**Rdimtools** is an R package for dimension reduction (DR) - including feature selection and manifold learning - and intrinsic dimension estimation (IDE) methods. We aim at building one of the *most comprehensive* toolbox available online, where current version delivers 145 DR algorithms and 17 IDE methods.

The philosophy is simple, **the more we have at hands, the better we can play**.

Our logo characterizes the foundational nature of multivariate data analysis; we may be blind people wrangling the data to see an elephant to grasp an idea of what the data looks like with partial information from each algorithm.

You can install a release version from CRAN:

`install.packages("Rdimtools")`

or the development version from github:

```
## install.packages("devtools")
devtools::install_github("kisungyou/Rdimtools")
```

Here is an example of dimension reduction on the famous `iris`

dataset. Principal Component Analysis (`do.pca`

), Laplacian Score (`do.lscore`

), and Diffusion Maps (`do.dm`

) are compared, each from a family of algorithms for linear reduction, feature extraction, and nonlinear reduction.

```
# load the library
library(Rdimtools)
# load the data
X = as.matrix(iris[,1:4])
lab = as.factor(iris[,5])
# run 3 algorithms mentioned above
mypca = do.pca(X, ndim=2)
mylap = do.lscore(X, ndim=2)
mydfm = do.dm(X, ndim=2, bandwidth=10)
# visualize
par(mfrow=c(1,3))
plot(mypca$Y, pch=19, col=lab, xlab="axis 1", ylab="axis 2", main="PCA")
plot(mylap$Y, pch=19, col=lab, xlab="axis 1", ylab="axis 2", main="Laplacian Score")
plot(mydfm$Y, pch=19, col=lab, xlab="axis 1", ylab="axis 2", main="Diffusion Maps")
```

Swill Roll is a classic example of 2-dimensional manifold embedded in ℝ^{3} and one of 11 famous model-based samples from `aux.gensamples()`

function. Given the ground truth that *d* = 2, let’s apply several methods for intrinsic dimension estimation.

```
# generate sample data
set.seed(100)
roll = aux.gensamples(dname="swiss")
# we will compare 6 methods (out of 17 methods from version 1.0.0)
vecd = rep(0,5)
vecd[1] = est.Ustat(roll)$estdim # convergence rate of U-statistic on manifold
vecd[2] = est.correlation(roll)$estdim # correlation dimension
vecd[3] = est.made(roll)$estdim # manifold-adaptive dimension estimation
vecd[4] = est.mle1(roll)$estdim # MLE with Poisson process
vecd[5] = est.twonn(roll)$estdim # minimal neighborhood information
# let's visualize
plot(1:5, vecd, type="b", ylim=c(1.5,2.5),
main="true dimension is d=2",
xaxt="n",xlab="",ylab="estimated dimension")
xtick = seq(1,5,by=1)
axis(side=1, at=xtick, labels = FALSE)
text(x=xtick, par("usr")[3],
labels = c("Ustat","correlation","made","mle1","twonn"), pos=1, xpd = TRUE)
```

We can observe that all 5 methods we tested estimated the intrinsic dimension around *d* = 2. It should be noted that the estimated dimension may not be integer-valued due to characteristics of each method.

The logo icon is made by Freepik from www.flaticon.com.The rotating Swiss Roll image is taken from Dinoj Surendran’s website.