtum data
finddensityordinate finddensityordinate calculates the log ordinate of a kernel density estimate given a point
findtriangular findtriangular calculates a discrete triangular distribution

-- G --

getdat getdat gets weekly equity data from Yahoo
getfinmdat getfinmdat gets and returns monthly prm of funds and the sp500 index plus FF5 factors
getfinwdat getfinwdat gets and returns weekly prm of funds and the sp500 index plus FF3 factors
getmdat getmdat gets monthly equity data from Yahoo
getmrskfree getmrskfree gets monthly 3-month tbill data from Yahoo
getrskfree getrskfree gets weekly 3-month tbill data from Yahoo
ggtsplot ggtsplot plots one or more series

-- L --

lagdf lagdf takes lags of columns of data.frame
lagnew lagnew calculates the lag of a vector or a ts or xts object
lamregsim lamregsim generates lambda in the grouped prior reg model
lamstsim lamstsim generates lambda in the student-t reg model
likmus2 likmus2 calculates the log likelihood value for a sample from N(mu,s2)
logmarglik logmarglik extracts the log marginal likelihood from MCMC output

-- M --

makebayesportfolioaftercapmsv 'makebayesportfolioaftercapmsv' make a Bayesian mean-variance portfolio from the CAPM implied covariance matrix
makebayesportfolioaftersureg 'makebayesportfolioaftersureg' make a Bayesian mean-variance portfolio after fitting MCMCsureg
makebayesportfolioaftersuret 'makebayesportfolioaftersuret' make a Bayesian mean-variance portfolio after fitting MCMCsuret
makedatdfls makedatdfls is a utilty function
makehierpaneldat makehierpaneldat makes the y, X, W and ind data in the hierarchical gaussian panel model
makekscmixt makekscmixt makes the Kim, Shepard and Chib (1998) mixture parameters
makemodelformulas 'makemodelformulas' makes formulas from all possibles of xnames
makepaneldat makepaneldat makes the y, X, W and ind data in the gaussian panel model
makeplot makeplot is a utility function to do nonpar plotting
makesubsets 'makesubsets' makes all subsets of names in a data-frame
marglik marglik extracts the log marginal likelihood from MCMC output
maximize maximize maximizes the function loglik over theta - this is a wrapper to the optim function
MCMCbinprobit 'MCMCbinprobit' Bayesian Albert-Chib (1993) estimation of the binary probit model
MCMCheterog MCMCheterog does Bayes estimation of the heteroskedastic model by Kim, Shephard and Chib (1998) and the Chib and Greenberg (2013) method
MCMCheterot MCMCheterot does Bayes estimation of the heteroskedastic student-t model by Kim, Shephard and Chib (1999) and the Chib and Greenberg (2013) method
MCMChetpanelg MCMChetpanelg does Bayes estimation of the Gaussian panel model by Algorithm 2 of Chib and Carlin (1999)
MCMChierg MCMChierg does Bayes estimation of the Gaussian hierarchical by Algorithm 2 of Chib and Carlin (1999)
MCMCnonpar MCMCnonpar does Bayes estimation of the nonparametric model by the Chib and Greenberg (2010) method
MCMCpanelg MCMCpanelg does Bayes estimation of the Gaussian panel model by Algorithm 2 of Chib and Carlin (1999)
MCMCregpoiss MCMCregpoiss does Bayes estimation of the Poisson reg model
MCMCregresschangeg MCMCregresschangeg does Bayes estimation of the Gaussian change-point regression by the Chib (1998) method
MCMCregressdpm MCMCregressdpm does Bayes estimation of the DPM reg model with student-t G0
MCMCregressg MCMCregressg does Bayes estimation of the Gaussian reg model
MCMCregressgc MCMCregressgc does Bayes estimation of the Gaussian reg model under a conjugate prior
MCMCregresst MCMCregresst does Bayes estimation of the student-t reg model
MCMCregresstg MCMCregresstg does Bayes estimation of the Gaussian reg model under a t prior on beta
MCMCregresstt MCMCregresstt does Bayes estimation of the student-t reg model under a t prior on beta
MCMCsingleindexg MCMCsingleindexg does Bayes estimation of the semiparametric single index model with heteroskedasticity
MCMCsureg MCMCsureg does Bayes estimation of the Gaussian sure model
MCMCsuret MCMCsuret does Bayes estimation of the student-t sure model
MCMCsvg 'MCMCsvg' esimates the Bayesian stochastic volatility model using Kim, Shephard and Chib (1998) and Chib, Nardari and Shephard (2002)
minimize minimize minimizes the function loglik (sent in as negative loglik) over theta - this is a wrapper to the ucminf function
modelorder modelorder summarizes the models by posterior model probabilities
mvrmat mvrmat generate the multivariate regression y and X matrices

