Package index • tidyabc

ABC workflow

abc_adaptive(): Perform ABC sequential adaptive fitting
abc_rejection(): Perfom simple ABC rejection algorithm
abc_smc(): Perform ABC sequential Monte Carlo fitting
calculate_rmse(): Generate a function to calculate a Root Mean Squared Error (RMSE)
calculate_wasserstein(): Calculate a Wasserstein distance
default_termination_fn(): Set up default convergence criteria for SMC and adaptive ABC
fixed_wave_termination_fn(): Run the SMC or adaptive algorithm for a set number of waves
plot_convergence(): Plot convergence metrics by wave for SMC and adaptive ABC
plot_correlations(): A parameter posterior correlation plot
plot_evolution(): Plot the evolution of the density function by wave for SMC and adaptive ABC
plot_simulations(): Spaghetti plot of resampled posterior fits
posterior_distance_metrics(): Generate a set of metrics from component scores
posterior_fit_analytical(): Fit analytical distribution to posterior samples for generating more waves
posterior_fit_empirical(): Fit empirical distribution to posterior samples for generating more waves
posterior_resample(): Generate a set of samples from selected posteriors
posterior_summarise(): Calculate a basket of summaries from a weighted list of posterior samples
priors(): Construct a set of priors
test_simulation(): Run the simulation for one set of parameters
wasserstein_calculator() experimental: Generate a function to calculate a wasserstein distance

Empirical, mixture and truncated distributions

empirical(): Fit a piecewise logit transformed linear model to cumulative data
empirical_cdf(): Fit a piecewise logit transformed linear model to a CDF
empirical_data(): Fit a piecewise logit transformed linear model to weighted data
kurtosis(): Calculate the excess kurtosis of a set of data
mixture(): Construct a mixture distribution
skew(): Calculate the skew of a set of data
transform(): Generate a distribution from a link transform of another
truncate(): Generate a distribution from a truncation of another
wbw.nrd(): Weighted bandwidth selector
widen(): Increase the dispersion of a distribution
wmean(): Weighted mean
wquantile(): Quantile from weighted data with link function support
wsd(): Weighted standard deviation

Reparameterised and non-standard distributions

dbeta2(): The Beta Distribution
dcgamma(): Density: gamma distribution constrained to have mean > sd
dgamma2(): The Gamma Distribution
dlnorm2(): The Log Normal Distribution
dlogitnorm(): Logit-normal distribution
dlogitnorm2(): Logit-normal distribution
dnbinom2(): The Negative Binomial Distribution
dnull(): Null distributions always returns NA
dwedge(): Wedge distribution
pbeta2(): The Beta Distribution
pcgamma(): Cumulative probability: gamma distribution constrained to have mean > sd
pgamma2(): The Gamma Distribution
plnorm2(): The Log Normal Distribution
plogitnorm(): Logit-normal distribution
plogitnorm2(): Logit-normal distribution
pnbinom2(): The Negative Binomial Distribution
pnull(): Null distributions always returns NA
pwedge(): Wedge distribution
qbeta2(): The Beta Distribution
qcgamma(): Quantile: gamma distribution constrained to have mean > sd
qgamma2(): The Gamma Distribution
qlnorm2(): The Log Normal Distribution
qlogitnorm(): Logit-normal distribution
qlogitnorm2(): Logit-normal distribution
qnbinom2(): The Negative Binomial Distribution
qnull(): Null distributions always returns NA
qwedge(): Wedge distribution
rbern(): A random Bernoulli sample as a logical value
rbeta2(): The Beta Distribution
rcategorical(): Sampling from the multinomial equivalent of the Bernoulli distribution
rcgamma(): Sampling: gamma distribution constrained to have mean > sd
rexpgrowth(): Randomly sample incident times in an exponentially growing process
rexpgrowthI0(): Randomly sample incident times in an exponentially growing process with initial case load
rgamma2(): The Gamma Distribution
rlnorm2(): The Log Normal Distribution
rlogitnorm(): Logit-normal distribution
rlogitnorm2(): Logit-normal distribution
rnbinom2(): The Negative Binomial Distribution
rnull(): Null distributions always returns NA
rwedge(): Wedge distribution
wedge: Wedge distribution

S3 abc fit class

new_abc_fit() format(<abc_fit>) summary(<abc_fit>) tidy.abc_fit() print(<abc_fit>) plot(<abc_fit>): abc_fit S3 class

S3 abc prior class

new_abc_prior() as.abc_prior() is.abc_prior() format(<abc_prior>) print(<abc_prior>) plot(<abc_prior>): abc_prior S3 class

S3 distribution function class

as.dist_fns(): Create a dist_fns S3 object
c(<dist_fns>): Concatenate a dist_fns S3 object or dist_fns_lists
dist_fns(): Create an empty dist_fns_list
format(<dist_fns>): Format a dist_fns S3 object
is.dist_fns(): Check if this is a dist_fns S3 object
is.dist_fns_list(): Check if this is a dist_fns_list S3 object
map2_dist_fns(): Map over two inputs returning a dist_fns_list
map_dist_fns(): Apply a function to each element of a vector returning a dist_fns_list
plot(<dist_fns>): Plot a dist_fns S3 object
plot(<dist_fns_list>): Plot a dist_fns_list S3 object
pmap_dist_fns(): Map over multiple inputs returning a dist_fns_list

S3 link function class

as.link_fns(): Create a link_fns S3 object
c(<link_fns>): Concatenate a link_fns S3 object or link_fns_lists
format(<link_fns>): Format a link_fns S3 object
is.link_fns(): Check if this is a link_fns S3 object
is.link_fns_list(): Check if this is a link_fns_list S3 object
link_fns(): Create an empty link_fns_list
map2_link_fns(): Map over two inputs returning a link_fns_list
map_link_fns(): Apply a function to each element of a vector returning a link_fns_list
pmap_link_fns(): Map over multiple inputs returning a link_fns_list

Others

sim_outbreak: The sim_outbreak dataset