[SPARK-19391][SPARKR][ML] Tweedie GLM API for SparkR

## What changes were proposed in this pull request?
Port Tweedie GLM  #16344  to SparkR

felixcheung yanboliang

## How was this patch tested?
new test in SparkR

Author: actuaryzhang <actuaryzhang10@gmail.com>

Closes #16729 from actuaryzhang/sparkRTweedie.

1 files changed, 18 insertions, 1 deletions

diff --git a/R/pkg/vignettes/sparkr-vignettes.Rmd b/R/pkg/vignettes/sparkr-vignettes.Rmd
index 43c255cff3..a6ff650c33 100644
--- a/R/pkg/vignettes/sparkr-vignettes.Rmd
+++ b/R/pkg/vignettes/sparkr-vignettes.Rmd
@@ -672,6 +672,7 @@ gaussian | identity, log, inverse
 binomial | logit, probit, cloglog (complementary log-log)
 poisson | log, identity, sqrt
 gamma | inverse, identity, log
+tweedie | power link function
 
 There are three ways to specify the `family` argument.
 
@@ -679,7 +680,11 @@ There are three ways to specify the `family` argument.
 
 * Family function, e.g. `family = binomial`.
 
-* Result returned by a family function, e.g. `family = poisson(link = log)`
+* Result returned by a family function, e.g. `family = poisson(link = log)`.
+
+* Note that there are two ways to specify the tweedie family:
+  a) Set `family = "tweedie"` and specify the `var.power` and `link.power`
+  b) When package `statmod` is loaded, the tweedie family is specified using the family definition therein, i.e., `tweedie()`.
 
 For more information regarding the families and their link functions, see the Wikipedia page [Generalized Linear Model](https://en.wikipedia.org/wiki/Generalized_linear_model).
 
@@ -695,6 +700,18 @@ gaussianFitted <- predict(gaussianGLM, carsDF)
 head(select(gaussianFitted, "model", "prediction", "mpg", "wt", "hp"))
 ```
 
+The following is the same fit using the tweedie family:
+```{r}
+tweedieGLM1 <- spark.glm(carsDF, mpg ~ wt + hp, family = "tweedie", var.power = 0.0)
+summary(tweedieGLM1)
+```
+We can try other distributions in the tweedie family, for example, a compound Poisson distribution with a log link:
+```{r}
+tweedieGLM2 <- spark.glm(carsDF, mpg ~ wt + hp, family = "tweedie", 
+                         var.power = 1.2, link.power = 0.0)
+summary(tweedieGLM2)
+```
+
 #### Isotonic Regression
 
 `spark.isoreg` fits an [Isotonic Regression](https://en.wikipedia.org/wiki/Isotonic_regression) model against a `SparkDataFrame`. It solves a weighted univariate a regression problem under a complete order constraint. Specifically, given a set of real observed responses $y_1, \ldots, y_n$, corresponding real features $x_1, \ldots, x_n$, and optionally positive weights $w_1, \ldots, w_n$, we want to find a monotone (piecewise linear) function $f$ to  minimize