sla_simulated_compare_mle_eb
Zihao
2021-06-30
Last updated: 2021-06-30
Checks: 7 0
Knit directory: ebpmf_data_analysis/
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Untracked: output/fastTopics_fit/fit_droplet_simulated_0.20_fastTopics_k9.Rout
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Untracked: output/fastTopics_fit/fit_pbmc_68k_fastTopics_k10.Rds
Untracked: output/fastTopics_fit/fit_pbmc_68k_fastTopics_k10.Rout
Untracked: output/fastTopics_fit/fit_pbmc_68k_fastTopics_k11.Rds
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Untracked: output/fastTopics_fit/fit_pbmc_68k_fastTopics_k13.Rds
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Untracked: output/fastTopics_fit/fit_pbmc_68k_fastTopics_k19.Rds
Untracked: output/fastTopics_fit/fit_pbmc_68k_fastTopics_k19.Rout
Untracked: output/fastTopics_fit/fit_pbmc_68k_fastTopics_k2.Rds
Untracked: output/fastTopics_fit/fit_pbmc_68k_fastTopics_k2.Rout
Untracked: output/fastTopics_fit/fit_pbmc_68k_fastTopics_k20.Rds
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Untracked: output/fastTopics_fit/fit_pbmc_68k_fastTopics_k8.Rds
Untracked: output/fastTopics_fit/fit_pbmc_68k_fastTopics_k8.Rout
Untracked: output/fastTopics_fit/fit_pbmc_68k_fastTopics_k9.Rds
Untracked: output/fastTopics_fit/fit_pbmc_68k_fastTopics_k9.Rout
Untracked: output/fastTopics_fit/fit_pbmc_68k_simulated_fastTopics_k10.Rds
Untracked: output/fastTopics_fit/fit_pbmc_68k_simulated_fastTopics_k10.Rout
Untracked: output/fastTopics_fit/fit_pbmc_68k_simulated_fastTopics_k11.Rds
Untracked: output/fastTopics_fit/fit_pbmc_68k_simulated_fastTopics_k11.Rout
Untracked: output/fastTopics_fit/fit_pbmc_68k_simulated_fastTopics_k12.Rds
Untracked: output/fastTopics_fit/fit_pbmc_68k_simulated_fastTopics_k12.Rout
Untracked: output/fastTopics_fit/fit_pbmc_68k_simulated_fastTopics_k13.Rds
Untracked: output/fastTopics_fit/fit_pbmc_68k_simulated_fastTopics_k13.Rout
Untracked: output/fastTopics_fit/fit_pbmc_68k_simulated_fastTopics_k14.Rds
Untracked: output/fastTopics_fit/fit_pbmc_68k_simulated_fastTopics_k14.Rout
Untracked: output/fastTopics_fit/fit_pbmc_68k_simulated_fastTopics_k15.Rds
Untracked: output/fastTopics_fit/fit_pbmc_68k_simulated_fastTopics_k15.Rout
Untracked: output/fastTopics_fit/fit_pbmc_68k_simulated_fastTopics_k16.Rds
Untracked: output/fastTopics_fit/fit_pbmc_68k_simulated_fastTopics_k16.Rout
Untracked: output/fastTopics_fit/fit_pbmc_68k_simulated_fastTopics_k17.Rds
Untracked: output/fastTopics_fit/fit_pbmc_68k_simulated_fastTopics_k17.Rout
Untracked: output/fastTopics_fit/fit_pbmc_68k_simulated_fastTopics_k18.Rds
Untracked: output/fastTopics_fit/fit_pbmc_68k_simulated_fastTopics_k18.Rout
Untracked: output/fastTopics_fit/fit_pbmc_68k_simulated_fastTopics_k19.Rds
Untracked: output/fastTopics_fit/fit_pbmc_68k_simulated_fastTopics_k19.Rout
Untracked: output/fastTopics_fit/fit_pbmc_68k_simulated_fastTopics_k2.Rds
Untracked: output/fastTopics_fit/fit_pbmc_68k_simulated_fastTopics_k2.Rout
Untracked: output/fastTopics_fit/fit_pbmc_68k_simulated_fastTopics_k20.Rds
Untracked: output/fastTopics_fit/fit_pbmc_68k_simulated_fastTopics_k20.Rout
Untracked: output/fastTopics_fit/fit_pbmc_68k_simulated_fastTopics_k3.Rds
