Function reference • mrf2d

All functions
`Z_potts`	Example objects from `mrf2d`
`fourier_2d()` `polynomial_2d()`	Creation of basis functions
`bold5000`	BOLD5000 neuroimaging data
`cp_mrf2d()`	Conditional probabilities in a pixel position
`field1` `hfield1`	Example Data
`dplot()` `cplot()`	Plotting functions for lattice data
`fit_ghm()`	EM estimation for Gaussian Hidden Markov field
`fit_pl()`	Maximum Pseudo-likelihood fitting of MRFs on 2d lattices.
`fit_sa()`	Stochastic Approximation fitting of MRFs on 2d lattices
`print(<hmrfout>)` `summary(<hmrfout>)` `plot(<hmrfout>)`	MRF fitting functions output
`mrf2d-family`	Parameter restriction families
`mrfi()` `rpositions()` `as.list(<mrfi>)` `length(<mrfi>)` `[[`(<mrfi>,<numeric>,<missing>) `[`(<mrfi>,<numeric>,<missing>) `+`(<mrfi>,<numeric>) `-`(<mrfi>,<numeric>) `+`(<mrfi>,<mrfi>) `-`(<mrfi>,<mrfi>) `mrfi_to_string()`	mrfi: MRF interaction structure
`print(<mrfout>)` `summary(<mrfout>)` `plot(<mrfout>)`	MRF fitting functions output
`pl_mrf2d()`	Pseudo-likelihood function for MRFs on 2d lattices
`plot(<mrfi>)`	Plotting of `mrfi` objects.
`rmrf2d()`	Sampling of Markov Random Fields on 2d lattices
`rmrf2d_mc()`	Markov Chain sampling of MRFs for Monte-Carlo methods
`smr_array()` `expand_array()`	Summarized representation of theta arrays
`smr_stat()` `cohist()` `vec_description()`	Summary Statistics

Reference