estimation - MSM estimation from data (msmtools.estimation)

Countmatrix

count_matrix(dtraj, lag[, sliding, ...]) Generate a count matrix from given microstate trajectory.
cmatrix(dtraj, lag[, sliding, ...]) Generate a count matrix from given microstate trajectory.

Connectivity

connected_sets(C[, directed]) Compute connected sets of microstates.
largest_connected_set(C[, directed]) Largest connected component for a directed graph with edge-weights given by the count matrix.
largest_connected_submatrix(C[, directed, lcc]) Compute the count matrix on the largest connected set.
connected_cmatrix(C[, directed, lcc]) Compute the count matrix on the largest connected set.
is_connected(C[, directed]) Check connectivity of the given matrix.

Estimation

transition_matrix(C[, reversible, mu, method]) Estimate the transition matrix from the given countmatrix.
tmatrix(C[, reversible, mu, method]) Estimate the transition matrix from the given countmatrix.
rate_matrix(C[, dt, method, sparsity, ...]) Estimate a reversible rate matrix from a count matrix.
log_likelihood(C, T) Log-likelihood of the count matrix given a transition matrix.
tmatrix_cov(C[, k]) Covariance tensor for non-reversible transition matrix posterior.
error_perturbation(C, S) Error perturbation for given sensitivity matrix.

Sampling

tmatrix_sampler(C[, reversible, mu, T0, ...]) Generate transition matrix sampler object.

Bootstrap

bootstrap_counts(dtrajs, lagtime[, corrlength]) Generates a randomly resampled count matrix given the input coordinates.
bootstrap_trajectories(trajs, correlation_length) Generates a randomly resampled trajectory segments.

Priors

prior_neighbor(C[, alpha]) Neighbor prior for the given count matrix.
prior_const(C[, alpha]) Constant prior for given count matrix.
prior_rev(C[, alpha]) Prior counts for sampling of reversible transition matrices.