In npjc/growr: High level functions to help summarise growth curve fits.

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)
library(tidyverse)
library(tidytext)
db <- tools::CRAN_package_db()
d <- as_tibble(db, .name_repair = 'unique')


tokens <- d %>% 
    select(Package, Title, Description) %>% 
    unite(title_description, Title, Description, sep = ' ') %>% 
    unnest_tokens(word, title_description)
clean_tokens <- tokens %>% 
    anti_join(get_stopwords(), by = "word") 

Find other CRAN packages with similar scope

• I tokenized the title and description sections of all CRAN packages.
• Cleaned them by removing stop words.

query <- 'growt?h?'
matches <- clean_tokens %>% 
  filter(str_detect(word, query)) %>% 
  distinct(Package)

Read through

d %>% 
  semi_join(matches, by = "Package") %>% 
  select(Package, Title, Description) %>% 
  gt::gt()

to_exclude <- trible(
  ~pkg, ~reason,
  'AGD',
  'AMORE',
  'aqp',
  'BIOdry',
  'childsds',
  'chillR',
  'BIOdry',
  'childsds',
  'chillR',
  'colorednoise',
  'dcv',
  'DDM',
  'DendroSync',
  'dequer',
  'Dowd',
  'dplRCon',
  'Ecohydmod',
  'embyrogrowth',
  'erer',
  'FCGR',
  'fcr',
  'fdq',
  'Fgmutils',
  'fishdata',
  'forestmangr',
  'frambgrowth',
  'GDELTtools',
  'ggfittext',
  'GDELTtools',
  'GillespieSSA', # perhaps usefl but orhphaned and oto complicated.
  'GlobalFit', # mayb useufl but too complicated
  'gym',
  'halfcircle',
  'imageData',
  'InfDim',
  'IPMpack',
  'iRF',
  'ITGM',
  'jubilee',
  'Lambda4',
  'landscapeR',
  'LifeHist',
  'live',
  'memnet',
  'MST',
  'multilaterals',
  'nsRFA',
  'numOSL',
  'osmose',
  'OTE',
  'PCMBase',
  'PointFore',
  'pollen',
  'popdemo',
  'Pstat',
  'quint',
  randomUniformForest
  rmutil
  rpartScore
  RStorm
  rtrim
  sclero
  SeedCalc
  SGP
  SGPdata
  ShapePattern
  ShapeSelectForest
  shelltrace
  sitreeE
  sizeMat
  stocks
  StratifiedRF
  streambugs
  sybilccFBA # flux balance analysis
  TGS
  TTCA
  ufs
  vegperiod
  zipfR
  zscorer







)
to_exclude

Test Drive Other Packages:

babar

# install.packages('babar')
library(babar)
utils::browseVignettes('babar')

Bayesian fit of standard growth models:

1. Bayesfit() one of linear, logistic or Baranyi. 3,4,6 parameter variants of baranyi models available.
2. nested sampling to compare if two data samples represent the same underlying sample (like a t-test but bayes style).
3. calculate baye's evidence for each model and return the difference between the two.
4. use jeffrey's scale to extract the answer.

BaTLFED3D

too complicated/unclear if appropriate and I don't understand how to use confidently.

blavaan

latent growth curve models, requires I brew install jags and get the blavaan and runjags and rjags

# install.packages('blavaan')
# install.packages("runjags")
# install.packages("rjags")
library(blavaan)
# utils::browseVignettes('blavaan')
library(runjags)
library(rjags)
# brew install jags
?bgrowth

model.syntax <- '
  # intercept and slope with fixed coefficients
    i =~ 1*t1 + 1*t2 + 1*t3 + 1*t4
    s =~ 0*t1 + 1*t2 + 2*t3 + 3*t4

  # regressions
    i ~ x1 + x2
    s ~ x1 + x2

  # time-varying covariates
    t1 ~ c1
    t2 ~ c2
    t3 ~ c3
    t4 ~ c4
'
Demo.growth
fit <- bgrowth(model.syntax, data=Demo.growth)
summary(fit)

cellVolumeDist

install.packages('cellVolumeDist')

diffusion

Model innovation/ new product diffusion into a population over time. This seems to be a companion package to the 25 year review published at 10.1016/j.ijforecast.2006.01.005.

diskImageR

disk diffusion drug resistance microbes on plates.

epitrix

Just the one function that computes R0 dist from growth rate vector seems interesting.

http://www.repidemicsconsortium.org/epitrix/reference/r2R0.html

eyetrackingR

has a vignette on growth curve analysis but it is mostly confusing..fits polynomial models..

facilitation

full on simulation of individual-based models to reconstitue plant-plant interaction dyanmics.

