forked from paobranco/UBL
-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathNAMESPACE
67 lines (58 loc) · 1.58 KB
/
NAMESPACE
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
## ------------------------------------------------------------------------------------------------
useDynLib(UBL, .registration=TRUE)
## ------------------------------------------------------------------------------------------------
import(methods)
importFrom("grDevices", "boxplot.stats")
importFrom("stats", "rnorm", "runif", "sd", "approxfun", "density", "isoreg", "loess",
"loess.control", "predict")
importFrom("graphics", "contour", "image", "points")
importFrom("MBA", "mba.points", "mba.surf")
importFrom("randomForest", "randomForest")
importFrom("automap", "autoKrige")
importFrom("sp", "coordinates<-", "SpatialPoints")
importFrom("gstat", "idw")
## ------------------------------------------------------------------------------------------------
## Functions
export(
##classification pre-processing methods
AdasynClassif,
CNNClassif,
ENNClassif,
GaussNoiseClassif,
ImpSampClassif,
NCLClassif,
OSSClassif,
RandOverClassif,
RandUnderClassif,
SmoteClassif,
TomekClassif,
## regression pre-processing methods
GaussNoiseRegress,
ImpSampRegress,
RandOverRegress,
RandUnderRegress,
SmoteRegress,
## phi related function
phi.control,
# phi.setup,
# phi.extremes,
# phi.range,
phi,
#tPhi,
#BL,
#UtilNewRegress,
##surface interpolation methods
UtilInterpol,
## utility-based evaluation metrics for classification and regression
EvalClassifMetrics,
EvalRegressMetrics,
## utility-based optimal predictions
UtilOptimClassif,
UtilOptimRegress,
## utility-based learning
#MetacostClassif,
#MetacostRegress,
## neighbours function
neighbours,
distances
)