additive                Generalised additive model on the flattened
                        representation
artifacts               Read and write the artifacts that cross the
                        language boundary
as.matrix.timesift_matrix
                        A representation as a block of predictors
average_precision       Average precision
bind_channels           Put channels side by side
boosting                Gradient boosted trees on the flattened
                        representation
boyce_index             The continuous Boyce index
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
discriminant            Flexible discriminant analysis on the flattened
                        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_spread         How far the members of an ensemble disagree
ensemble_weights        The weights the combiner fitted
envelope                biomod2's surface range envelope on the
                        flattened representation
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
hierarchical            Bayesian logistic model with a spatial field
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
linear                  A generalised linear model on the flattened
                        representation
lookback_matrix         Reduce sensor series to a lookback anchored on
                        each target
mars                    Multivariate adaptive regression splines on the
                        flattened representation
maxent                  Maxent on the flattened representation
native                  How a series becomes an array a learner reads
occlusion               What part of the record a fitted model reads
ordinal_metric          A metric of ordinal classes
paired_contrast         Compare two arms cell by cell
perceptron              Single-hidden-layer network on the flattened
                        representation
plot.timesift           Draw a run
plot.timesift_ladder    Draw a ladder
plot.timesift_range_change
                        Draw the change in range
plot.timesift_response_curve
                        Draw a response curve
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
project                 Project a fit onto rasters
pseudo_absences         Draw pseudo-absences from a pool of background
                        units
range_change            How the range of each response changes between
                        two projections
register_learner        Register a learner
register_metric         Register a metric
register_response       Register a response head
register_tuning         Register the grid a learner is tuned over
regression_metric       A metric of a numeric response and its
                        predictions
response_curve          How a prediction responds to one predictor
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
table_metric            A metric of the two-by-two table of decisions
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
tree                    Classification and regression tree on the
                        flattened representation
tss                     The true skill statistic
tss_inflation           How much a self-selected threshold inflates the
                        reported level
tune                    Tune a learner's settings on the training units
