Implementation of S3 methods for describe

Implementation of S3 method for describe from Hmisc.
Renders simple statistics.
**Basic examples**
```
library(Hmisc)
set.seed(1)
x <- describe(runif(200))
pander(x)
pander(x, short = FALSE)
dfr <- data.frame(x=rnorm(400),y=sample(c('male','female'),400,TRUE))
y <- describe(dfr)
pander(y)
```
There is one thing I could not decide for 2nd example. When rendering description, how is the best way to separate information for variables.
For example, printing for y (separates with dashed lines)
```
d> y
dfr 

 2  Variables      400  Observations
-------------------------------------------------------------------------------------------------------------------------------------------------------------------------
x 
       n  missing   unique     Mean      .05      .10      .25      .50      .75      .90      .95 
     400        0      400 0.001083 -1.64182 -1.32308 -0.64280 -0.05831  0.67754  1.35234  1.72182 

lowest : -3.008 -2.889 -2.592 -2.403 -2.343, highest:  2.351  2.447  2.498  2.649  3.810 
-------------------------------------------------------------------------------------------------------------------------------------------------------------------------
y 
      n missing  unique 
    400       0       2 

female (187, 47%), male (213, 53%) 
-------------------------------------------------------------------------------------------------------------------------------------------------------------------------
```
And with how I implemented it 
```
d> pander(y, split.tables = Inf)
dfr 

 2  Variables	 400  Observations

-------------------------------------------------------------------------------------------
 n   missing   unique    Mean     .05      .10      .25      .50      .75     .90     .95  
--- --------- -------- -------- -------- -------- -------- -------- ------- ------- -------
400     0       400    0.001083 -1.64182 -1.32308 -0.64280 -0.05831 0.67754 1.35234 1.72182
-------------------------------------------------------------------------------------------

Table: Variable x


* lowest: -3.008 -2.889 -2.592 -2.403 -2.343 
* highest:  2.351  2.447  2.498  2.649  3.810 
 

----------------------
 n   missing   unique 
--- --------- --------
400     0        2    
----------------------

Table: Variable y

female (187, 47%), male (213, 53%) 
```