## SSANOVA, GSSANOVA, SSDEN, SSCDEN, SSLLRM, SSHZD, AND SSCOX SUITES
ssanova                     Fitting smoothing spline ANOVA models
predict.ssanova             Predicting from ssanova fits
predict1.ssanova            Predicting from ssanova fits
summary.ssanova             Summarizing ssanova fits
project.ssanova             Projecting ssanova fits for model diagnostic
ssanova9                    Fitting smoothing spline ANOVA models with correlated data
summary.ssanova9            Summarizing ssanova9 fits
project.ssanova9            Projecting ssanova9 fits for model diagnostic
ssanova0                    Fitting smoothing spline ANOVA models
predict.ssanova0            Predicting from ssanova0 fits
summary.ssanova0            Summarizing ssanova0 fits
residuals.ssanova           Extracting the residuals from ssanova objects
fitted.ssanova              Extracting the fitted values from ssanova objects
print.ssanova               Print function for ssanova objects
print.ssanova0              Print function for ssanova0 objects
print.summary.ssanova       Print function for summary.ssanova objects

gssanova                    Fitting smoothing spline ANOVA models with non Gaussian data
gssanova1                   Fitting smoothing spline ANOVA models with non Gaussian data
predict9.gssanova           Predicting from gssanova fits
summary.gssanova            Summarizing gssanova fits
project.gssanova            Projecting gssanova1 fits for model diagnostic
gssanova0                   Fitting smoothing spline ANOVA models with non Gaussian data
summary.gssanova0           Summarizing gssanova0 fits
residuals.gssanova          Extracting the residuals from gssanova objects
fitted.gssanova             Extracting the fitted values from gssanova objects
print.gssanova              Print function for gssanova objects
print.summary.gssanova      Print function for summary.gssanova objects
print.summary.gssanova0     Print function for summary.gssanova0 objects

ssden                       Estimating probability density using smoothing splines
d.ssden                     Evaluating pdf of ssden estimates
project.ssden               Projecting ssden fits for model diagnostic
ssden1                      Estimating probability density using smoothing splines
d.ssden1                    Evaluating pdf of ssden1 estimates
project.ssden1              Projecting ssden1 fits for model diagnostic
dssden                      Evaluating pdf of ssden estimates
pssden                      Evaluating cdf of 1-D ssden estimates
qssden                      Evaluating quantiles of 1-D ssden estimates
cdssden                     Evaluating conditional pdf of ssden estimates
cpssden                     Evaluating 1-D conditional cdf of ssden estimates
cqssden                     Evaluating 1-D conditional quantiles of ssden estimates
print.ssden                 Print function for ssden objects
sscomp                      Estimating composition with one sample
sscomp2                     Estimating composition with multiple samples

sscden                      Estimating conditional density using smoothing splines
d.sscden                    Evaluating pdf of sscden estimates
project.sscden              Projecting sscden fits for model diagnostic
sscden1                     Estimating conditional density using smoothing splines
d.sscden1                   Evaluating pdf of sscden1 estimates
project.sscden1             Projecting sscden1 fits for model diagnostic
dsscden                     Evaluating pdf of sscden estimates
psscden                     Evaluating cdf of sscden estimates with 1-D Y
qsscden                     Evaluating quantiles of ssden estimates with 1-D Y
cdsscden                    Evaluating conditional pdf of sscden estimates
cpsscden                    Evaluating 1-D conditional cdf of sscden estimates
cqsscden                    Evaluating 1-D conditional quantiles of sscden estimates
print.sscden                Print function for sscden objects

ssllrm                      Fitting smoothing spline log-linear regression models
predict.ssllrm              Evaluating log-linear regression model fits
project.ssllrm              Projecting ssllrm fits for model diagnostic
print.ssllrm                Print function for ssllrm objects

sshzd                       Estimating hazard function using smoothing splines
project.sshzd               Projecting sshzd fits for model diagnostic
sshzd1                      Estimating hazard function using smoothing splines
project.sshzd1              Projecting sshzd1 fits for model diagnostic
hzdrate.sshzd               Evaluating hazard estimates
hzdcurve.sshzd              Evaluating hazard curves
survexp.sshzd               Computing expected survivals
print.sshzd                 Print function for sshzd objects

sscox                       Estimating relative risk using smoothing splines
predict.sscox               Projecting sscox fits for model diagnostic
project.sscox               Predicting from sscox fits
print.sscox                 Print function for sscox objects

