Updates

11/26/18 (version 0.1.4)

fixed problems with package versions (I think they didn't update in previous pushes)
bug fix: wedin/random direction percentiles switched to correct singular value scale
Problems with indiv_rank =0 and pca scores projection.
bug fix: setting store_full = False now works
setting joint_rank/individual_ranks = 0 now works
bug fix: centers now computed correctly
got rid of nticks for scree plots

bug fix: AJIVE.common and PCA.from_precomputed now correctly extract observation names

Major changes to both frontend and backend

cleaned up backend code to be more readable
renamed main object JIVE -> AJIVE
frontend now follows sklearn API more closely
fixed weird joint rank estimation bug
added jive.PCA.PCA object
added a number of new visualization tools
modified documentation to follow new API
TODOs left: finish documentation, testing, some minor issues noted in the code, sparse capabilities, put on pip

changes to Jive object API
- block specific/common estimates now called via jive.get_block_specific_estimates() and jive.get_common_joint_space_estimate()
- now only one wedin bound resampling procedure (e.g. removed basis resampling)
- removed jive.compute_wedin_bound
added random direction bound (e.g. jive.sample_random_direction_bounds())
jive block returns samples from random direction (before just returned point estimate).
- samples can be accessed via jive.wedin_sv_samples and jive.random_sv_samples
added diagnostic plot (e.g. jive.plot_joint_diagnostic())
added diagnostic demo

changes to Jive object API
- Jive() no longer creates the scree plot on initialization
- the results of SVD (i.e. U, D, V) are now called scores, sv, loadings
added sparse functionality
- Jive() now works for both sparse and dense matrices (user can input a mix)
- Jive() will not do the full SVD for sparse matrices -- the user has to specify the rank of the initial SVD (via the init_svd_ranks argument which is a list). The initial SVD rank should be larger than the initial signal rank.
- The block specific joint/initial space estimation present a problem for large, sparse matrices. If S is a sparse matrix then UU^TS will be a dense matrix the same size as S. To prevent creating and attempting to compute the SVD of very large sparse matrices we implemented some functions based on scipy.linalg.sparse.LinearOperator that will do lazy evaluation for UU^TS and (I - UU^T)S (see lazymatpy module).