| ml-params | Spark ML - ML Params | 
| ml-persistence | Spark ML - Model Persistence | 
| ml-transform-methods | Spark ML - Transform, fit, and predict methods (ml_ interface) | 
| ml-tuning | Spark ML - Tuning | 
| ml_aft_survival_regression | Spark ML - Survival Regression | 
| ml_als | Spark ML - ALS | 
| ml_als_tidiers | Tidying methods for Spark ML ALS | 
| ml_approx_nearest_neighbors | Utility functions for LSH models | 
| ml_approx_similarity_join | Utility functions for LSH models | 
| ml_association_rules | Frequent Pattern Mining - FPGrowth | 
| ml_binary_classification_eval | Spark ML - Evaluators | 
| ml_binary_classification_evaluator | Spark ML - Evaluators | 
| ml_bisecting_kmeans | Spark ML - Bisecting K-Means Clustering | 
| ml_chisquare_test | Chi-square hypothesis testing for categorical data. | 
| ml_classification_eval | Spark ML - Evaluators | 
| ml_clustering_evaluator | Spark ML - Clustering Evaluator | 
| ml_compute_cost | Spark ML - K-Means Clustering | 
| ml_compute_silhouette_measure | Spark ML - K-Means Clustering | 
| ml_corr | Compute correlation matrix | 
| ml_cross_validator | Spark ML - Tuning | 
| ml_decision_tree | Spark ML - Decision Trees | 
| ml_decision_tree_classifier | Spark ML - Decision Trees | 
| ml_decision_tree_regressor | Spark ML - Decision Trees | 
| ml_default_stop_words | Default stop words | 
| ml_describe_topics | Spark ML - Latent Dirichlet Allocation | 
| ml_evaluate | Evaluate the Model on a Validation Set | 
| ml_evaluate.ml_evaluator | Evaluate the Model on a Validation Set | 
| ml_evaluate.ml_generalized_linear_regression_model | Evaluate the Model on a Validation Set | 
| ml_evaluate.ml_linear_regression_model | Evaluate the Model on a Validation Set | 
| ml_evaluate.ml_logistic_regression_model | Evaluate the Model on a Validation Set | 
| ml_evaluate.ml_model_classification | Evaluate the Model on a Validation Set | 
| ml_evaluate.ml_model_clustering | Evaluate the Model on a Validation Set | 
| ml_evaluate.ml_model_generalized_linear_regression | Evaluate the Model on a Validation Set | 
| ml_evaluate.ml_model_linear_regression | Evaluate the Model on a Validation Set | 
| ml_evaluate.ml_model_logistic_regression | Evaluate the Model on a Validation Set | 
| ml_evaluator | Spark ML - Evaluators | 
| ml_feature_importances | Spark ML - Feature Importance for Tree Models | 
| ml_find_synonyms | Feature Transformation - Word2Vec (Estimator) | 
| ml_fit | Spark ML - Transform, fit, and predict methods (ml_ interface) | 
| ml_fit.default | Spark ML - Transform, fit, and predict methods (ml_ interface) | 
| ml_fit_and_transform | Spark ML - Transform, fit, and predict methods (ml_ interface) | 
| ml_fpgrowth | Frequent Pattern Mining - FPGrowth | 
| ml_freq_itemsets | Frequent Pattern Mining - FPGrowth | 
| ml_freq_seq_patterns | Frequent Pattern Mining - PrefixSpan | 
| ml_gaussian_mixture | Spark ML - Gaussian Mixture clustering. | 
| ml_gbt_classifier | Spark ML - Gradient Boosted Trees | 
| ml_gbt_regressor | Spark ML - Gradient Boosted Trees | 
| ml_generalized_linear_regression | Spark ML - Generalized Linear Regression | 
| ml_glm_tidiers | Tidying methods for Spark ML linear models | 
| ml_gradient_boosted_trees | Spark ML - Gradient Boosted Trees | 
| ml_isotonic_regression | Spark ML - Isotonic Regression | 
| ml_isotonic_regression_tidiers | Tidying methods for Spark ML Isotonic Regression | 
| ml_is_set | Spark ML - ML Params | 
| ml_kmeans | Spark ML - K-Means Clustering | 
| ml_kmeans_cluster_eval | Evaluate a K-mean clustering | 
| ml_labels | Feature Transformation - StringIndexer (Estimator) | 
| ml_lda | Spark ML - Latent Dirichlet Allocation | 
| ml_lda_tidiers | Tidying methods for Spark ML LDA models | 
| ml_linear_regression | Spark ML - Linear Regression | 
| ml_linear_svc | Spark ML - LinearSVC | 