# install.packages('facilitation')
library(facilitation)
browseVignettes('facilitation')
rates <- matrix(c(1,0,2),nrow=1) # parameters must be in a matrix
results <- community(maxtime=2,numstages=1,parameters=rates,init=10)
results$data %>% 
  ggplot(aes(x,y, label = id)) + 
  geom_text()

The faciliation vignette is great and allows for easy gif generation of spatial animation.

maxt<-40 # this is gonna take a while
rates <- matrix(c(0,1,0,2,.7,4, 1,1,0,2,1.2,0, 1,0,6,2,2,0),nrow=3,byrow=T) # maximum stress effect is 4 for stage 1 and 0 for stages 2 and 3
results5 <- community(maxtime=maxt,numstages=3,parameters=rates,init=c(40,0,0),
                      interactionsD=matrix(c(-2,0,0, 0,0,0, 0,0,0),3)) # competition is only between seedlings
times <- seq(0,results$maxtime,length.out=20)         # array of times of interest
spatialanimation(results,times,interval=0.1,movie.name="sim.gif") 

fracprolif

fracprolif models of growth data with subset of population that enters quiescnece

fwsim

population simulation with fisher-wirght model with intermediate saving and implemented in rcpp.

https://arxiv.org/pdf/1210.1773.pdf

growcurves

library(growcurves) # depends on rcpp armadillo and formaula...
browseVignettes('growcurves') # none
data(datsim)
## attach(datsim)
## run dpgrow mixed effects model, returning object of class "dpgrow"
shape.dp    = 4
res     = dpgrow(y = datsim$y, subject = datsim$subject, 
            trt = datsim$trt, time = datsim$time,
            n.random = 3, n.fix_degree = 2, 
            n.iter = 10000, n.burn = 2000, 
            n.thin = 10, shape.dp = shape.dp, 
            option = "dp")
## Each plot is a "ggplot2" object saved in 
## a list to plot.results
plot.results    = plot(res) ## includes subject and 
##                    treatment growth curves
## Extract credible intervals (2.5%, mean, 97.5%).
## Includes fit statistics:  Dbar, DIC, pD, lpml.  
## Note: DIC is the DIC3 of Celeaux et. al. (2006) 
## for option = "dp".  Finally, the constructed fixed
## and random effects matrices, X and Z, are returned 
## with growth curve covariates appended 
## to user submitted nuisance covariates. 
summary.results = summary(res)
## View the summary results in the console
print(summary.results)
## Collect posterior sampled values over 
## the (n.iter - n.burn) retained iterations 
## for each sampled parameter.  
samples.posterior   = samples(res)
## model residuals (y - fit)
residuals       = resid(res) 
## Model with DP on clients effects, but 
## now INCLUDE session random effects
## in a multiple membership construction 
## communicated with the N x S matrix, W.subj.aff.
## Returns object, res.mm, of class "dpgrowmm".
shape.dp    = 4
strength.mm = 0.1
res.mm  = dpgrowmm(y = datsim$y, subject = datsim$subject, 
                     trt = datsim$trt, time = datsim$time, 
        n.random = 3, 
        Omega = datsim$Omega, group = datsim$group, 
        subj.aff = datsim$subj.aff,
        W.subj.aff = datsim$W.subj.aff, 
        n.iter = 10000, n.burn = 2000, n.thin = 10,
        strength.mm = strength.mm, 
        shape.dp = shape.dp, 
        option = "mmcar")
plot.results        = plot(res.mm)

growth

growthcurver

growthmodels

growthrate

growthrates

hamlet

tumor growth response patterns

microPop

solve diff equations to model microbial populations

OLScurve

ordinary least sequare growth cruve trajectories

other pkgs in the maybe category:

petitr
phenofit
PVAClone
quantregGrowth
sgmodel
sicegar
sitar
statmod # growth curve comparisons
testassay # illustrate tools by applying to growth inihibition assay
tumgr # tumor growth rate analysis

---

Vignettes are long form documentation commonly included in packages. Because they are part of the distribution of the package, they need to be as compact as possible. The html_vignette output type provides a custom style sheet (and tweaks some options) to ensure that the resulting html is as small as possible. The html_vignette format:

• Never uses retina figures¡¡
• Has a smaller default figure size
• Uses a custom CSS stylesheet instead of the default Twitter Bootstrap style

Vignette Info

Note the various macros within the vignette section of the metadata block above. These are required in order to instruct R how to build the vignette. Note that you should change the title field and the \VignetteIndexEntry to match the title of your vignette.

Styles

The html_vignette template includes a basic CSS theme. To override this theme you can specify your own CSS in the document metadata as follows:

output: 
  rmarkdown::html_vignette:
    css: mystyles.css

Figures

The figure sizes have been customised so that you can easily put two images side-by-side.

plot(1:10)
plot(10:1)

You can enable figure captions by fig_caption: yes in YAML:

output:
  rmarkdown::html_vignette:
    fig_caption: yes

Then you can use the chunk option fig.cap = "Your figure caption." in knitr.

More Examples

You can write math expressions, e.g. $Y = X\beta + \epsilon$, footnotes^[A footnote here.], and tables, e.g. using knitr::kable().

knitr::kable(head(mtcars, 10))

Also a quote using >:

"He who gives up [code] safety for [code] speed deserves neither." (via)

npjc/growr documentation built on Nov. 9, 2019, 7:29 a.m.