Untracked: output/fastTopics_fit/fit_pbmc_68k_simulated_fastTopics_k3.Rout
Untracked: output/fastTopics_fit/fit_pbmc_68k_simulated_fastTopics_k4.Rds
Untracked: output/fastTopics_fit/fit_pbmc_68k_simulated_fastTopics_k4.Rout
Untracked: output/fastTopics_fit/fit_pbmc_68k_simulated_fastTopics_k5.Rds
Untracked: output/fastTopics_fit/fit_pbmc_68k_simulated_fastTopics_k5.Rout
Untracked: output/fastTopics_fit/fit_pbmc_68k_simulated_fastTopics_k6.Rds
Untracked: output/fastTopics_fit/fit_pbmc_68k_simulated_fastTopics_k6.Rout
Untracked: output/fastTopics_fit/fit_pbmc_68k_simulated_fastTopics_k7.Rds
Untracked: output/fastTopics_fit/fit_pbmc_68k_simulated_fastTopics_k7.Rout
Untracked: output/fastTopics_fit/fit_pbmc_68k_simulated_fastTopics_k8.Rds
Untracked: output/fastTopics_fit/fit_pbmc_68k_simulated_fastTopics_k8.Rout
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Untracked: output/fastTopics_fit/fit_pbmc_68k_simulated_fastTopics_k9.Rout
Untracked: output/fastTopics_fit/fit_sla_fastTopics_k10.Rds
Untracked: output/fastTopics_fit/fit_sla_fastTopics_k10.Rout
Untracked: output/fastTopics_fit/fit_sla_fastTopics_k11.Rds
Untracked: output/fastTopics_fit/fit_sla_fastTopics_k11.Rout
Untracked: output/fastTopics_fit/fit_sla_fastTopics_k12.Rds
Untracked: output/fastTopics_fit/fit_sla_fastTopics_k12.Rout
Untracked: output/fastTopics_fit/fit_sla_fastTopics_k13.Rds
Untracked: output/fastTopics_fit/fit_sla_fastTopics_k13.Rout
Untracked: output/fastTopics_fit/fit_sla_fastTopics_k14.Rds
Untracked: output/fastTopics_fit/fit_sla_fastTopics_k14.Rout
Untracked: output/fastTopics_fit/fit_sla_fastTopics_k15.Rds
Untracked: output/fastTopics_fit/fit_sla_fastTopics_k15.Rout
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Untracked: output/fastTopics_fit/fit_sla_fastTopics_k16.Rout
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Untracked: output/fastTopics_fit/fit_sla_fastTopics_k17.Rout
Untracked: output/fastTopics_fit/fit_sla_fastTopics_k18.Rds
Untracked: output/fastTopics_fit/fit_sla_fastTopics_k18.Rout
Untracked: output/fastTopics_fit/fit_sla_fastTopics_k19.Rds
Untracked: output/fastTopics_fit/fit_sla_fastTopics_k19.Rout
Untracked: output/fastTopics_fit/fit_sla_fastTopics_k2.Rds
Untracked: output/fastTopics_fit/fit_sla_fastTopics_k2.Rout
Untracked: output/fastTopics_fit/fit_sla_fastTopics_k20.Rds
Untracked: output/fastTopics_fit/fit_sla_fastTopics_k20.Rout
Untracked: output/fastTopics_fit/fit_sla_fastTopics_k3.Rds
Untracked: output/fastTopics_fit/fit_sla_fastTopics_k3.Rout
Untracked: output/fastTopics_fit/fit_sla_fastTopics_k4.Rds
Untracked: output/fastTopics_fit/fit_sla_fastTopics_k4.Rout
Untracked: output/fastTopics_fit/fit_sla_fastTopics_k5.Rds
Untracked: output/fastTopics_fit/fit_sla_fastTopics_k5.Rout
Untracked: output/fastTopics_fit/fit_sla_fastTopics_k6.Rds
Untracked: output/fastTopics_fit/fit_sla_fastTopics_k6.Rout
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Untracked: output/fastTopics_fit/fit_sla_fastTopics_k8.Rds
Untracked: output/fastTopics_fit/fit_sla_fastTopics_k8.Rout
Untracked: output/fastTopics_fit/fit_sla_fastTopics_k9.Rds
Untracked: output/fastTopics_fit/fit_sla_fastTopics_k9.Rout
