Learn Predictive Representations of Time-Varying Data

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Documentation for package ‘timesift’ version 0.6.1

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A B C D E F G H I K L M N O P R S T W

-- A --

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
as_sift Several representations to run the same learners across
average_precision Average precision

-- B --

bind_channels Put channels side by side
block_cv How the folds are drawn
boosting Gradient boosted trees on the flattened representation
boyce_index The continuous Boyce index
build_representation Build one representation for a set of targets

-- C --

c.timesift_learner Combine learners, or representations, into a set
c.timesift_models Combine learners, or representations, into a set
c.timesift_representation Combine learners, or representations, into a set
c.timesift_sift Combine learners, or representations, into a set
calendar_channels Where in the year, or the day, each bin sits
cnn Sequence encoders with a joint multi-label head
combine Combine learners, or representations, into a set
coverage Which units reach which bins
cv How the folds are drawn

-- D --

decision_threshold Cohen's kappa, and where two models disagree
decision_threshold.default Cohen's kappa, and where two models disagree
decision_threshold.timesift Cohen's kappa, and where two models disagree
digest_array The cross-language digest of a representation
discriminant Flexible discriminant analysis on the flattened representation

-- E --

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
env_cv How the folds are drawn

-- F --

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

-- G --

grain How a series becomes an array a learner reads
grains Several representations to run the same learners across
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
grouped_cv How the folds are drawn

-- H --

hierarchical Bayesian logistic model with a spatial field

-- I --

implied_skill What population skill a reported level is consistent with

-- K --

kappa_score Cohen's kappa, and where two models disagree

-- L --

learner Define a learner
learners Register a learner
linear A generalised linear model on the flattened representation
lookback How a series becomes an array a learner reads
lookbacks Several representations to run the same learners across
lookback_matrix Reduce sensor series to a lookback anchored on each target

-- M --

mars Multivariate adaptive regression splines on the flattened representation
maxent Maxent on the flattened representation
metrics Register a metric
mlp Sequence encoders with a joint multi-label head
model_agreement Cohen's kappa, and where two models disagree
multigrain How a series becomes an array a learner reads

-- N --

native How a series becomes an array a learner reads

-- O --

occlusion What part of the record a fitted model reads
occlusion.default What part of the record a fitted model reads
occlusion.timesift What part of the record a fitted model reads
occlusion.timesift_ladder What part of the record a fitted model reads
ordinal_metric A metric of ordinal classes

-- P --

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
predict.timesift_fit Fit one learner at one grain
print.timesift What a run found
print.timesift_summary What a run found
project Project a fit onto rasters
pseudo_absences Draw pseudo-absences from a pool of background units

-- R --

range_change How the range of each response changes between two projections
read_cells Read and write the artifacts that cross the language boundary
read_folds Read and write the artifacts that cross the language boundary
read_response Read and write the artifacts that cross the language boundary
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
rescnn Sequence encoders with a joint multi-label head
responses Register a response head
response_curve How a prediction responds to one predictor
response_curve.default How a prediction responds to one predictor
response_curve.timesift How a prediction responds to one predictor
roc_auc The area under the ROC curve

-- S --

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
summary.timesift What a run found
summary.timesift_ladder Fit at every grain and see where skill saturates
summary.timesift_selection Choose the grain inside the training data, and score the whole procedure

-- T --

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
tunings Register the grid a learner is tuned over

-- W --

write_cells Read and write the artifacts that cross the language boundary
write_folds Read and write the artifacts that cross the language boundary
write_response Read and write the artifacts that cross the language boundary