| ml_linear_svc_tidiers | Tidying methods for Spark ML linear svc | 
| ml_load | Spark ML - Model Persistence | 
| ml_logistic_regression | Spark ML - Logistic Regression | 
| ml_logistic_regression_tidiers | Tidying methods for Spark ML Logistic Regression | 
| ml_log_likelihood | Spark ML - Latent Dirichlet Allocation | 
| ml_log_perplexity | Spark ML - Latent Dirichlet Allocation | 
| ml_metrics_binary | Extracts metrics from a fitted table | 
| ml_metrics_multiclass | Extracts metrics from a fitted table | 
| ml_metrics_regression | Extracts metrics from a fitted table | 
| ml_model_data | Extracts data associated with a Spark ML model | 
| ml_multiclass_classification_evaluator | Spark ML - Evaluators | 
| ml_multilayer_perceptron | Spark ML - Multilayer Perceptron | 
| ml_multilayer_perceptron_classifier | Spark ML - Multilayer Perceptron | 
| ml_multilayer_perceptron_tidiers | Tidying methods for Spark ML MLP | 
| ml_naive_bayes | Spark ML - Naive-Bayes | 
| ml_naive_bayes_tidiers | Tidying methods for Spark ML Naive Bayes | 
| ml_one_vs_rest | Spark ML - OneVsRest | 
| ml_param | Spark ML - ML Params | 
| ml_params | Spark ML - ML Params | 
| ml_param_map | Spark ML - ML Params | 
| ml_pca | Feature Transformation - PCA (Estimator) | 
| ml_pca_tidiers | Tidying methods for Spark ML Principal Component Analysis | 
| ml_pipeline | Spark ML - Pipelines | 
| ml_power_iteration | Spark ML - Power Iteration Clustering | 
| ml_predict | Spark ML - Transform, fit, and predict methods (ml_ interface) | 
| ml_predict.ml_model_classification | Spark ML - Transform, fit, and predict methods (ml_ interface) | 
| ml_prefixspan | Frequent Pattern Mining - PrefixSpan | 
| ml_random_forest | Spark ML - Random Forest | 
| ml_random_forest_classifier | Spark ML - Random Forest | 
| ml_random_forest_regressor | Spark ML - Random Forest | 
| ml_recommend | Spark ML - ALS | 
| ml_regression_evaluator | Spark ML - Evaluators | 
| ml_save | Spark ML - Model Persistence | 
| ml_save.ml_model | Spark ML - Model Persistence | 
| ml_stage | Spark ML - Pipeline stage extraction | 
| ml_stages | Spark ML - Pipeline stage extraction | 
| ml_sub_models | Spark ML - Tuning | 
| ml_summary | Spark ML - Extraction of summary metrics | 
| ml_survival_regression | Spark ML - Survival Regression | 
| ml_survival_regression_tidiers | Tidying methods for Spark ML Survival Regression | 
| ml_topics_matrix | Spark ML - Latent Dirichlet Allocation | 
| ml_train_validation_split | Spark ML - Tuning | 
| ml_transform | Spark ML - Transform, fit, and predict methods (ml_ interface) | 
| ml_tree_feature_importance | Spark ML - Feature Importance for Tree Models | 
| ml_tree_tidiers | Tidying methods for Spark ML tree models | 
| ml_uid | Spark ML - UID | 
| ml_unsupervised_tidiers | Tidying methods for Spark ML unsupervised models | 
| ml_validation_metrics | Spark ML - Tuning | 
| ml_vocabulary | Feature Transformation - CountVectorizer (Estimator) | 
| mutate | Mutate | 
| sdf-saveload | Save / Load a Spark DataFrame | 
| sdf-transform-methods | Spark ML - Transform, fit, and predict methods (sdf_ interface) | 
| sdf_along | Create DataFrame for along Object | 
| sdf_bind | Bind multiple Spark DataFrames by row and column | 
| sdf_bind_cols | Bind multiple Spark DataFrames by row and column | 
| sdf_bind_rows | Bind multiple Spark DataFrames by row and column | 
| sdf_broadcast | Broadcast hint | 
| sdf_checkpoint | Checkpoint a Spark DataFrame | 
| sdf_coalesce | Coalesces a Spark DataFrame | 
| sdf_collect | Collect a Spark DataFrame into R. | 
| sdf_copy_to | Copy an Object into Spark | 
| sdf_crosstab | Cross Tabulation | 
| sdf_debug_string | Debug Info for Spark DataFrame | 
| sdf_describe | Compute summary statistics for columns of a data frame | 
| sdf_dim | Support for Dimension Operations | 
