NEWS.md
In fastRG: Sample Generalized Random Dot Product Graphs in Linear Time

fastRG 0.3.2

Added documentation about block sorting in blockmodels when sort_nodes = TRUE (#35). Blocks are now only sorted when sort_nodes = TRUE, although they were previously always sorted. In directed stochastic blocks, flipped incoming and outgoing blocks, such that X now contains info about outgoing blocks and Y now contains info about incoming blocks, as you would expected if A[i, j] encodes an edge from node i to node j
Fixed bug where isolated nodes were sometimes dropped from igraph and tidygraph objects (#35)
Added plot_expectation(), plot_sparse_matrix() and expectation() utilities (#34)
Fixed incorrect computation in expected_degrees() (#34)

Users must now pass poisson_edges and allow_self_loops arguments to model object constructors (i.e. sbm()) rather than sample_*() methods. Additionally, when poisson_edges = FALSE, the mixing matrix S is taken (after degree-scaling and possible symmetrization for undirected models) to represent desired inter-factor connection probabilities, and thus should be between zero and one. This Bernoulli-parameterized S is then transformed into the equivalent (or approximately equivalent) Poisson S. See Section 2.3 of Rohe et al. (2017) for additional details about this conversion and approximation of Bernoulli graphs by Poisson graphs (#29).

Add overlapping stochastic blockmodel (#7, #25)
Add directed degree-corrected stochastic blockmodels (#18)
Allow rank 1 undirected stochastic block models
Fix bug where isolated nodes where dropped from sampled tidygraphs (#23)
Allow users to force model identification in DC-SBMs with force_identifiability = TRUE, and in overlapping SBMs with force_pure = TRUE, which are now the default.
Improve population expected degree/density computations (#19)
Let user know when theta_out is automatically generated for directed DC-SBMs (#22)
Fixed an obscure but pesky issue sampling from models with empty blocks (#13)
Documented svds() and eigs_sym() methods, which allow users to take spectral decompositions of expected adjacency matrices conditional X, S and Y.