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Algorithms and utility functions for indoor positioning using fingerprinting techniques. These functions are designed for manipulation of RSSI (Received Signal Strength Intensity) data sets, estimation of positions,comparison of the performance of different models, and graphical visualization of data. Machine learning algorithms and methods such as k-nearest neighbors or probabilistic fingerprinting are implemented in this package to perform analysis and estimations over RSSI data sets.
| Version: | 0.7.2 |
| Depends: | R (≥ 2.10) |
| Imports: | Rcpp, methods, stats, apcluster, cluster, dplyr, ggplot2 |
| LinkingTo: | Rcpp |
| Published: | 2018-01-04 |
| DOI: | 10.32614/CRAN.package.ipft |
| Author: | Emilio Sansano [aut, cre], Raúl Montoliu [ctb] |
| Maintainer: | Emilio Sansano <esansano at uji.es> |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: | yes |
| CRAN checks: | ipft results |
| Reference manual: | ipft.pdf |
| Package source: | ipft_0.7.2.tar.gz |
| Windows binaries: | r-devel: ipft_0.7.2.zip, r-release: ipft_0.7.2.zip, r-oldrel: ipft_0.7.2.zip |
| macOS binaries: | r-release (arm64): ipft_0.7.2.tgz, r-oldrel (arm64): ipft_0.7.2.tgz, r-release (x86_64): ipft_0.7.2.tgz, r-oldrel (x86_64): ipft_0.7.2.tgz |
| Old sources: | ipft archive |
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