bbolker

library(brms)
library(metafor)
library(broom)
library(broom.mixed)

set.seed(101)
y <- rnorm(10)
sei <- rgamma(10, shape = 1)

## metafor: fixed-effect meta-analysis

bbolker

i1 <- installed.packages()
null <- matrix(nrow=0, ncol = 2,
               dimnames=list(NULL, c("Item", "type")))
get_data <- function(pkg) {
    cat(pkg,"\n")
    dd <- data(package=pkg)$results
    ## library(pkg, character.only = TRUE)
    ## on.exit(try(detach(paste0("package:", pkg))))
    if (nrow(dd) == 0) return(NULL)
    FUN <- function(x) {

bbolker

## original by Richard McElreath at https://twitter.com/rlmcelreath/status/1701165075493470644, 
##   https://gist.github.com/rmcelreath/39dd410fc6bb758e54d79249b11eeb2f
## originally based on https://doi.org/10.1006/jmva.1999.1820
## with improvements from https://gist.github.com/murphyk/94205bcf335837108ff0e6d51331785a

post_prior <- function(
                       m_pr = 10, m_lik = 0,        ## mean values for prior and likelihood
                       sd_pr = 1, sd_lik = 1,       ## standard deviations
                       df_pr = 1000, df_lik = 1000, ## dfs (df = 1000 is effectively Gaussian)
                       xlim = c(-5, 15), n = 1001,   ## range and delta for x-vector

bbolker

## adapted from Richard McElreath's code at
## https://gist.github.com/rmcelreath/4f7010e8d5688c69bbeb7008f0aabe65

binom_p <- function(N=10, M=5, p=0.25, b=0,
                    test = c("Wald", "LRT")) {
    test <- match.arg(test)
    x <- rep(0:1, each = N/2)
    p <- plogis(qlogis(p)+b*x)
    y <- rbinom(N, size=M, prob=p)
    z <- glm( cbind(y, M-y) ~ x , family=binomial )

bbolker

start_date <- "2023-06-21"
end_date <- "2023-07-01"
refdate  <- "2023-06-21"
season <- "summer"

library(ggplot2); theme_set(theme_bw())
library(colorspace)
library(purrr)
library(dplyr)
## need archived package for sunrise/sunset calcs

bbolker

cbrm <- function(formula,
                 data,
                 family = gaussian(),
                 prior = NULL,
                 autocor = NULL,
                 cov_ranef = NULL,
                 sample_prior = c("no", "yes", "only"),
                 sparse = FALSE,
                 knots = NULL,
                 stan_funs = NULL,

bbolker

library(coxme)
library(Matrix)

#' @param fit a fitted \code{coxme} model
#' @param data the data frame for which to evaluate conditional SDs
#' @param grpvar (character) grouping variable (should be able to
#' extract this from the model, but it's a nuisance)
#' @param rform random-effects term (ditto)
#' @param sd.only return only sqrt(diag(V))? (otherwise return full covariance matrix)
coxme.condsd <- function(fit,

bbolker

library(lme4)
library(nlme)
library(glmmTMB)
library(Matrix)

## what do various R packages do when confronted with an
## undefined REML problem (fixed effects *and* random effects
## for every subject?

## Twitter thread: https://twitter.com/ten_photos/status/1657399290166222850

bbolker

#https://dynamicecology.wordpress.com/2021/03/15/how-long-do-institutional-investigations-into-accusations-of-serious-scientific-misconduct-typically-take-heres-some-data/
## https://secretariat.mcmaster.ca/app/uploads/Research-Integrity-Policy.pdf
## https://www-nature-com.libaccess.lib.mcmaster.ca/articles/d41586-020-00287-y

library(tidyverse)
dd <- read.table(header=TRUE, text="
subject duration year
Fuji 24 2010
Fuji 3 2012
Boldt 21 2010

bbolker

library(lme4)
set.seed(101)
nn <- 100
ng <- 10
dd <- data.frame(x = rnorm(nn),
                 f = factor(rep(1:(nn/ng), ng)),
                 g = factor(rep(1:ng, each = ng)))
dd$y1 <- simulate( ~ x + (1|f),
                 newdata = dd,
                 newparams = list(beta = c(1,1),