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update vigngette
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NEWS.md

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<div style="text-align: left;">
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## GPTCM 1.1.3 (2025-10-01) (GitHub Only)
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### GPTCM 1.1.3 (2025-10-31)
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* TODO: update results in vignette
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* Remove redundant variables `logPosteriorZeta` and `logPosteriorBeta` in cpp files
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* Remove partial likelihood `loglikelihood0()`, and always use joint likelihood
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* Remove useless func `arms_simple()`
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* Pass more references and using more `const` keyword in functions
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* Update `simData()` by changing xi0 to simulate survival data with censoring rate 0.2
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* Remove `arms_simple`
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* Use `openmp` to parallelize some for-loop
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* Update vignette
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* TODO: decide whether keep `kappaIGamma=TRUE` or not
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* TODO: remove redundant arguments "tauSq" and "wSq" after bringing them out of ARMS files
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* TODO: bug for posterior pi_{jl}
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## GPTCM 1.1.2 (2025-09-26)
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### GPTCM 1.1.2 (2025-09-26)
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* Update simulation examples in vignette due to the change of `mvnfast::rmvn()`
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* Convert line endings in `configure.ac` to LF
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* Improve help documentation
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* Update function `simData()` for any number of clusters
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* Simplify `BVS.cpp`
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## GPTCM 1.1.1 (2025-09-16)
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### GPTCM 1.1.1 (2025-09-16)
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* Fix a warning about fallback compilation with Armadillo 14.6.3 from CRAN check on machine `r-devel-linux-x86_64-debian-gcc`
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* Fix issues raised from CRAN reviewer
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* Change `MASS::mvrnorm()` to `mvnfast::rmvn()`
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## GPTCM 1.1.0 (2025-09-07) (GitHub Only)
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### GPTCM 1.1.0 (2025-09-07) (GitHub Only)
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* First CRAN version
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* Add vignette
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## GPTCM 1.0.3 (2025-09-05) (GitHub Only)
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### GPTCM 1.0.3 (2025-09-05) (GitHub Only)
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* Fix NOTE about `elapsed time` from Winbuilder checks
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## GPTCM 1.0.2 (2025-09-05) (GitHub Only)
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### GPTCM 1.0.2 (2025-09-05) (GitHub Only)
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* Improve help files
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## GPTCM 1.0.1 (2025-08-28) (GitHub Only)
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### GPTCM 1.0.1 (2025-08-28) (GitHub Only)
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* In `simData()`, suppress intercept when simulating data based on the Cox model
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## GPTCM 1.0.0 (2025-08-25) (GitHub Only)
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### GPTCM 1.0.0 (2025-08-25) (GitHub Only)
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* First released version at `ocbe-uio` github
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README.md

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p <- 10 # variable selection predictors
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L <- 3 # cell types
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library(GPTCM)
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set.seed(123)
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set.seed(1234)
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dat <- simData(n, p, L)
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# KM curve

man/figures/cran_betas.png

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man/figures/cran_brier.png

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man/figures/cran_etas.png

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man/figures/cran_gammas.png

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man/figures/cran_km.png

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man/figures/cran_zetas.png

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vignettes/GPTCM.Rmd

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rm(list = ls())
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# simulate data
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set.seed(123)
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n <- 200 # subjects
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p <- 10 # variable selection predictors
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L <- 3 # cell types
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library(GPTCM)
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set.seed(123)
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set.seed(1234)
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dat <- simData(n, p, L)
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# KM curve

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