artifacts               Read and write the artifacts that cross the
                        language boundary
average_precision       Average precision
bind_channels           Put channels side by side
build_representation    Build one representation for a set of targets
calendar_channels       Where in the year, or the day, each bin sits
combine                 Combine learners, or representations, into a
                        set
coverage                Which units reach which bins
cv                      How the folds are drawn
digest_array            The cross-language digest of a representation
elasticnet              Penalised regression on the flattened
                        representation
ensemble                How the candidates are combined
ensemble_combine        Combine one prediction per member into one
                        prediction
ensemble_fit            Fit the combiner on the out-of-fold predictions
ensemble_weights        The weights the combiner fitted
feature_matrix          Bring an already-reduced feature table into a
                        ladder
fit_learner             Fit one learner at one grain
fold_map                Assign units to cross-validation folds
forest                  Random forest on the flattened representation
grain_contrasts         Compare every grain against a learner's best
                        one
grain_ladder            Fit at every grain and see where skill
                        saturates
grain_matrix            Reduce sensor series to a temporal grain
grains                  Several representations to run the same
                        learners across
implied_skill           What population skill a reported level is
                        consistent with
kappa_score             Cohen's kappa, and where two models disagree
learner                 Define a learner
lookback_matrix         Reduce sensor series to a lookback anchored on
                        each target
native                  How a series becomes an array a learner reads
occlusion               What part of the record a fitted model reads
paired_contrast         Compare two arms cell by cell
plot.timesift           Draw a run
plot.timesift_ladder    Draw a ladder
plot.timesift_selection
                        Draw how stable the choice of grain was
positive_weights        Case weights that balance a rare response
predict.timesift        Predict from a fitted timesift
register_learner        Register a learner
register_metric         Register a metric
register_response       Register a response head
roc_auc                 The area under the ROC curve
scorable_cells          Which cells a score is defined on
score_predictions       Score held-out predictions on the cells the
                        mask allows
select_grain            Choose the grain inside the training data, and
                        score the whole procedure
simulate_records        Simulate sensor records whose response acts at
                        a known temporal grain
stepwise                Forward selection by AIC on the flattened
                        representation
timesift                Fit and compare representations of time-varying
                        data
timesift_report         What a run found
timesift_set            Several built representations of the same
                        targets
torch_learners          Sequence encoders with a joint multi-label head
train_control           Training settings every neural learner reads
tss                     The true skill statistic
tss_inflation           How much a self-selected threshold inflates the
                        reported level
