Package index • rtemis

All functions

HyperparameterCandidates(): The values a tuner may choose among for one hyperparameter
JSONSchema_to_S7(): Build an S7 class from a JSON Schema
S7_dispatcher_JSONSchema(): Generate a config family's dispatcher JSON Schema
S7_to_JSONSchema(): Convert an S7 class built with prop_* factories to a JSON Schema
ai_review(): Write an assessment of a model review with a language model
applicable_metrics(): Which Decomposition Metrics Apply to Which Algorithm
apply_decomp(): Apply Decomposition to New Data
apply_preprocessor(): Apply trained Preprocessor to new data
available_supervised() available_clustering() available_calibration() available_decomposition(): Available Algorithms
bias_variance(): Estimate Bias and Variance
calibrate(): Calibrate Classification & ClassificationRes Models
check_data(): Check Data
class_imbalance(): Class Imbalance
classification_metrics(): Classification Metrics
cluster(): Perform Clustering
conformal(): Conformal prediction regions
conformal_metrics(): Score a Conformal Prediction Region
data_fingerprint(): Fingerprint a dataset
data_profile(): Profile a dataset
ddb_collect(): Collect a lazy-read duckdb table
ddb_data(): Read CSV using DuckDB
decomp(): Perform Data Decomposition
decomp_metrics(): Decomposition Metrics
decomposition_traits(): Decomposition Algorithm Traits
describe(): Describe object
df_movecolumn(): Move data frame column
df_nunique_perfeat(): Unique values per feature
draw_linad(): Plot a Linear Additive Tree
dt_describe(): Describe data.table
dt_inspect_types(): Inspect column types
dt_keybin_reshape(): Long to wide key-value reshaping
dt_merge(): Merge data.tables
dt_names_by_attr(): List column names by attribute
dt_nunique_perfeat(): Number of unique values per feature
dt_pctmatch(): Get N and percent match of values between two columns of two data.tables
dt_pctmissing(): Get percent of missing values from every column
dt_set_autotypes(): Set column types automatically
dt_set_clean_all(): Clean column names and factor levels in-place
dt_set_cleanfactorlevels(): Clean factor levels of data.table in-place
dt_set_logical2factor(): Convert data.table logical columns to factors
dt_set_one_hot(): Convert data.table's factor to one-hot encoding in-place
exc(): Exclude columns by character or numeric vector.
explain(): Explain a prediction
explanation_methods(): Explanation Methods per Algorithm
feature_matrix(): Convert tabular data to feature matrix
feature_names(): Get feature names
features(): Get features from tabular data
fit_predict(): Fit a Learner and Predict New Data
fitted_config(): A Preprocessor's config with its learned values filled in
get_factor_names(): Get factor names
get_learning_curve(): Learning curve of a fitted model
get_loadings(): Get Decomposition Loadings
get_metrics(): Get metrics
get_mode(): Get the mode of a factor or integer
get_varimp(): Get variable importance
getnames() getfactornames() getnumericnames() getlogicalnames() getcharacternames() getdatenames(): Get names by string matching or class
getnamesandtypes(): Get data.frame names and types
`%BC%`: Binary matrix times character vector
inc(): Select (include) columns by character or numeric vector.
index_col_by_attr(): Index columns by attribute name & value
ingest(): Read a data file and normalize it to Parquet
init_project_dir(): Initialize Project Directory
inspect(): Inspect rtemis object
inspect_type(): Inspect character and factor vector
is_constant(): Check if vector is constant
massGLM(): Mass-univariate GLM Analysis
matchcases(): Match cases by covariates
mgetnames(): Get names by string matching multiple patterns
names_by_class(): List column names by class
numeric_features(): Get numeric features from tabular data
one_hot2factor(): Convert one-hot encoded matrix to factor
outcome(): Get the outcome as a vector
outcome_name(): Get the name of the last column
partition(): Split a dataset into training and held-out test partitions
plot_learning(): Plot a Model's Learning Curve
plot_manhattan() plot_manhattan.MassGLM() plot(<MassGLM>): Plot a MassGLM Result
plot_metric(): Plot Stored Metrics Across Model Resamples
plot_roc(): Plot Classification ROC Curves
plot_session(): Plot a Supervised object's session timeline
plot_true_pred(): Plot a Model's True Versus Predicted Values
plot_varimp(): Plot a Model's Variable Importance
preprocess(): Preprocess Data
preprocessed(): Get preprocessed data from Preprocessor.