## SSCOPU, SSCOPU2, SSHZD2D, AND SSHZD2D1 SUITES
sscopu                      Fitting copula density using smoothing splines
sscopu2                     Fitting 2-D copula density using smoothing splines
dsscopu                     Evaluating pdf of sscopu estimates
cdsscopu                    Evaluating 1-D conditional pdf of sscopu estimates
cpsscopu                    Evaluating 1-D conditional cdf of sscopu estimates
cqsscopu                    Evaluating 1-D conditional quantiles of sscopu estimates
summary.sscopu              Calculating Kendall's tau and Spearman's rho of sscopu estimates
print.sscopu                Print function for sscopu objects

sshzd2d                     Estimating 2-D hazard function using smoothing splines
sshzd2d1                    Estimating 2-D hazard function using smoothing splines
hzdrate.sshzd2d             Evaluating 2-D hazard estimates
survexp.sshzd2d             Evaluating 2-D survival estimates
print.sshzd2d               Print function for sshzd2d objects

## UTILITIES FOR MAKING MODEL TERMS
mkterm                      Making model terms
mkterm.copu                 Making model terms for sscopu/sscopu2

mkphi.cubic                 Making phi function for cubic splines
mkrk.cubic                  Making RK function for cubic splines
mkrk.cubic.per              Making RK function for periodic cubic splines
mkrk.linear                 Making RK function for linear splines
mkrk.linear.per             Making RK function for periodic linear splines

mkphi.tp                    Making phi functions for thin-plate splines
mkphi.tp.p                  Making pseudo phi functions for thin-plate splines
mkrk.tp                     Making RK functions for thin-plate splines
mkrk.tp.p                   Making pseudo RK functions for thin-plate splines
mkrk.sphere                 Making RK functions for spherical splines

mkrk.nominal                Making RK function for nominal factors
mkrk.ordinal                Making RK function for ordinal factors

mkran                       Generating random effects in mixed-effect models
mkran1                      Combining random effects in mixed-effect models

mkcov.arma                  Making covariance function for ARMA models
mkcov.long                  Making covariance function for longitudinal data
mkcov.known                 Passing known covariance function to ssanova9

mkint                       Generating integrals of basis terms for ssden1 suite
mkint2                      Generating integrals of basis terms for ssden1 suite

## UTILITIES FOR DISTRIBUTION FAMILIES
mkdata.binomial             Making pseudo data for logistic regression
dev.resid.binomial          Deviance residuals for logistic regression
dev.null.binomial           Null model deviance for logistic regression
cv.binomial                 CV score for logistic regression
y0.binomial                 Preparing for KL projection of logistic fit
proj0.binomial              Making pseudo data for projection of logistic fit
kl.binomial                 Computing KL distance between logistic fits
cfit.binomial               Computing constant logistic fit

mkdata.poisson              Making pseudo data for Poisson regression
dev.resid.poisson           Deviance residuals for Poisson regression
dev.null.poisson            Null model deviance for Poisson regression
cv.poisson                  CV score for Poisson regression
y0.poisson                  Preparing for KL projection of Poisson fit
proj0.poisson               Making pseudo data for projection of Poisson fit
kl.poisson                  Computing KL distance between Poisson fits
cfit.poisson                Computing constant Poisson fit

mkdata.Gamma                Making pseudo data for Gamma regression
dev.resid.Gamma             Deviance residuals for Gamma regression
dev.null.Gamma              Null model deviance for Gamma regression
cv.Gamma                    CV score for Gamma regression
y0.Gamma                    Preparing for KL projection of Gamma fit
proj0.Gamma                 Making pseudo data for projection of Gamma fit
kl.Gamma                    Computing KL distance between Gamma fits
cfit.Gamma                  Computing constant Gamma fit

mkdata.inverse.gaussian     Making pseudo data for IG regression
dev.resid.inverse.gaussian  Deviance residuals for IG regression
dev.null.inverse.gaussian   Null model deviance for IG regression
cv.inverse.gaussian         CV score for IG regression
y0.inverse.gaussian         Preparing for KL projection of IG fit
proj0.inverse.gaussian      Making pseudo data for projection of IG fit
kl.inverse.gaussian         Computing KL distance between IG fits
cfit.inverse.gaussian       Computing constant IG fit

mkdata.nbinomial            Making pseudo data for negative binomial regression
dev.resid.nbinomial         Deviance residuals for negative binomial regression
dev.null.nbinomial          Null model deviance for negative binomial regression
cv.nbinomial                CV score for negative binomial regression
y0.nbinomial                Preparing for KL projection of negative binomial fit
proj0.nbinomial             Making pseudo data for projection of negative binomial fit
kl.nbinomial                Computing KL distance between negative binomial fits
cfit.nbinomial              Computing constant negative binomial fit

