abc |
Parameter estimation with Approximate Bayesian Computation (ABC) |
abc.return |
Parameter estimation with Approximate Bayesian Computation (ABC) |
cv4abc |
Cross validation for Approximate Bayesian Computation (ABC) |
cv4postpr |
Leave-one-our cross validation for model selection ABC |
expected.deviance |
Expected deviance |
getmode |
Summaries of posterior samples generated by ABC algortithms |
gfit |
Goodness of fit |
gfitpca |
Goodness of fit with principal component analysis |
hist.abc |
Posterior histograms |
human |
A set of R objects containing observed data from three human populations, and simulated data under three different demographic models. The data set is used to illustrate model selection and parameter inference in an ABC framework (see the package's vignette for more details). |
models |
A set of R objects containing observed data from three human populations, and simulated data under three different demographic models. The data set is used to illustrate model selection and parameter inference in an ABC framework (see the package's vignette for more details). |
musigma2 |
A set of objects used to estimate the population mean and variance in a Gaussian model with ABC. |
par.italy.sim |
A set of R objects containing observed data from three human populations, and simulated data under three different demographic models. The data set is used to illustrate model selection and parameter inference in an ABC framework (see the package's vignette for more details). |
par.sim |
A set of objects used to estimate the population mean and variance in a Gaussian model with ABC. |
plot.abc |
Diagnostic plots for ABC |
plot.cv4abc |
Cross-validation plots for ABC |
plot.cv4postpr |
Barplot of model misclassification |
plot.gfit |
Goodness-of-fit plot for ABC |
post.bott |
Data to illustrate the posterior predictive checks for the data 'human'. 'ppc' and 'human' are used to illustrate model selection and parameter inference in an ABC framework (see the package's vignette for more details). |
post.mu |
A set of objects used to estimate the population mean and variance in a Gaussian model with ABC. |
post.sigma2 |
A set of objects used to estimate the population mean and variance in a Gaussian model with ABC. |
postpr |
Estimating posterior model probabilities |
ppc |
Data to illustrate the posterior predictive checks for the data 'human'. 'ppc' and 'human' are used to illustrate model selection and parameter inference in an ABC framework (see the package's vignette for more details). |
stat.3pops.sim |
A set of R objects containing observed data from three human populations, and simulated data under three different demographic models. The data set is used to illustrate model selection and parameter inference in an ABC framework (see the package's vignette for more details). |
stat.obs |
A set of objects used to estimate the population mean and variance in a Gaussian model with ABC. |
stat.sim |
A set of objects used to estimate the population mean and variance in a Gaussian model with ABC. |
stat.voight |
A set of R objects containing observed data from three human populations, and simulated data under three different demographic models. The data set is used to illustrate model selection and parameter inference in an ABC framework (see the package's vignette for more details). |
summary.abc |
Summaries of posterior samples generated by ABC algortithms |
summary.cv4abc |
Calculates the cross-validation prediction error |
summary.cv4postpr |
Confusion matrix and misclassification probabilities of models |
summary.gfit |
Calculates the p-value of the goodness-of-fit test. |
summary.postpr |
Posterior model probabilities and Bayes factors |