present(): Describe and Plot Fitted Models
read(): Read tabular data from a variety of formats
read_config(): Read an rtemis config from file
read_result(): Read a portable result from a local schema registry
reconstruct(): Reconstruct Data from a Decomposition
record(): Derive a run record from a fitted model
regression_metrics(): Regression Metrics
resample(): Resample data
review(): Review a trained supervised model
rnormmat(): Random Normal Matrix
roc_curve(): ROC curve coordinates
rtemis rtemis-package: rtemis: Advanced Machine Learning and Visualization
rtemis_conditions rtemis_error rtemis_input_error rtemis_type_error rtemis_value_error rtemis_length_error rtemis_range_error rtemis_null_input rtemis_data_error rtemis_dim_error rtemis_level_mismatch rtemis_missing_data rtemis_outcome_error rtemis_io_error rtemis_file_not_found rtemis_file_exists rtemis_unsupported_error rtemis_runtime_error: rtemis error conditions
rtversion(): Get rtemis version and system info
runifmat(): Random Uniform Matrix
same_data(): Do two fingerprints identify the same dataset?
se(): Standard error of the fit
session_timeline(): Session Execution Timeline Table
set_outcome(): Move outcome to last column
set_positive_class(): Set positive class
setdiffsym(): Symmetric Set Difference
setup_ARFFIngest(): Set up an ARFFIngestConfig
setup_ASWCriterion(): Setup ASWCriterionConfig
setup_Autoencoder(): Setup Autoencoder config.
setup_BART(): Setup BART Hyperparameters
setup_Bootstrap(): Setup Bootstrap Resampler
setup_CART(): Setup CART Hyperparameters
setup_CHCriterion(): Setup CHCriterionConfig
setup_CMeans(): Setup CMeansConfig
setup_CQR(): Setup Conformalized Quantile Regression
setup_CUDA(): Setup an NVIDIA GPU as the compute device
setup_CVPlus(): Setup CV+ Conformal Prediction
setup_ClusterConfig(): Setup ClusterConfig
setup_ConditionalSuperLearner(): Setup ConditionalSuperLearner Hyperparameters
setup_Custom(): Setup Custom Resampler
setup_DBSCAN(): Setup DBSCANConfig
setup_DTAIngest(): Set up a DTAIngestConfig
setup_DecomposeConfig(): Setup DecomposeConfig
setup_DelimitedIngest(): Set up a DelimitedIngestConfig
setup_FutureExecution(): Set up a FutureExecutionConfig
setup_GAM(): Setup GAM Hyperparameters
setup_GLM(): Setup GLM Hyperparameters
setup_GLMNET(): Setup GLMNET Hyperparameters
setup_GLMTree(): Setup GLMTree Hyperparameters
setup_GMM(): Setup GMMConfig
setup_GridSearch(): Setup Grid Search Config
setup_GroupPartition(): Set up a GroupPartitionConfig
setup_HAL(): Setup HAL Hyperparameters
setup_HOPACH(): Setup HOPACHConfig
setup_HardCL(): Setup HardCLConfig
setup_ICA(): setup_ICA
setup_Isomap(): Setup Isomap config.