| sdf_distinct | Invoke distinct on a Spark DataFrame | 
| sdf_drop_duplicates | Remove duplicates from a Spark DataFrame | 
| sdf_expand_grid | Create a Spark dataframe containing all combinations of inputs | 
| sdf_fit | Spark ML - Transform, fit, and predict methods (sdf_ interface) | 
| sdf_fit_and_transform | Spark ML - Transform, fit, and predict methods (sdf_ interface) | 
| sdf_from_avro | Convert column(s) from avro format | 
| sdf_import | Copy an Object into Spark | 
| sdf_is_streaming | Spark DataFrame is Streaming | 
| sdf_last_index | Returns the last index of a Spark DataFrame | 
| sdf_len | Create DataFrame for Length | 
| sdf_load_parquet | Save / Load a Spark DataFrame | 
| sdf_load_table | Save / Load a Spark DataFrame | 
| sdf_ncol | Support for Dimension Operations | 
| sdf_nrow | Support for Dimension Operations | 
| sdf_num_partitions | Gets number of partitions of a Spark DataFrame | 
| sdf_partition | Partition a Spark Dataframe | 
| sdf_partition_sizes | Compute the number of records within each partition of a Spark DataFrame | 
| sdf_persist | Persist a Spark DataFrame | 
| sdf_pivot | Pivot a Spark DataFrame | 
| sdf_predict | Spark ML - Transform, fit, and predict methods (sdf_ interface) | 
| sdf_project | Project features onto principal components | 
| sdf_quantile | Compute (Approximate) Quantiles with a Spark DataFrame | 
| sdf_random_split | Partition a Spark Dataframe | 
| sdf_rbeta | Generate random samples from a Beta distribution | 
| sdf_rbinom | Generate random samples from a binomial distribution | 
| sdf_rcauchy | Generate random samples from a Cauchy distribution | 
| sdf_rchisq | Generate random samples from a chi-squared distribution | 
| sdf_read_column | Read a Column from a Spark DataFrame | 
| sdf_register | Register a Spark DataFrame | 
| sdf_repartition | Repartition a Spark DataFrame | 
| sdf_residuals | Model Residuals | 
| sdf_residuals.ml_model_generalized_linear_regression | Model Residuals | 
| sdf_residuals.ml_model_linear_regression | Model Residuals | 
| sdf_rexp | Generate random samples from an exponential distribution | 
| sdf_rgamma | Generate random samples from a Gamma distribution | 
| sdf_rgeom | Generate random samples from a geometric distribution | 
| sdf_rhyper | Generate random samples from a hypergeometric distribution | 
| sdf_rlnorm | Generate random samples from a log normal distribution | 
| sdf_rnorm | Generate random samples from the standard normal distribution | 
| sdf_rpois | Generate random samples from a Poisson distribution | 
| sdf_rt | Generate random samples from a t-distribution | 
| sdf_runif | Generate random samples from the uniform distribution U(0, 1). | 
| sdf_rweibull | Generate random samples from a Weibull distribution. | 
| sdf_sample | Randomly Sample Rows from a Spark DataFrame | 
| sdf_save_parquet | Save / Load a Spark DataFrame | 
| sdf_save_table | Save / Load a Spark DataFrame | 
| sdf_schema | Read the Schema of a Spark DataFrame | 
| sdf_separate_column | Separate a Vector Column into Scalar Columns | 
| sdf_seq | Create DataFrame for Range | 
| sdf_sort | Sort a Spark DataFrame | 
| sdf_sql | Spark DataFrame from SQL | 
| sdf_to_avro | Convert column(s) to avro format | 
| sdf_transform | Spark ML - Transform, fit, and predict methods (sdf_ interface) | 
| sdf_unnest_longer | Unnest longer | 
| sdf_unnest_wider | Unnest wider | 
| sdf_weighted_sample | Perform Weighted Random Sampling on a Spark DataFrame | 
| sdf_with_sequential_id | Add a Sequential ID Column to a Spark DataFrame | 
| sdf_with_unique_id | Add a Unique ID Column to a Spark DataFrame | 
| select | Select | 
| separate | Separate | 
| spark-api | Access the Spark API | 
| spark-connections | Manage Spark Connections | 
| sparklyr_get_backend_port | Return the port number of a 'sparklyr' backend. | 
