The goal of plm2024 is to …
You can install the development version of plm2024 from GitHub with:
# install.packages("devtools")
devtools::install_github("Goodgolden/plm2024")This is a basic example which shows you how to solve a common problem:
library(plm2024)
#> Loading required package: brokenstick
#> Loading required package: broom.mixed
#> Loading required package: dplyr
#>
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#>
#> filter, lag
#> The following objects are masked from 'package:base':
#>
#> intersect, setdiff, setequal, union
#> Loading required package: forcats
#> Loading required package: gamlss
#> Loading required package: splines
#> Loading required package: gamlss.data
#>
#> Attaching package: 'gamlss.data'
#> The following object is masked from 'package:datasets':
#>
#> sleep
#> Loading required package: gamlss.dist
#> Loading required package: MASS
#>
#> Attaching package: 'MASS'
#> The following object is masked from 'package:dplyr':
#>
#> select
#> Loading required package: nlme
#>
#> Attaching package: 'nlme'
#> The following object is masked from 'package:dplyr':
#>
#> collapse
#> Loading required package: parallel
#> ********** GAMLSS Version 5.1-7 **********
#> For more on GAMLSS look at http://www.gamlss.com/
#> Type gamlssNews() to see new features/changes/bug fixes.
#> Loading required package: here
#> here() starts at /Users/goodgolden5/Desktop/plm2024
#> Loading required package: janitor
#>
#> Attaching package: 'janitor'
#> The following objects are masked from 'package:stats':
#>
#> chisq.test, fisher.test
#> Loading required package: JMbayes
#> Loading required package: survival
#> Loading required package: doParallel
#> Loading required package: foreach
#> Loading required package: iterators
#> Loading required package: rstan
#> Loading required package: StanHeaders
#> Loading required package: ggplot2
#> Warning: package 'ggplot2' was built under R version 4.2.3
#> rstan (Version 2.21.8, GitRev: 2e1f913d3ca3)
#> For execution on a local, multicore CPU with excess RAM we recommend calling
#> options(mc.cores = parallel::detectCores()).
#> To avoid recompilation of unchanged Stan programs, we recommend calling
#> rstan_options(auto_write = TRUE)
#>
#> Attaching package: 'JMbayes'
#> The following object is masked from 'package:gamlss.data':
#>
#> aids
#> Loading required package: lme4
#> Warning: package 'lme4' was built under R version 4.2.3
#> Loading required package: Matrix
#> Warning: package 'Matrix' was built under R version 4.2.3
#>
#> Attaching package: 'lme4'
#> The following object is masked from 'package:gamlss':
#>
#> refit
#> The following object is masked from 'package:nlme':
#>
#> lmList
#> Loading required package: matrixcalc
#> Loading required package: rjags
#> Loading required package: coda
#>
#> Attaching package: 'coda'
#> The following object is masked from 'package:rstan':
#>
#> traceplot
#> Linked to JAGS 4.3.2
#> Loaded modules: basemod,bugs
#> Loading required package: shiny
#> Warning: package 'shiny' was built under R version 4.2.3
#> Loading required package: tibble
#> Loading required package: tidyr
#> Warning: package 'tidyr' was built under R version 4.2.3
#>
#> Attaching package: 'tidyr'
#> The following objects are masked from 'package:Matrix':
#>
#> expand, pack, unpack
#> The following object is masked from 'package:rstan':
#>
#> extract
#> Loading required package: tidyverse
#> Warning: package 'stringr' was built under R version 4.2.3
#> ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
#> ✔ lubridate 1.9.3 ✔ readr 2.1.5
#> ✔ purrr 1.0.2 ✔ stringr 1.5.1
#> ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
#> ✖ purrr::accumulate() masks foreach::accumulate()
#> ✖ nlme::collapse() masks dplyr::collapse()
#> ✖ tidyr::expand() masks Matrix::expand()
#> ✖ tidyr::extract() masks rstan::extract()
#> ✖ dplyr::filter() masks stats::filter()
#> ✖ dplyr::lag() masks stats::lag()
#> ✖ tidyr::pack() masks Matrix::pack()
#> ✖ MASS::select() masks dplyr::select()
#> ✖ tidyr::unpack() masks Matrix::unpack()
#> ✖ purrr::when() masks foreach::when()
#> ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
#>
#> Welcome to my package; this is a package
#> developed for Randy Jin's MS thesis
#>
#>
#> Attaching package: 'plm2024'
#>
#>
#> The following object is masked from 'package:base':
#>
#> match
## basic example codeWhat is special about using README.Rmd instead of just README.md?
You can include R chunks like so:
In that case, don’t forget to commit and push the resulting figure files, so they display on GitHub and CRAN.