The TempleMetrics package is a collection of functions implemented by members of the Econometrics Reading Group at Temple University. The main functions (at the moment) are built for distribution regression. That is, one can estimate the distribution of (Y) conditional on (X) using a model for a binary outcome. For example, where () is some known link function, such as logit.

You can install TempleMetrics from github with:

```
# install.packages("devtools")
devtools::install_github("bcallaway11/TempleMetrics")
#> Skipping install of 'TempleMetrics' from a github remote, the SHA1 (2229b379) has not changed since last install.
#> Use `force = TRUE` to force installation
```

or from CRAN using

`install.packages("TempleMetrics")`

The first example is how to run distribution regression for a single value of (y) of (Y). This example uses the `igm`

dataset which is a collection of 500 parent-child pairs of income along with the parent’s education level which comes from the Panel Study of Income Dynamics (PSID).

```
library(TempleMetrics)
data(igm)
head(igm)
#> lcfincome lfincome HEDUC
#> 1943 11.599712 11.46909 HS
#> 823 11.396635 10.71540 HS
#> 597 11.805380 10.87307 HS
#> 1291 11.007568 11.45044 HS
#> 3333 10.915150 11.30180 HS
#> 2717 9.764008 10.80891 LessHS
```

```
y0 <- median(igm$lcfincome)
dreg <- distreg(lcfincome ~ lfincome + HEDUC, igm, y0)
dreg
#> $yvals
#> [1] 11.04563
#>
#> $glmlist
#> $glmlist[[1]]
#>
#> Call: glm(formula = formla, family = binomial(link = link), data = dta)
#>
#> Coefficients:
#> (Intercept) lfincome HEDUCHS HEDUCLessHS
#> 15.3320 -1.3976 0.1629 0.2256
#>
#> Degrees of Freedom: 499 Total (i.e. Null); 496 Residual
#> Null Deviance: 693.1
#> Residual Deviance: 640.9 AIC: 648.9
#>
#>
#> attr(,"class")
#> [1] "DR"
```

In many cases, of primary interest with distribution regression is obtaining (_{Y|X}(y|x)) for some particular values of (y) and (x). That’s what we do in this example.

```
yvals <- seq(quantile(igm$lcfincome,.05,type=1),
quantile(igm$lcfincome,.95, type=1), length.out=100)
dres <- distreg(lcfincome ~ lfincome + HEDUC, igm, yvals)
xdf <- data.frame(lfincome=10, HEDUC="LessHS")
y0 <- yvals[50]
ecdf(igm$lcfincome)(y0)
#> [1] 0.328
Fycondx(dres, y0, xdf)
#> [[1]]
#> Empirical CDF
#> Call: NULL
#> x[1:100] = 9.6856, 9.7073, 9.7291, ..., 11.814, 11.836
```

This example says that: (1) the fraction of “children” in the dataset with income below 46628 is 0.33, but (2) we estimate that the fraction of children whose parent’s income is 22026 and have parent’s with less than a HS education with income below 46628 is 0.73.