Makes it easy to import the data from a 'RRD' database
(<https://oss.oetiker.ch/rrdtool/>) directly into R data structures. The
resulting objects are 'tibble' objects or a list of 'tibble' objects, making
it easy to manipulate the data.  The package uses 'librrd' to import the
numerical data in a 'RRD' database directly into R data structures without
using intermediate formats.
librrd: 'librrd-dev' (DEB), 'rrdtool-devel' (RPM),
'rrdtool' (OSX), 'rrdtool' (CSW)

rrdf - support for the Resource Description Framework

rrdflibs - package with Jena libraries for use with rrdf

rrdflibs - package with Jena and Apache HTTP libraries for use
with rrdf
Jena RDF and Apache HTTP Client libraries. Jena and HTTPClient are distributed under their original licenses.

Jena RDF and Apache HTTP Client libraries. Jena and
HTTPClient are distributed under their original licenses.

Environment for Assessing Recommender Systems
c(person("Ludovik","Çoba", role=c("aut","cre","cph"), email="lcoba@unishk.edu.al"),
person("Markus","Zanker", role="ctb",email="Markuz.Zanker@aau.at"))
Provides implementations of several popular recommendation systems. They can process standard recommendation datasets (user/item matrix) as input and generate rating predictions and recommendation lists.  Standard algorithm implementations included in this package are: Global/Item/User-Average baselines, Item-Based KNN, FunkSVD, BPR and weighted ALS. They can be assessed according to the standard offline evaluation methodology for recommender systems using measures such as MAE, RMSE, Precision, Recall, AUC, NDCG, RankScore and coverage measures. The package is intended for rapid prototyping of recommendation algorithms and education purposes.
R (>= 3.1.2), registry, proxy, MASS, stats
Ludovik Çoba [aut, cre, cph],
Markus Zanker [ctb]

R (>= 3.1.2), registry, proxy, MASS, stats, knitr

Environment for Evaluating Recommender Systems
c(person("Ludovik","Çoba", role=c("aut","cre","cph"), email="Ludovik.Coba@inf.unibz.it"),
person("Markus","Zanker", role="ctb",email="Markuz.Zanker@unibz.it"),
person("Panagiotis","Symeonidis", role="ctb",email="Panagiotis.Symeonidis@unibz.it"))
Processes standard recommendation datasets (e.g., a user-item rating matrix) as input and generates rating predictions and lists of recommended items. Standard algorithm implementations which are included in this package are the following: Global/Item/User-Average baselines, Weighted Slope One, Item-Based KNN, User-Based KNN, FunkSVD, BPR and weighted ALS. They can be assessed according to the standard offline evaluation methodology (Shani, et al. (2011) <doi:10.1007/978-0-387-85820-3_8>) for recommender systems using measures such as MAE, RMSE, Precision, Recall, F1, AUC, NDCG, RankScore and coverage measures. The package (Coba, et al.(2017) <doi: 10.1007/978-3-319-60042-0_36>) is intended for rapid prototyping of recommendation algorithms and education purposes.
R (>= 3.1.2), registry, MASS, stats, knitr, ggplot2
Ludovik Çoba [aut, cre, cph],
Markus Zanker [ctb],
Panagiotis Symeonidis [ctb]