mkdata.polr                 Making pseudo data for proportional odds regression
dev.resid.polr              Deviance residuals for proportional odds regression
dev.null.polr               Null model deviance for proportional odds regression
cv.polr                     CV score for proportional odds regression
y0.polr                     Preparing for KL projection of proportional odds fit
proj0.polr                  Making pseudo data for projection of proportional odds fit
kl.polr                     Computing KL distance between proportional odds fits
cfit.polr                   Computing constant proportional odds fit

mkdata.weibull              Making pseudo data for Weibull regression
dev.resid.weibull           Deviance residuals for Weibull regression
dev.null.weibull            Null model deviance for Weibull regression
cv.weibull                  CV score for Weibull regression
y0.weibull                  Preparing for KL projection of Weibull fit
proj0.weibull               Making pseudo data for projection of Weibull fit
kl.weibull                  Computing KL distance between Weibull fits
cfit.weibull                Computing constant Weibull fit

mkdata.lognorm              Making pseudo data for log normal regression
dev.resid.lognorm           Deviance residuals for log normal regression
dev0.resid.lognorm          Pseudo deviance residuals for log normal regression
dev.null.lognorm            Null model deviance for log normal regression
cv.lognorm                  CV score for log normal regression
y0.lognorm                  Preparing for KL projection of log normal fit
proj0.lognorm               Making pseudo data for projection of log normal fit
kl.lognorm                  Computing KL distance between log normal fits
cfit.lognorm                Computing constant log normal fit

mkdata.loglogis             Making pseudo data for log logistic regression
dev.resid.loglogis          Deviance residuals for log logistic regression
dev0.resid.loglogis         Pseudo deviance residuals for log logistic regression
dev.null.loglogis           Null model deviance for log logistic regression
cv.loglogis                  CV score for log logistic regression
y0.loglogis                  Preparing for KL projection of log logistic fit
proj0.loglogis               Making pseudo data for projection of log logistic fit
kl.loglogis                  Computing KL distance between log logistic fits
cfit.loglogis                Computing constant log logistic fit

## UTILITIES FOR NUMERICAL INTEGRATION
gauss.quad                  Generating Gauss-Legendre quadrature
smolyak.quad                Generating Smolyak cubature
smolyak.size                Getting the size of Smolyak cubature

## UTILITY FOR OPTIMIZATION
nlm0                        Minimizing univariate functions on finite intervals

## NUMERICAL ENGINE
sspreg0                     An interface to RKPACK driver DSIDR
mspreg0                     An interface to RKPACK driver DMUDR
sspregpoi                   Performance-oriented iteration using RKPACK driver DSIDR
mspregpoi                   Performance-oriented iteration using RKPACK driver DMUDR
getcrdr                     An interface to RKPACK utility DCRDR
getsms                      An interface to RKPACK utility DSMS

sspreg1                     Compute regression estimate with single smoothing parameter
mspreg1                     Compute regression estimate with multiple smoothing parameters
sspreg91                    Compute regression estimate with single smoothing parameter
mspreg91                    Compute regression estimate with multiple smoothing parameters
sspngreg                    Compute NG regression estimate with single smoothing parameter
mspngreg                    Compute NG regression estimate with single smoothing parameter
ngreg                       Newton iteration for NG regression with fixed smoothing parameter
ngreg1                      Performance-oriented iteration using sspreg1 and mspreg1
regaux                      Obtain auxiliary information needed for se calculation
ngreg.proj                  Calculate Kullback-Leibler projection for NG regression

sspdsty                     Compute density estimate with single smoothing parameter
mspdsty                     Compute density estimate with multiple smoothing parameters
sspdsty1                    Compute density estimate with single smoothing parameter
mspdsty1                    Compute density estimate with multiple smoothing parameters
mspcdsty                    Compute conditional density estimate
mspcdsty1                   Compute conditional density estimate
msphzd                      Compute hazard estimate with single or multiple smoothing parameters
msphzd1                     Compute hazard estimate with single or multiple smoothing parameters
sspcox                      Compute relative risk estimate with single smoothing parameter
mspcox                      Compute relative risk estimate with multiple smoothing parameters

mspllrm                     Compute log-linear regression model with multiple smoothing parameters

mspcopu2                    Compute 2-D copula density estimate under censoring/truncation