Untracked: output/fastTopics_fit/fit_sla_sim_fastTopics_evals.Rds
Untracked: output/fastTopics_fit/fit_sla_sim_fastTopics_k10.Rds
Untracked: output/fastTopics_fit/fit_sla_sim_fastTopics_k10.Rout
Untracked: output/fastTopics_fit/fit_sla_sim_fastTopics_k11.Rds
Untracked: output/fastTopics_fit/fit_sla_sim_fastTopics_k11.Rout
Untracked: output/fastTopics_fit/fit_sla_sim_fastTopics_k12.Rds
Untracked: output/fastTopics_fit/fit_sla_sim_fastTopics_k12.Rout
Untracked: output/fastTopics_fit/fit_sla_sim_fastTopics_k13.Rds
Untracked: output/fastTopics_fit/fit_sla_sim_fastTopics_k13.Rout
Untracked: output/fastTopics_fit/fit_sla_sim_fastTopics_k13_comparison.pdf
Untracked: output/fastTopics_fit/fit_sla_sim_fastTopics_k14.Rds
Untracked: output/fastTopics_fit/fit_sla_sim_fastTopics_k14.Rout
Untracked: output/fastTopics_fit/fit_sla_sim_fastTopics_k15.Rds
Untracked: output/fastTopics_fit/fit_sla_sim_fastTopics_k15.Rout
Untracked: output/fastTopics_fit/fit_sla_sim_fastTopics_k16.Rds
Untracked: output/fastTopics_fit/fit_sla_sim_fastTopics_k16.Rout
Untracked: output/fastTopics_fit/fit_sla_sim_fastTopics_k17.Rds
Untracked: output/fastTopics_fit/fit_sla_sim_fastTopics_k17.Rout
Untracked: output/fastTopics_fit/fit_sla_sim_fastTopics_k18.Rds
Untracked: output/fastTopics_fit/fit_sla_sim_fastTopics_k18.Rout
Untracked: output/fastTopics_fit/fit_sla_sim_fastTopics_k19.Rds
Untracked: output/fastTopics_fit/fit_sla_sim_fastTopics_k19.Rout
Untracked: output/fastTopics_fit/fit_sla_sim_fastTopics_k2.Rds
Untracked: output/fastTopics_fit/fit_sla_sim_fastTopics_k2.Rout
Untracked: output/fastTopics_fit/fit_sla_sim_fastTopics_k20.Rds
Untracked: output/fastTopics_fit/fit_sla_sim_fastTopics_k20.Rout
Untracked: output/fastTopics_fit/fit_sla_sim_fastTopics_k3.Rds
Untracked: output/fastTopics_fit/fit_sla_sim_fastTopics_k3.Rout
Untracked: output/fastTopics_fit/fit_sla_sim_fastTopics_k4.Rds
Untracked: output/fastTopics_fit/fit_sla_sim_fastTopics_k4.Rout
Untracked: output/fastTopics_fit/fit_sla_sim_fastTopics_k5.Rds
Untracked: output/fastTopics_fit/fit_sla_sim_fastTopics_k5.Rout
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These are the previous versions of the repository in which changes were made to the R Markdown (analysis/sla_simulated_compare_mle_eb.Rmd) and HTML (docs/sla_simulated_compare_mle_eb.html) files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view the files as they were in that past version.
| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 983a327 | Zihao | 2021-06-30 | sla_simulated_compare_mle_eb.Rmd |
Introduction
- I thin sla dataset by half to get training and test dataset (see prepare_sla_sim). I first fit MLE (
fastTopics) on original dataset, and call it truth . (Probably not a good name but it’s convenient…). Then I fit MLE and EB on thinned dataset. A good model should have good likelihood on test data, and should be close totruth. - I initialize MLE (
fastTopics) and EB (ebpmf-wbg) methods using the truth. - EB approach is much better than MLE in the two cases shown below: \(K = 6\) & \(K = 20\).
- Note that the mean matrix is slightly different bewteen the original data and train/test, as I remove some columns with 0 counts on training.