-- O --

orderby orderby orders a matrix or data.frame by the values in a given column

-- P --

paramg paramg calculates the gamma(a/2,b/2) distribution given mean and sd
paramg0 paramg0 calculates the gamma(shape,rate) distribution given mean and sd
paramig paramig calculates the inverse-gamma(nu/2,delta/2) distribution given mean and sd
paramlogn paramlogn calculates the parameters of the log-normal distribution
pdfavg pdfavg calculates the mean of densities given log densities
plot.nonpar plot.nonpar does plotting of nonparametric function estimates from MCMCnonpar
plotbayesportfolio 'plotbayesportfolio' plots the portfolio output
plotden plotden does kernel density plots
plotdf plotdf plots a single or multiple time series which are in a data.frame date type objects
plotdisc plotdisc does discrete distribution plots
plotnormalt plotnormalt does normal and student-t density plots
plotxts plotxts plots a single or multiple time series which can be xts objects or not
postmod postmod calculates the posterior model probabilities from log marginal likelihoods
predictpanelg predictpanelg generates predictions from a gaussian panel model
predictregressg predictregressg generates predictions from a gaussian regression
predictregresst predictregresst generates predictions from a student-t regression
predictsureg predictsureg generates predictions from a gaussian SURE
predictsuret predictsuret generates predictions from a gaussian SURE

-- R --

rgamss rgamss Sample small-shape gamma random variables via accept-reject
rigamma rigamma generates a random draw from the inverse-gamma distribution
rtg rtg samples a t-distribution with a parameter for dispersion
rwish rwish generates a random Wishart draw

-- S --

s2panelsim s2panelsim generates s2 in the gaussian panel model
sample2 sample2 generates draws from a bivariate discrete distribution
sample3 sample3 generates draws from a trivariate discrete distribution
samplethepriormus2 samplethepriormus2 samples the prior and then outcomes from a N(mu,s2) model
samplethepriornonpar samplethepriornonpar samples the prior and then outcomes in the Gaussian nonparametric model by the Chib and Greenberg (2010) method
samplethepriorpanelg samplethepriorpanelg samples the prior and then outcomes from a Gaussian panel model
samplethepriorregg samplethepriorregg samples the prior and then outcomes from a regression model
samplethepriorsureg samplethepriorsureg samples the prior and then outcomes from a Gaussian SURE model
sigma sigma calculates the draws of sigma from the MCMCregressg and MCMCregresst functions
subsetthetam subsetthetam subsets rows and columns of MCMC draws
summary.cbw summary.cbw produces summary given MCMC draws from cbw package
summaryb summaryb produces a basic summary
summarycorr summarycorr gives posterior mean and posterior sd of correlation parameters in sureg and suret models
summarymcmc summarymcmc produces summary given MCMC draws from cbw package
suremat suremat generate the sure y and X matrices
surematz surematz generate the sure y and X matrices in the Zellner format

-- T --

tau2regsim tau2regsim generates tau2 in the gaussian reg model
tau2stsim tau2stsim generates tau2 in the student-t reg model
theme_white theme_white is a utility function
topmodels topmodels orders models in a list given a vector of log marginal likelihoods
trainpriormvrg trainpriormvrg calculates the training sample prior in the Gaussian MVR model
trainpriorpanel trainpriorpanel calculates the training sample prior in the Gaussian panel model
trainpriorregg trainpriorregg calculates the training sample prior in the Gaussian regression model
trainpriorsureg trainpriorsureg calculates the training sample prior in the Gaussian sure model