Java version 8 or higher (https://www.java.com/)
rJava, SqlRender (>= 1.6.3), methods, stringr, rlang, utils,
DBI (>= 1.0.0), urltools, bit64
aws.s3, R.utils, withr, testthat, DBItest, knitr, rmarkdown,
RSQLite, ssh, Andromeda, dplyr

rJava, SqlRender (>= 1.7.0), methods, stringr, rlang, utils,
DBI (>= 1.0.0), urltools, bit64

An R 'DataBase Interface' ('DBI') compatible interface to various database platforms ('PostgreSQL', 'Oracle', 'Microsoft SQL Server',
'Amazon Redshift', 'Microsoft Parallel Database Warehouse', 'IBM Netezza', 'Apache Impala', 'Google BigQuery', 'Spark', and 'SQLite'). Also includes support for
fetching data as 'Andromeda' objects. Uses 'Java Database Connectivity' ('JDBC') to connect to databases (except SQLite).
rJava, SqlRender (>= 1.7.0), methods, stringr, readr, rlang,
utils, DBI (>= 1.0.0), urltools, bit64
aws.s3, R.utils, withr, testthat, DBItest, knitr, rmarkdown,
RSQLite, ssh, Andromeda (>= 0.5.0), dplyr

rJava, SqlRender (>= 1.8.3), methods, stringr, readr, rlang,
utils, DBI (>= 1.0.0), urltools, bit64
aws.s3, R.utils, withr, testthat, DBItest, knitr, rmarkdown,
RSQLite, ssh, Andromeda (>= 0.6.0), dplyr

An R 'DataBase Interface' ('DBI') compatible interface to various database platforms ('PostgreSQL', 'Oracle', 'Microsoft SQL Server',
'Amazon Redshift', 'Microsoft Parallel Database Warehouse', 'IBM Netezza', 'Apache Impala', 'Google BigQuery', 'Snowflake', 'Spark', and 'SQLite'). Also includes support for
fetching data as 'Andromeda' objects. Uses 'Java Database Connectivity' ('JDBC') to connect to databases (except SQLite).

JAR Dependencies for the 'DatabaseConnector' Package
Provides external JAR dependencies for the 'DatabaseConnector' package.

Swarm Intelligence for Self-Organized Clustering
Algorithms implementing populations of agents which interact with one another and sense their environment may exhibit emergent behavior such as self-organization and swarm intelligence. Here a swarm system, called databionic swarm (DBS), is introduced which is able to adapt itself to structures of high-dimensional data such as natural clusters characterized by distance and/or density based structures in the data space. The first module is the parameter-free projection method Pswarm, which exploits the concepts of self-organization and emergence, game theory, swarm intelligence and symmetry considerations. The second module is a parameter-free high-dimensional data visualization technique, which generates projected points on a topographic map with hypsometric colors based on the generalized U-matrix. The third module is the clustering method itself with non-critical parameters. The clustering can be verified by the visualization and vice versa. The term DBS refers to the method as a whole. DBS enables even a non-professional in the field of data mining to apply its algorithms for visualization and/or clustering to data sets with completely different structures drawn from diverse research fields.
plotrix, geometry, sp, spdep, AdaptGauss, ABCanalysis,
parallel, matrixStats, rgl, png, ProjectionBasedClustering