Estimate ED50 and Its Confidence Interval
Functions of five estimation method for ED50 (50 percent effective dose) are provided, and they are respectively
Dixon-Mood method (1948) <doi:10.2307/2280071>, Choi's original turning point method (1990) <doi:10.2307/2531453> and it's modified version given by
us, as well as logistic regression and isotonic regression. Besides, the package also supports
comparison between two estimation results.

Fengru Wang, Yongbo Gan, Zhijian Yang, Wei Mei

Experimental Design and Analysis for Tree Improvement
c(
person(c("Muhammad", "Yaseen"),  email = "myaseen208@gmail.com", role = c("aut", "cre"))
, person(c("Sami", "Ullah"),  email = "samiullahuos@@gmail.com", role = c("aut", "ctb"))
)
Provides data sets and R Codes for Williams, E.R., Matheson, A.C. and Harwood, C.E. (2002). Experimental Design and Analysis for Tree Improvement, CSIRO Publishing.
tidyverse, stats, dplyr, ggplot2, magrittr, dae
Muhammad Yaseen [aut, cre],
Sami Ullah [aut, ctb]

dplyr, dae, emmeans, ggplot2, lme4, magrittr, pbkrtest, stats,
tidyverse

c(
person(
c("Muhammad", "Yaseen")
, email = "myaseen208@gmail.com"
, role = c("aut", "cre", "cph")
, comment = c(ORCID = "0000-0002-5923-1714")
)
, person(
c("Sami", "Ullah")
, email = "samiullahuos@gmail.com"
, role = c("aut", "ctb")
)
)
Muhammad Yaseen [aut, cre, cph]
(<https://orcid.org/0000-0002-5923-1714>),
Sami Ullah [aut, ctb]

Exploratory Data Analysis using Random Forests
Functions useful for exploratory data analysis
using random forests which can be used to compute multivariate partial
dependence, observation, class, and variable-wise marginal and joint permutation
importance as well as observation-specific measures of distance
(supervised or unsupervised). All of the aforementioned functions are
accompanied by 'ggplot2' plotting functions.
Zachary M. Jones <zmj@zmjones.com> and Fridolin Linder
<fridolin.linder@gmail.com>
foreach, iterators, ggplot2, assertthat, reshape2, plyr
party, randomForest, randomForestSRC, doParallel, testthat,
rmarkdown, knitr