earobinson95 Goto Github PK

218-01-lab-intro-to-r

218-02-lab-quantitative-eda

218-03-lab-one-mean-inference

218-04-lab-simple-linear-regression

218-06-lab-paired

218-07-lab-categorical-eda

218-09-lab-two-way-chi-square-test

asymptotics-applications-unlstat984

Showcase in depth understanding of statistical methodologies at an advanced level including: how to reason informally in common statistical settings, apply and prove the basic convergence properties for many statistical quantities, expand statistical quantities for application, etc.

data-for-download

earobinson95.github.io

emilyarobinson--blogdownsite

personal blogdown website

emilyarobinson-unl-dissertation

estimation-for-spatially-dependent-data

example

eye-fitting-straight-lines-in-the-modern-era

game-plans-case

lineups

Shiny front-end/testing framework for lineupgen package

loblollypine-mle-summer2018

Created a code pipeline for genomic prediction in Pinus taeda using Bayesian Generalized Linear Regression. Performed prediction using high dimentional geonomic data and environmental information via multiple cross validation methods.

log-perception-prolific

contains the series of final graphical testing applets for log perception.

logarithmic-lineups

First manuscript in the series of graphical tests on logarithmic scales. Uses visual inference and lineups to evaluate our perception of curvature and exponential growth.

logarithmic-youdrawit

Second manuscript in the series of graphical tests on logarithmic scales. Uses 'You Draw It' feature to evaluate our ability to make predictions of exponential growth.

mickeymouse

Example personal website.

model-averaging-with-super-learner

Improved Classification with Super Learner: Imagine there is a jar of marbles and each person is asked to guess how many marbles are in the jar. Some individuals will guess over and some will guess under. What happens if you average the guesses?

perception-of-statistical-graphics-log

presentations

rweekly.org

R Weekly

sdss-2022-you-draw-it-manuscript

soybean-growth

Implemented non-linear modeling for soybean growth using Gauss-Newton estimation.

stat-331-pa6

stat-331-pa6-71