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Package: SemiSupervised | ||
Type: Package | ||
Title: Safe Semi-Supervised Learning Tools | ||
Version: 1.0 | ||
Date: 2018-5-07 | ||
Author: Mark Vere Culp | ||
Maintainer: Mark Vere Culp <mvculp@mail.wvu.edu> | ||
Depends: R (>= 2.10),methods | ||
Suggests: caret,mlbench,kernlab,randomForest,glmnet,spa,e1071 | ||
LazyLoad: Yes | ||
Description: Implements several safe graph-based semi-supervised learning algorithms. The first algorithm is the Semi-Supervised Semi-Parametric Model (S4PM) and the fast Anchor Graph version of this approach. For additional technical details, refer to Culp and Ryan (2013) <http://jmlr.org/papers/v14/culp13a.html>, Ryan and Culp (2015) <http://www.jmlr.org/papers/v16/ryan15a.html> and the package vignette. The underlying fitting routines are executed in C++. All tuning parameter estimation is optimized using K-fold Cross-Validation. | ||
License: GPL-2 | ||
NeedsCompilation: yes | ||
Repository: CRAN | ||
Packaged: 2018-05-09 18:36:30 UTC; mculp | ||
Date/Publication: 2018-05-11 09:59:25 UTC |
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295f143a4de737f28b1d9e7b0ce243b2 *DESCRIPTION | ||
2828319ec6d1592799b2c4fda2dad579 *NAMESPACE | ||
222bc2e1f5850ffd4af2764fc4f494fb *R/A_SemiSupervised-objects.R | ||
891a9f3a93b4ddf0de3bb4ac164d7610 *R/SemiSupervised-external.R | ||
f5d5f355222c005203ab5eea5f9f9cdf *R/SemiSupervised-internal.R | ||
6a1c4c5e609028f02c710e64cc88d38b *R/agraph-external.R | ||
093a7d11a90cd28eaf02452d200695e1 *R/agraph-internal.R | ||
0d9a30b5c0aae0260b75f0fcc73cc80d *R/jtharm-external.R | ||
fb98223e4c98d75ca51a2fe3384bc666 *R/jtharm-internal.R | ||
fa5d7a2dbfca0af3e72404f3084fac32 *R/s4pm-external.R | ||
42aea3df3dc6fe6063a6ecdc5580e65e *R/s4pm-internal.R | ||
3ad9fd50bef159f13b3af3bc0ff91d46 *build/vignette.rds | ||
e847b72bf7b5ae52f98942ea2ccc91c2 *data/powerplant.rda | ||
6444b4777f64c0d64fa0ca4f2ae468f9 *demo/00Index | ||
c4822d776710ed3f68ed209e2850e762 *demo/SemiSupervised.R | ||
53257358fed79064638115c33eac8cd9 *inst/doc/SemiSupervised.Rnw | ||
7ad75a67c3a9ecefc08541ea5339a229 *inst/doc/SemiSupervised.pdf | ||
4faa91a89978a46632a22e3a85833e81 *inst/document_code/Generate_Figures.R | ||
951bba25b20d1e929c6f512c4384abf4 *inst/document_code/Generate_Tables.R | ||
22a14bbae95e01eb03b8861f7ea76a85 *inst/document_code/README.txt | ||
d5b7b20fa3ad74238128b517bf329cd5 *inst/document_code/csvs/casp_CPU.csv | ||
3a4614a1efe2912e25c101356f7d3219 *inst/document_code/csvs/casp_change_m.csv | ||
241763063f8b0f0265571629e5c8d8ab *inst/document_code/csvs/perf_results.csv | ||
0fa3e96afe09612ac7bab8f08441d566 *inst/document_code/run_casp_CPU.R | ||
67f6f90417a732290291a28cabe40b74 *inst/document_code/run_casp_m.R | ||
ff5f8be7ef89224c5432fb6cb1a28990 *inst/document_code/run_perforamcne.R | ||
caff88e0e5ab4ee2deee5bb8e34f6226 *inst/document_code/source/base.R | ||
fc774368d6c6a707c218ea043e2d93a9 *inst/document_code/source/data_reader.R | ||
84e0e821eadc4b52deec1a60b104cd3c *man/AnchorGraph.Rd | ||
47489aad4e541ab5539cc0cee81f9ebc *man/SemiSupervised-class.Rd | ||
8a518dc2949dd61d70e21aaf625634f1 *man/SemiSupervised.control.Rd | ||
