njtierney

pct_resupply <- tibble::tribble(
                                          ~Section, ~Days, ~`Distance.(mi)`, ~`Total.(mi)`,  ~Resupply,
                             "Campo to Mt. Laguna",    3L,             42.9,          42.9,        "B",
                    "Mt. Laguna to Warner Springs",    4L,             66.6,         109.5,        "B",
                     "Warner Springs to Idyllwild",    5L,             69.9,         179.4,        "B",
                      "Idyllwild to Big Bear City",    6L,             95.6,           275,        "B",
                     "Big Bear City to Wrightwood",    6L,             94.5,         369.5,        "B",
                        "Wrightwood to Agua Dulce",    6L,               85,         454.5,        "B",
               "Agua Dulce to Tehachapi or Mojave",    6L,              112,         566.5,        "B",
                    "Tehachapi to Kennedy Meadows",    8L,            135.5,         702.2,        "M",

njtierney

clean_site_name_forbes <- function(site_name, out_name){
  dplyr::case_when(
    str_detect(site_name, "ST") ~ "STP Forbes",
    str_detect(site_name, "Muddy") ~ "STP Forbes",
    str_detect("ST", site_name) ~ "STP Forbes",
    .default = "unmatched"
  )
}

njtierney

n.pixel <- 1000
n.other.spec <- 20
spec.names <- letters[1:(n.other.spec+1)]

## Geographic covariates affecting species abundance
x <- matrix(rnorm(2*n.pixel),nrow=n.pixel)

## Geographic covariate causing selection bias (correlated with x1)
z <- scale(x[,1] + rnorm(n.pixel)*sqrt(.95^(-2)-1))

njtierney

library(polite)
library(tidyverse)
library(httr2)
library(rvest)

url <- "https://njt.micro.blog/2023/08/19/pct-day-kennedy.html"

extract_pct_summary <- function(url){
  raw <- bow(url) %>% scrape()
  raw %>% 

njtierney

library(tidyverse)

# 4 data sets
# survey
n <- 100
create_survey <- function(n, year, id = 1:n){
  tibble(
  id = id,
  year = year,

njtierney

library(tidyverse)

# 4 data sets
# survey
n <- 100
create_survey <- function(n, year, id = 1:n){
  tibble(
  id = id,
  year = year,
  province = sample(1:9, size = n, replace = TRUE),

njtierney

library(tidyverse)
cause_for_dismissal <- c("A",
                         "B",
                         "C")

vic_moz_long <- tibble(
  id = 1:5,
  species = c("B", "C", "D", "E", "F")
)

njtierney

# comparison of Prem vs conmat for germany:

library(deSolve)
library(tidyverse)
library(conmat)
world_data <- socialmixr::wpp_age() %>%
  mutate(
    new_lower_age = if_else(lower.age.limit >= 75, 75L, lower.age.limit)
  ) %>%

njtierney

library(distributional)
library(tidyverse)

dat <- tibble(
  id = 1:10,
  mean = c(1:10),
  sd = c(1:10)
) %>% 
  mutate(dist = dist_normal(

njtierney

# ABS - what is statistically special about these numbers?
set_one <- c(5, 2, 7, 3, 5, 1)
set_two <- c(9, 6, 3, 5, 8, 6)

summary(set_one)
#>    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
#>   1.000   2.250   4.000   3.833   5.000   7.000
summary(set_two)
#>    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.