rm(list = ls())
source("code/misc.R")
library(fastTopics)
library(ebpmf.alpha)
datafile = "data/sim/sla_simulated.Rds"
data = readRDS(datafile)
data$Y_train = as(data$Y_train, "sparseMatrix")
data$Y_test = as(as.matrix(data$Y_test), "sparseMatrix") ## the way i generate simulated data is a bit stupid
dim(data$Y_train)[1] 3207 8030dim(data$Y_test)[1] 3207 8030sum(data$Y_train != 0)/(nrow(data$Y_train) * ncol(data$Y_train))[1] 0.004549318sum(data$Y_test != 0)/(nrow(data$Y_test) * ncol(data$Y_test))[1] 0.004498682\(K = 6\)
k = 6
truth.file = sprintf("output/fastTopics_fit/fit_sla_fastTopics_k%d.Rds", k)
fitted.mle.file = sprintf("output/fastTopics_fit/fit_sla_simualted_fastTopics_init_truth_k%d.Rds", k)
fitted.eb.file = sprintf("output/fastTopics_fit/fit_sla_simualted_ebpmf_wbg_init_truth_k%d.Rds", k)
truth = readRDS(truth.file)
fitted.mle = readRDS(fitted.mle.file)
fitted.eb = readRDS(fitted.eb.file)
## get "truth"
truth = fastTopics::multinom2poisson(truth)
truth = list(L = truth$L, F = truth$F[data$w_idx,])
class(truth) <- c("poisson_nmf_fit","list")
truth = fastTopics::poisson2multinom(truth) ## s is not right, but this does not affect computation of log-likelihood
fitted.mle.m = fastTopics::poisson2multinom(fitted.mle)
fitted.eb.p = ebpmf2fastTopics_pmf(fitted.eb)
fitted.eb.m = fastTopics::poisson2multinom(fitted.eb.p)
idx.mle = match_topics(truth$F, fitted.mle.m$F)
idx.eb = match_topics(truth$F, fitted.eb.m$F)
## see if there is 1-1 correspondence bewteen topics
idx.mle[1] 1 2 3 4 5 6idx.eb[1] 1 2 3 4 5 6compare log-lik on training/test
loglik.train.truth = fastTopics::loglik_multinom_topic_model(X = data$Y_train, truth)
loglik.train.mle = fastTopics::loglik_multinom_topic_model(X = data$Y_train, fitted.mle.m)
loglik.train.eb = fastTopics::loglik_multinom_topic_model(X = data$Y_train, fitted.eb.m)
loglik.test.truth = fastTopics::loglik_multinom_topic_model(X = data$Y_test, truth)
loglik.test.mle = fastTopics::loglik_multinom_topic_model(X = data$Y_test, fitted.mle.m)
loglik.test.eb = fastTopics::loglik_multinom_topic_model(X = data$Y_test, fitted.eb.m)
## training error
### truth mle eb
c(mean(loglik.train.truth), mean(loglik.train.mle), mean(loglik.train.eb))[1] -187.0736 -184.3535 -188.8222## test error
### truth mle eb
c(mean(loglik.test.truth), mean(loglik.test.mle), mean(loglik.test.eb))[1] -183.5079 -204.8046 -191.4603MLE vs truth
model = fitted.mle.m
idx = match_topics(truth$F, model$F)
l_mse <- c()
f_mse <- c()
par(mfrow = c(k, 2))
for(i in 1:k){
j = idx[i]
plot(truth$L[,i], model$L[,j], xlab = "loading (truth)", ylab = "loading (fitted)", main = sprintf("topic %d", i))
abline(a = 0, b = 1, col = "blue")
plot(truth$F[,i], model$F[,j], xlab = "factor (truth)", ylab = "factor (fitted)", main = sprintf("topic %d", i))
abline(a = 0, b = 1, col = "blue")
l_mse = c(l_mse, mean((truth$L[,i] - model$L[,j])^2))
f_mse = c(l_mse, mean((truth$F[,i] - model$F[,j])^2))
}
l_mse.mle = l_mse
f_mse.mle = f_mseEB vs truth
model = fitted.eb.m
idx = match_topics(truth$F, model$F)
l_mse <- c()
f_mse <- c()
par(mfrow = c(k, 2))
for(i in 1:k){
j = idx[i]
plot(truth$L[,i], model$L[,j], xlab = "loading (truth)", ylab = "loading (fitted)", main = sprintf("topic %d", i))
