README.Rmd

---
output: github_document
---

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```{r, echo = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "README-",
  eval = FALSE
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# hpiR

![](/images/hpiR.png){width=100px, height=100px}

This package intends to simplify and standardize the creation of house price indexes in R.  It also provides a framework for judging the quality of a given index by testing for predictive accuracy, volatility and revision.  By providing these metrics various index methods (and estimators) can be accurately compared against each other.  

While there are a (ever-increasing) variety of methods and models to use in house price index creation, this initial version (0.3.0) focuses on the two most common: repeat sales (transactions) and hedonic price.  Base, robust and weighted estimators are provided when appropriate.  Additionally, a new method using random forests and a post model interpretability method -- partial dependence plots -- is also used. 

The package also includes a dataset of single family and townhome sales from the City of Seattle during January 2010 to December 2016 time period.

Please see the [vignette](https://github.com/andykrause/hpiR/blob/master/vignettes/introduction.Rmd) for more information on using the package.

Also, please log issues or pull requests on this [github page](http://www.github.com/andykrause/hpiR).

## Installation

You can install hpiR from github with:

**Install the released version from CRAN**

```{r, eval=FALSE}
  install.packages("hpiR")
```

**Development version from GitHub:**

```{r, eval=FALSE}
  #install.packages("devtools")
  devtools::install_github("andykrause/hpiR")
```
## Example

This is a basic example which shows you how to solve a common problem:

```{r example}

  library(hpiR)

  # Load prepared data
  data(ex_rtdata)

  # Create an index
  hpi <- rtIndex(trans_df = ex_rtdata,
                 estimator = 'robust',
                 log_dep = TRUE,
                 trim_model = TRUE,
                 smooth = TRUE)

```