Bayesian Inference for Neyman-Scott Point Processes

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Documentation for package ‘binspp’ version 0.2.2

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AuxVarGen Generate auxiliary variable for given proposed parameters.
binspp Bayesian inference for Neyman-Scott point processes
coeff Calculate parameters for Birth and Death Interaction likelihood functions.
cov_refor Distance to the reforestration polygon
cov_reserv Distance to the reservoir
cov_slope Slope of the area
cov_tdensity Trees density
cov_tmi Topographic moisture index
estgtp Bayesian MCMC estimation of parameters of generalized Thomas process
estgtpr Results for Bayesian MCMC estimation of parameters of generalized Thomas process
estinternsp Estimation of interaction Neyman-Scott point process using auxiliary variable algorithm into Markov chain Monte Carlo.
estintp Estimation of Thomas-type cluster point process with complex inhomogeneities
first_step Estimate the first-order inhomogeneity
pCClik2 Evaluate unnormalized likelihood for auxiliary variable
plot_conn plot_conn
plot_outputs Graphical output describing the posterior distributions
print_outputs Text output describing the posterior distributions
print_outputs_internsp Text output describing the posterior distributions
re_estimate Re-estimate the posterior distributions with different burn-in
rgtp Simulation of generalized Thomas process
rThomasInhom Simulate a realization of Thomas-type cluster point process with complex inhomogeneities
trees_N4 Spanish oak trees
x_left_N4 Left horizontal corners for trees_N4 dataset
x_right_N4 Right horizontal corners for trees_N4 dataset
y_bottom_N4 Bottom vertical corners for trees_N4 dataset
y_top_N4 Vertical corners for trees_N4 dataset