| spark_adaptive_query_execution | Retrieves or sets status of Spark AQE | 
| spark_advisory_shuffle_partition_size | Retrieves or sets advisory size of the shuffle partition | 
| spark_apply | Apply an R Function in Spark | 
| spark_apply_bundle | Create Bundle for Spark Apply | 
| spark_apply_log | Log Writer for Spark Apply | 
| spark_auto_broadcast_join_threshold | Retrieves or sets the auto broadcast join threshold | 
| spark_available_versions | Download and install various versions of Spark | 
| spark_coalesce_initial_num_partitions | Retrieves or sets initial number of shuffle partitions before coalescing | 
| spark_coalesce_min_num_partitions | Retrieves or sets the minimum number of shuffle partitions after coalescing | 
| spark_coalesce_shuffle_partitions | Retrieves or sets whether coalescing contiguous shuffle partitions is enabled | 
| spark_compilation_spec | Define a Spark Compilation Specification | 
| spark_config | Read Spark Configuration | 
| spark_config_kubernetes | Kubernetes Configuration | 
| spark_config_settings | Retrieve Available Settings | 
| spark_connect | Manage Spark Connections | 
| spark_connection | Retrieve the Spark Connection Associated with an R Object | 
| spark_connection-class | spark_connection class | 
| spark_connection_find | Find Spark Connection | 
| spark_connection_is_open | Manage Spark Connections | 
| spark_connect_method | Function that negotiates the connection with the Spark back-end | 
| spark_context | Access the Spark API | 
| spark_context_config | Runtime configuration interface for the Spark Context. | 
| spark_dataframe | Retrieve a Spark DataFrame | 
| spark_default_compilation_spec | Default Compilation Specification for Spark Extensions | 
| spark_dependency | Define a Spark dependency | 
| spark_dependency_fallback | Fallback to Spark Dependency | 
| spark_disconnect | Manage Spark Connections | 
| spark_disconnect_all | Manage Spark Connections | 
| spark_extension | Create Spark Extension | 
| spark_get_checkpoint_dir | Set/Get Spark checkpoint directory | 
| spark_home_set | Set the SPARK_HOME environment variable | 
| spark_ide_columns | Set of functions to provide integration with the RStudio IDE | 
| spark_ide_connection_actions | Set of functions to provide integration with the RStudio IDE | 
| spark_ide_connection_closed | Set of functions to provide integration with the RStudio IDE | 
| spark_ide_connection_open | Set of functions to provide integration with the RStudio IDE | 
| spark_ide_connection_updated | Set of functions to provide integration with the RStudio IDE | 
| spark_ide_objects | Set of functions to provide integration with the RStudio IDE | 
| spark_ide_preview | Set of functions to provide integration with the RStudio IDE | 
| spark_insert_table | Inserts a Spark DataFrame into a Spark table | 
| spark_install | Download and install various versions of Spark | 
| spark_installed_versions | Download and install various versions of Spark | 
| spark_install_dir | Download and install various versions of Spark | 
| spark_install_tar | Download and install various versions of Spark | 
| spark_integ_test_skip | It lets the package know if it should test a particular functionality or not | 
| spark_jobj | Retrieve a Spark JVM Object Reference | 
| spark_jobj-class | spark_jobj class | 
| spark_last_error | Surfaces the last error from Spark captured by internal 'spark_error' function | 
| spark_load_table | Reads from a Spark Table into a Spark DataFrame. | 
| spark_log | View Entries in the Spark Log | 
| spark_read | Read file(s) into a Spark DataFrame using a custom reader | 
| spark_read_avro | Read Apache Avro data into a Spark DataFrame. | 
| spark_read_binary | Read binary data into a Spark DataFrame. | 
| spark_read_csv | Read a CSV file into a Spark DataFrame | 
