---
title: "UC Beamer Template"
author: "Saannidhya Rawat"
date: "`r format(Sys.Date(), '%B %e, %Y')`"
output:
  Rbearcat::bcat_beamer: default
biblio-style: apalike
institute: University of Cincinnati
link-citations: yes
bibliography: references.bib
aspectratio: 169
classoption: t, table
mainfont: Open Sans
---

```{r setup, echo=FALSE, message=FALSE, warning=FALSE}

# load packages
library(Rbearcat)
library(tidyverse)
library(lubridate)
library(haven)
library(stringr)
library(here)
library(knitr)
library(janitor)
library(scales)
library(viridis)
library(RColorBrewer)
library(kableExtra)
library(flextable)
# include additional packages here (if needed)
# library(DT)
# library(ggrepel)

# set default Rmd options
Rbearcat::bcat_setup_rmd()

# set default UC geoms
Rbearcat::set_UC_geoms()

# output type
doc_type <- knitr::opts_knit$get('rmarkdown.pandoc.to')

```

# A Slide

Some bullets!

* item 1
* item 2
	* sub-item 1
	* sub-item 2

# Another Slide

A pretty plot:

```{r line-plot, fig.cap="A Line Plot"}

# using function from Rbearcat package
Rbearcat::bcat_plt_line(df = economics,
                     x = date,
                     y = unemploy)

```

# A nicely formatted table:

```{r table}

Rbearcat::bcat_fmt_style_table(iris[1:10,])

```

# Some math 

A vector of observations $y$ having $n$ components is assumed to be a realization of a random variable $Y$ whose components are independently distributed with means $\mu$.

In the original formulation of GLMs, the assumed distribution of $Y$ is a member of an *exponential family* which have a probability density function of form:

$$f(y_i) = exp\{\frac{y_i\theta_i - b(\theta_i)}{a_i(\phi)} + c(y_i, \phi)\}$$

where $\theta_i$ and $\phi$ are parameters and $a(\cdot)$, $b(\cdot)$, and $c(\cdot)$ are known functions.

# Technical Notes

This document was written in R Markdown, using the `rmarkdown` [@xie-markdown] and `knitr` [@xie-knitr] packages.

# References 