abline(a = 0, b = 1, col = "blue")
plot(truth$F[,i], model$F[,j], xlab = "factor (truth)", ylab = "factor (fitted)", main = sprintf("topic %d", i))
abline(a = 0, b = 1, col = "blue")
l_mse = c(l_mse, mean((truth$L[,i] - model$L[,j])^2))
f_mse = c(l_mse, mean((truth$F[,i] - model$F[,j])^2))
}
l_mse.eb = l_mse
f_mse.eb = f_msecompare RMSE from truth
round(rbind(l_mse.mle, l_mse.eb), digits = 4) [,1] [,2] [,3] [,4] [,5] [,6]
l_mse.mle 0.0174 0.0105 0.0161 0.0144 0.0156 0.0100
l_mse.eb 0.0096 0.0067 0.0120 0.0080 0.0062 0.0035round(rbind(f_mse.mle, f_mse.eb), digits = 4) [,1] [,2] [,3] [,4] [,5] [,6] [,7]
f_mse.mle 0.0174 0.0105 0.0161 0.0144 0.0156 0.0100 0
f_mse.eb 0.0096 0.0067 0.0120 0.0080 0.0062 0.0035 0\(K = 20\)
k = 20
truth.file = sprintf("output/fastTopics_fit/fit_sla_fastTopics_k%d.Rds", k)
fitted.mle.file = sprintf("output/fastTopics_fit/fit_sla_simualted_fastTopics_init_truth_k%d.Rds", k)
fitted.eb.file = sprintf("output/fastTopics_fit/fit_sla_simualted_ebpmf_wbg_init_truth_k%d.Rds", k)
truth = readRDS(truth.file)
fitted.mle = readRDS(fitted.mle.file)
fitted.eb = readRDS(fitted.eb.file)
## get "truth"
truth = fastTopics::multinom2poisson(truth)
truth = list(L = truth$L, F = truth$F[data$w_idx,])
class(truth) <- c("poisson_nmf_fit","list")
truth = fastTopics::poisson2multinom(truth) ## s is not right, but this does not affect computation of log-likelihood
fitted.mle.m = fastTopics::poisson2multinom(fitted.mle)
fitted.eb.p = ebpmf2fastTopics_pmf(fitted.eb)
fitted.eb.m = fastTopics::poisson2multinom(fitted.eb.p)
idx.mle = match_topics(truth$F, fitted.mle.m$F)
idx.eb = match_topics(truth$F, fitted.eb.m$F)
## see if there is 1-1 correspondence bewteen topics
idx.mle [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20idx.eb [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20compare log-lik on training/test
loglik.train.truth = fastTopics::loglik_multinom_topic_model(X = data$Y_train, truth)
loglik.train.mle = fastTopics::loglik_multinom_topic_model(X = data$Y_train, fitted.mle.m)
loglik.train.eb = fastTopics::loglik_multinom_topic_model(X = data$Y_train, fitted.eb.m)
loglik.test.truth = fastTopics::loglik_multinom_topic_model(X = data$Y_test, truth)
loglik.test.mle = fastTopics::loglik_multinom_topic_model(X = data$Y_test, fitted.mle.m)
loglik.test.eb = fastTopics::loglik_multinom_topic_model(X = data$Y_test, fitted.eb.m)
## training error
### truth mle eb
c(mean(loglik.train.truth), mean(loglik.train.mle), mean(loglik.train.eb))[1] -172.6338 -167.1411 -174.7843## test error
### truth mle eb
c(mean(loglik.test.truth), mean(loglik.test.mle), mean(loglik.test.eb))[1] -169.4004 -218.8784 -188.2133MLE vs truth
model = fitted.mle.m
idx = match_topics(truth$F, model$F)
l_mse <- c()
f_mse <- c()
par(mfrow = c(k, 2))
for(i in 1:k){
j = idx[i]
plot(truth$L[,i], model$L[,j], xlab = "loading (truth)", ylab = "loading (fitted)", main = sprintf("topic %d", i))
abline(a = 0, b = 1, col = "blue")
plot(truth$F[,i], model$F[,j], xlab = "factor (truth)", ylab = "factor (fitted)", main = sprintf("topic %d", i))
abline(a = 0, b = 1, col = "blue")
l_mse = c(l_mse, mean((truth$L[,i] - model$L[,j])^2))
f_mse = c(l_mse, mean((truth$F[,i] - model$F[,j])^2))
}