b4098c0eb9676e537e9415e65c3467be *man/agraph-class.Rd | ||
85cc31984483ddeef3406977f8bdbb4f *man/agraph.Rd | ||
0b16e948ecd1037c2f425d009af83450 *man/cv.folds.Rd | ||
7822f595602ccf5b83ad8fcb1f8f1c83 *man/dG.Rd | ||
dd710550f347492888440c9e951a2dde *man/impute.median.Rd | ||
8d3dba44bce81552956defd8a5ebb64d *man/jtharm-class.Rd | ||
901d92e7691e8402a47b95f5ace3fd32 *man/jtharm.Rd | ||
c5df96223f3b0f915072249c6ca8e2d9 *man/knnGraph.Rd | ||
d0a841f9f4aa1111c6f957b4d343ca68 *man/powerplant.Rd | ||
49c12e55578920efc1a4eb7e33e4ce1e *man/predict.agraph.Rd | ||
1780bc90906d99eb925bd8f3542738cf *man/predict.jtharm.Rd | ||
1832272a84ea162647b3fcd016e4f0ed *man/predict.s4pm.Rd | ||
b0d24806855681f643523b9b406897c4 *man/s4pm-class.Rd | ||
ee08a496eba159ee0f2de30bf6c6ab3f *man/s4pm.Rd | ||
a2b1c960916bcb6c13df3f893a557241 *man/x.scaleL.Rd | ||
4706e6fcaa5b25fec18d91b87d6cd2c5 *src/Anchor_Reg.c | ||
e84d0733e0a7aa1e3f78a3b142a19eee *src/Anchor_Reg.h | ||
270770188decaad651d1eabb3fb3b863 *src/LAE.c | ||
fa5292b8172854610c6bb85fd86076bb *src/LAE.h | ||
2a6f9e9e044a78154d3cfda5936d6f48 *src/Makevars | ||
8304157c6b2083ebd369eb5fdb0b7b7e *src/general.c | ||
9d54c4e82b79a58f027715d4fa454c28 *src/general.h | ||
72a5911d6aed23c08696c036f7005ea0 *src/joint_harmonic.c | ||
6efac7dae71afb1da0fed59f4e752e45 *src/joint_harmonic.h | ||
6f3ba6d17963292201fdd6ca4529a5cd *src/s4pm.c | ||
7436e57f84c3911c13afc8679b0b303a *src/s4pm.h | ||
17a88293b73a58f5e0eead21a46487e2 *src/win_defs.c | ||
7167c5b3fa63b759fa52b2692f887385 *src/win_defs.h | ||
53257358fed79064638115c33eac8cd9 *vignettes/SemiSupervised.Rnw | ||
e20a446d5206887a2b7e97b137b76d3d *vignettes/SemiSupervised.bib | ||
851b1e8cdc8913b1d99a0bb60b37a25e *vignettes/fig1a.pdf | ||
585e97d695db445b591ee3c8d5097356 *vignettes/fig1b.pdf | ||
60aa9f24585fe0541eaa651a20dc4c9e *vignettes/fig_time1.pdf | ||
939c21ca220255fc5a905590bda72796 *vignettes/fig_time2.pdf |
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useDynLib("SemiSupervised",.registration=TRUE,.fixes = "C_") | ||
import("methods") | ||
importFrom("stats", "as.formula", "delete.response", "dist", "kmeans", | ||
"median", "model.frame", "model.matrix", "model.response", | ||
"na.pass", "quantile", "terms","na.omit") | ||
export( | ||
"agraph", | ||
"agraph.default", | ||
"jtharm", | ||
"jtharm.default", | ||
"s4pm", | ||
"s4pm.default", | ||
"SemiSupervised.control", | ||
"AnchorGraph", | ||
"knnGraph", | ||
"kgraph.predict", | ||
"kgraph.symm", | ||
"epsGraph", | ||
"eps.predict", | ||
"eps.symm", | ||
"impute.median", | ||
"dG", | ||
"sG", | ||
"aG", | ||
"x.scaleL", | ||
"getAnchor", | ||
"cv.folds", | ||
"h.est", | ||
"extract.a", | ||
"extract.xg", | ||
"extract.g", | ||
"inter.predict", | ||
"euclidDist", | ||
"cosineDist", | ||
"ymatrix", | ||
"xmatrix", | ||
"gmatrix", | ||
"measures", | ||
"type", | ||
"lparm", | ||
"gparm", | ||
"hparm", | ||
"ymatrix<-", | ||
"xmatrix<-", | ||
"gmatrix<-", | ||
"measures<-", | ||
"type<-", | ||
"lparm<-", | ||
"gparm<-", | ||
"hparm<-") | ||
|
||
exportMethods("fitted","dim","show","predict") | ||
exportClasses("SemiSupervised","parm") |
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