| spark_read_delta | Read from Delta Lake into a Spark DataFrame. | 
| spark_read_image | Read image data into a Spark DataFrame. | 
| spark_read_jdbc | Read from JDBC connection into a Spark DataFrame. | 
| spark_read_json | Read a JSON file into a Spark DataFrame | 
| spark_read_libsvm | Read libsvm file into a Spark DataFrame. | 
| spark_read_orc | Read a ORC file into a Spark DataFrame | 
| spark_read_parquet | Read a Parquet file into a Spark DataFrame | 
| spark_read_source | Read from a generic source into a Spark DataFrame. | 
| spark_read_table | Reads from a Spark Table into a Spark DataFrame. | 
| spark_read_text | Read a Text file into a Spark DataFrame | 
| spark_save_table | Saves a Spark DataFrame as a Spark table | 
| spark_session | Access the Spark API | 
| spark_session_config | Runtime configuration interface for the Spark Session | 
| spark_set_checkpoint_dir | Set/Get Spark checkpoint directory | 
| spark_statistical_routines | Generate random samples from some distribution | 
| spark_submit | Manage Spark Connections | 
| spark_table_name | Generate a Table Name from Expression | 
| spark_uninstall | Download and install various versions of Spark | 
| spark_version | Get the Spark Version Associated with a Spark Connection | 
| spark_version_from_home | Get the Spark Version Associated with a Spark Installation | 
| spark_web | Open the Spark web interface | 
| spark_write | Write Spark DataFrame to file using a custom writer | 
| spark_write_avro | Serialize a Spark DataFrame into Apache Avro format | 
| spark_write_csv | Write a Spark DataFrame to a CSV | 
| spark_write_delta | Writes a Spark DataFrame into Delta Lake | 
| spark_write_jdbc | Writes a Spark DataFrame into a JDBC table | 
| spark_write_json | Write a Spark DataFrame to a JSON file | 
| spark_write_orc | Write a Spark DataFrame to a ORC file | 
| spark_write_parquet | Write a Spark DataFrame to a Parquet file | 
| spark_write_rds | Write Spark DataFrame to RDS files | 
| spark_write_source | Writes a Spark DataFrame into a generic source | 
| spark_write_table | Writes a Spark DataFrame into a Spark table | 
| spark_write_text | Write a Spark DataFrame to a Text file | 
| src_databases | Show database list | 
| stream_find | Find Stream | 
| stream_generate_test | Generate Test Stream | 
| stream_id | Spark Stream's Identifier | 
| stream_lag | Apply lag function to columns of a Spark Streaming DataFrame | 
| stream_name | Spark Stream's Name | 
| stream_read_cloudfiles | Read files created by the stream | 
| stream_read_csv | Read files created by the stream | 
| stream_read_delta | Read files created by the stream | 
| stream_read_json | Read files created by the stream | 
| stream_read_kafka | Read files created by the stream | 
| stream_read_orc | Read files created by the stream | 
| stream_read_parquet | Read files created by the stream | 
| stream_read_socket | Read files created by the stream | 
| stream_read_table | Read files created by the stream | 
| stream_read_text | Read files created by the stream | 
| stream_render | Render Stream | 
| stream_stats | Stream Statistics | 
| stream_stop | Stops a Spark Stream | 
| stream_trigger_continuous | Spark Stream Continuous Trigger | 
| stream_trigger_interval | Spark Stream Interval Trigger | 
| stream_view | View Stream | 
| stream_watermark | Watermark Stream | 
| stream_write_console | Write files to the stream | 
| stream_write_csv | Write files to the stream | 
| stream_write_delta | Write files to the stream | 
| stream_write_json | Write files to the stream | 
| stream_write_kafka | Write files to the stream | 
| stream_write_memory | Write Memory Stream | 
| stream_write_orc | Write files to the stream | 
| stream_write_parquet | Write files to the stream | 
| stream_write_table | Write Stream to Table | 
| stream_write_text | Write files to the stream |