l_mse.mle = l_mse
f_mse.mle = f_mseEB vs truth
model = fitted.eb.m
idx = match_topics(truth$F, model$F)
l_mse <- c()
f_mse <- c()
par(mfrow = c(k, 2))
for(i in 1:k){
j = idx[i]
plot(truth$L[,i], model$L[,j], xlab = "loading (truth)", ylab = "loading (fitted)", main = sprintf("topic %d", i))
abline(a = 0, b = 1, col = "blue")
plot(truth$F[,i], model$F[,j], xlab = "factor (truth)", ylab = "factor (fitted)", main = sprintf("topic %d", i))
abline(a = 0, b = 1, col = "blue")
l_mse = c(l_mse, mean((truth$L[,i] - model$L[,j])^2))
f_mse = c(l_mse, mean((truth$F[,i] - model$F[,j])^2))
}
l_mse.eb = l_mse
f_mse.eb = f_msecompare RMSE from truth
round(rbind(l_mse.mle, l_mse.eb), digits = 4) [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9]
l_mse.mle 0.0050 0.0074 0.0052 0.0060 0.0049 0.0037 0.0034 0.0043 0.0056
l_mse.eb 0.0019 0.0019 0.0019 0.0025 0.0019 0.0010 0.0021 0.0012 0.0018
[,10] [,11] [,12] [,13] [,14] [,15] [,16] [,17] [,18]
l_mse.mle 0.0061 0.0077 0.0029 0.0065 0.0040 0.0098 0.0065 0.0040 0.0043
l_mse.eb 0.0028 0.0045 0.0011 0.0027 0.0018 0.0039 0.0059 0.0018 0.0024
[,19] [,20]
l_mse.mle 0.0049 0.0056
l_mse.eb 0.0024 0.0028round(rbind(f_mse.mle, f_mse.eb), digits = 4) [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9]
f_mse.mle 0.0050 0.0074 0.0052 0.0060 0.0049 0.0037 0.0034 0.0043 0.0056
f_mse.eb 0.0019 0.0019 0.0019 0.0025 0.0019 0.0010 0.0021 0.0012 0.0018
[,10] [,11] [,12] [,13] [,14] [,15] [,16] [,17] [,18]
f_mse.mle 0.0061 0.0077 0.0029 0.0065 0.0040 0.0098 0.0065 0.0040 0.0043
f_mse.eb 0.0028 0.0045 0.0011 0.0027 0.0018 0.0039 0.0059 0.0018 0.0024
[,19] [,20] [,21]
f_mse.mle 0.0049 0.0056 0
f_mse.eb 0.0024 0.0028 0
sessionInfo()R version 3.6.1 (2019-07-05)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Scientific Linux 7.4 (Nitrogen)
Matrix products: default
BLAS/LAPACK: /software/openblas-0.2.19-el7-x86_64/lib/libopenblas_haswellp-r0.2.19.so
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] ebpmf.alpha_0.4.5 fastTopics_0.5-53 pheatmap_1.0.12 workflowr_1.6.2
loaded via a namespace (and not attached):
[1] httr_1.4.1 tidyr_1.1.0 jsonlite_1.6
[4] viridisLite_0.3.0 gtools_3.8.1 RcppParallel_4.4.3
[7] assertthat_0.2.1 mixsqp_0.3-43 yaml_2.2.0
[10] progress_1.2.2 ggrepel_0.9.1 pillar_1.4.2
[13] backports_1.1.4 lattice_0.20-38 quantreg_5.41
[16] glue_1.3.1 quadprog_1.5-7 digest_0.6.20
[19] RColorBrewer_1.1-2 promises_1.0.1 colorspace_1.4-1
[22] cowplot_1.0.0 htmltools_0.3.6 httpuv_1.5.1
[25] Matrix_1.2-18 pkgconfig_2.0.2 invgamma_1.1
[28] SparseM_1.77 purrr_0.3.4 scales_1.1.0
[31] whisker_0.3-2 later_0.8.0 Rtsne_0.15
[34] MatrixModels_0.4-1 git2r_0.26.1 tibble_2.1.3
[37] ggplot2_3.3.3 ashr_2.2-47 lazyeval_0.2.2
[40] magrittr_1.5 crayon_1.3.4 mcmc_0.9-6
[43] evaluate_0.14 ebpm_0.0.1.1 fs_1.3.1
[46] MASS_7.3-51.4 truncnorm_1.0-8 tools_3.6.1
[49] data.table_1.13.2 prettyunits_1.0.2 hms_0.5.3
[52] lifecycle_0.1.0 stringr_1.4.0 MCMCpack_1.4-5
[55] plotly_4.9.0 munsell_0.5.0 irlba_2.3.3
[58] compiler_3.6.1 rlang_0.4.10 grid_3.6.1
[61] htmlwidgets_1.3 rmarkdown_1.13 gtable_0.3.0
[64] R6_2.4.0 knitr_1.23 dplyr_0.8.3
[67] uwot_0.1.5 rprojroot_1.3-2 stringi_1.4.3
[70] SQUAREM_2017.10-1 Rcpp_1.0.5 vctrs_0.3.1
[73] tidyselect_1.1.0 xfun_0.8 coda_0.19-3