setup_Isotonic(): Setup Isotonic Hyperparameters
setup_KFold(): Setup KFold Resampler
setup_KMeans(): Setup KMeansConfig
setup_KNN(): Setup KNN Hyperparameters
setup_LINAD(): Setup LINAD Hyperparameters
setup_LINADForest(): Setup LINADForest Hyperparameters
setup_LOOCV(): Setup LOOCV Resampler
setup_LightCART(): Setup LightCART Hyperparameters
setup_LightGBM(): Setup LightGBM Hyperparameters
setup_LightRF(): Setup LightRF Hyperparameters
setup_LightRuleFit(): Setup LightRuleFit Hyperparameters
setup_LinearSVM(): Setup LinearSVM Hyperparameters
setup_MARS(): Setup MARS Hyperparameters
setup_MDS(): Setup MDS config.
setup_MLP(): Setup MLP Hyperparameters
setup_MiraiExecution(): Set up a MiraiExecutionConfig
setup_ModalityStacking(): Setup ModalityStacking Hyperparameters
setup_MonotonicHAL(): Setup MonotonicHAL Hyperparameters
setup_MultiASWCriterion(): Setup MultiASWCriterionConfig
setup_NMF(): Setup NMF config.
setup_NNLS(): Setup NNLS Hyperparameters
setup_NeuralGas(): Setup NeuralGasConfig
setup_Nystrom(): Setup NystromConfig
setup_PAM(): Setup PAMConfig
setup_PAMK(): Setup PAMKConfig
setup_PCA(): Setup PCA config.
setup_PCoA(): Setup PCoA config.
setup_ParquetIngest(): Set up a ParquetIngestConfig
setup_PredefinedPartition(): Set up a PredefinedPartitionConfig
setup_Preprocessor(): Setup Preprocessor
setup_RDSIngest(): Set up an RDSIngestConfig
setup_RadialSVM(): Setup RadialSVM Hyperparameters
setup_RandomPartition(): Set up a RandomPartitionConfig
setup_Ranger(): Setup Ranger Hyperparameters
setup_Resampler(): Setup a Resampler (Deprecated)
setup_SHAP(): Setup SHAP Explanation
setup_SPLS(): Setup SPLS Hyperparameters
setup_SerialExecution(): Set up a SerialExecutionConfig
setup_SpectralLaplace(): Setup SpectralLaplaceConfig
setup_SpectralLocal(): Setup SpectralLocalConfig
setup_SpectralRBF(): Setup SpectralRBFConfig
setup_SplitConformal(): Setup Split Conformal Prediction
setup_StratBoot(): Setup StratBoot Resampler
setup_StratSub(): Setup StratSub Resampler
setup_SuperConfig(): Setup SuperConfig
setup_SuperConfigLive(): Setup SuperConfigLive
setup_SuperLearner(): Setup SuperLearner Hyperparameters
setup_SupervisedPreprocessor(): Setup Supervised Preprocessor
setup_TabNet(): Setup TabNet Hyperparameters
setup_TimePartition(): Set up a TimePartitionConfig
setup_UMAP(): Setup UMAP config.
setup_VariationalAutoencoder(): Setup Variational Autoencoder config.
setup_XLSXIngest(): Set up an XLSXIngestConfig
setup_tSNE(): Setup tSNE config.
shap_by_level(): Contributions of a categorical feature, by the level each case has
shap_case(): One case's contributions, ordered by how much they moved the prediction
shap_long(): Every contribution, one row per case per feature
show_color_key(): Show rtemis color key
size(): Size of object
table_column_attr(): Tabulate column attributes
train(): Train Supervised Learning Models
tune_over(): Mark the values to tune a hyperparameter over
tuning_grid(): Tuning grid of a Hyperparameters object
validate_config(): Validate an rtemis config, against the schema and optionally against data
varimp_table(): Variable importance as one table
write_config(): Write an rtemis config to a JSON file
write_record(): Write a run record to a JSON file
write_result(): Write a portable result with optional Parquet payloads
write_text(): Write a report as text
writeup(): Write the Methods and Results sections for a trained model