'key-value' databases ('Redis'), and (with limitations)

redux, RSQLite (>= 2.2.4), DBI, testthat
Packaged: 2021-07-23 08:50:26 UTC; ralfherold
Author: Ralf Herold [aut, cre] (<https://orcid.org/0000-0002-8148-6748>),

Description: Simplified document database access and manipulation,
providing a common API across supported 'NoSQL' databases
'Elasticsearch', 'CouchDB', 'MongoDB' as well as 'SQLite/JSON1'.
Imports: stringi, jsonlite, jsonify, uuid, jqr, sofa (>= 0.3.0),
X-schema.org-keywords: database, MongoDB, Elasticsearch, CouchDB,
Packaged: 2021-11-21 14:35:09 UTC; ralfherold

Packaged: 2021-11-25 22:46:14 UTC; ralfherold

'Elasticsearch', 'CouchDB', 'MongoDB' as well as
SQLite, PostgreSQL, NoSQL, JSON, documents
Packaged: 2021-12-01 09:25:43 UTC; ralfherold

Packaged: 2022-01-04 11:07:05 UTC; ralfherold

Packaged: 2022-05-04 18:26:21 UTC; ralfherold

Packaged: 2022-06-12 20:26:12 UTC; ralfherold

Packaged: 2022-07-01 13:13:33 UTC; ralfherold

Imports: stringi, jsonlite, jsonify, uuid, jqr, DBI
RSQLite (>= 2.2.4), duckdb (>= 0.6.0), RPostgres, testthat,
SQLite, PostgreSQL, DuckDB, NoSQL, JSON, documents
Packaged: 2022-10-30 15:52:33 UTC; ralfherold

Packaged: 2022-11-20 20:28:26 UTC; ralfherold

Title: Algorithmic Framework for Representational Learning on Graphs
Description: Given any graph, the 'node2vec' algorithm can learn continuous feature representations for the nodes, which can then be used for various downstream machine learning tasks.The techniques are detailed in the paper "node2vec: Scalable Feature Learning for Networks" by Aditya Grover, Jure Leskovec(2016),available at <arXiv:1607.00653>.
Imports: data.table, igraph, word2vec, rlist, dplyr, vctrs, vegan
Packaged: 2020-12-27 15:31:28 UTC; Administrator

Title: Compute and visualize the Node Harvest estimator
Description: Node harvest is a simple interpretable tree-like estimator
for high-dimensional regression and classification. A few nodes
are selected from an initially large ensemble of nodes, each
associated with a positive weight. New observations can fall
into one or several nodes and predictions are the weighted
average response across all these groups. The package offers
visualization of the estimator. Predictions can return the
nodes a new observation fell into, along with the mean response
of training observations in each node, offering a simple