“update”

.Rbuildignore

@@ -1,2 +1,3 @@
 ^GeneTrajectory\.Rproj$
 ^\.Rproj\.user$
+^README\.Rmd$

.gitignore

@@ -1 +1,2 @@
 .Rproj.user
+inst/doc

DESCRIPTION

@@ -30,3 +30,7 @@ Imports:
 	biclust,
 	stats,
 	Matrix (>= 1.5)
+Suggests: 
+    knitr,
+    rmarkdown
+VignetteBuilder: knitr

NAMESPACE

@@ -8,13 +8,11 @@ export(GetGenePseudoorder)
 export(GetGraphDistance)
 export(LoadGeneDistMat)
 export(RunDM)
-export(checkIgtPythonEnvironment)
 export(coarse.grain)
 export(diffusion.map)
 export(get.RW.matrix)
-export(setupIgtPythonEnvironment)
-export(simulate.bifurcation)
-export(simulate.coral)
-export(simulate.cyclic)
-export(simulate.cylinder)
-export(simulate.linear)
+export(simulate_bifurcation)
+export(simulate_coral)
+export(simulate_cyclic)
+export(simulate_cylinder)
+export(simulate_linear)

NEWS.md

@@ -0,0 +1,3 @@
+# GeneTrajectory 1.0.0
+
+* Initial CRAN submission.

R/simulate.bifurcation.R → R/simulate_bifurcation.R

@@ -15,12 +15,12 @@
 #' @return
 #' Returns a gene-by-cell count matrix
 #'
-#' @export simulate.bifurcation
+#' @export simulate_bifurcation
 #'
 #' @examples
 #'
 
-simulate.bifurcation <- function(N_cells = 5*c(100, 50, 50),
+simulate_bifurcation <- function(N_cells = 5*c(100, 50, 50),
                                  N_genes = 5*c(100, 50, 50),
                                  model = "poisson",
                                  meanlog = 0,

R/simulate.coral.R → R/simulate_coral.R

@@ -16,13 +16,13 @@
 #' @return
 #' Returns a gene-by-cell count matrix
 #'
-#' @export simulate.coral
+#' @export simulate_coral
 #'
 #' @examples
 #'
 
 
-simulate.coral <- function(N_cells = 15*rep(100, 7),
+simulate_coral <- function(N_cells = 15*rep(100, 7),
                            N_genes = 2*rep(100, 7),
                            N_genes_CC = 400,
                            model = "poisson",
@@ -124,7 +124,7 @@ simulate.coral <- function(N_cells = 15*rep(100, 7),
 
   gc_mat_tree <- gc_mat
 
-  gc_mat_cyclic <- simulate.cyclic(N_cells = sum(N_cells),
+  gc_mat_cyclic <- simulate_cyclic(N_cells = sum(N_cells),
                                    N_genes = N_genes_CC,
                                    model = model,
                                    meanlog = meanlog,

R/simulate.cyclic.R → R/simulate_cyclic.R

@@ -14,12 +14,12 @@
 #' @return
 #' Returns a gene-by-cell count matrix
 #'
-#' @export simulate.cyclic
+#' @export simulate_cyclic
 #'
 #' @examples
 #'
 
-simulate.cyclic <- function(N_cells = 1000,
+simulate_cyclic <- function(N_cells = 1000,
                             N_genes = 500,
                             model = "poisson",
                             meanlog = 0,

R/simulate.cylinder.R → R/simulate_cylinder.R

@@ -15,12 +15,12 @@
 #' @return
 #' Returns a gene-by-cell count matrix
 #'
-#' @export simulate.cylinder
+#' @export simulate_cylinder
 #'
 #' @examples
 #'
 
-simulate.cylinder <- function(N_cells = 5000,
+simulate_cylinder <- function(N_cells = 5000,
                               N_genes = rep(500,2),
                               model = "poisson",
                               meanlog = 0,
@@ -31,7 +31,7 @@ simulate.cylinder <- function(N_cells = 5000,
                               sort = TRUE,
                               sparsity = 0.1,
                               theta = 10){
-  gc_mat_linear <- simulate.linear(N_cells = N_cells,
+  gc_mat_linear <- simulate_linear(N_cells = N_cells,
                                    N_genes = N_genes[1],
                                    model = model,
                                    meanlog = meanlog,
@@ -42,7 +42,7 @@ simulate.cylinder <- function(N_cells = 5000,
                                    sort = sort,
                                    sparsity = sparsity,
                                    theta = theta)
-  gc_mat_cyclic <- simulate.cyclic(N_cells = N_cells,
+  gc_mat_cyclic <- simulate_cyclic(N_cells = N_cells,
                                      N_genes = N_genes[2],
                                      model = model,
                                      meanlog = meanlog,

R/simulate.linear.R → R/simulate_linear.R

@@ -15,13 +15,13 @@
 #' @return
 #' Returns a gene-by-cell count matrix
 #'
-#' @export simulate.linear
+#' @export simulate_linear
 #'
 #' @examples
 #'
 #'
 
-simulate.linear <- function(N_cells = 1000,
+simulate_linear <- function(N_cells = 1000,
                             N_genes = 200,
                             model = "poisson",
                             meanlog = 0,

README.Rmd

@@ -0,0 +1,54 @@
+---
+output: github_document
+---
+
+<!-- README.md is generated from README.Rmd. Please edit that file -->
+
+```{r, include = FALSE}
+knitr::opts_chunk$set(
+  collapse = TRUE,
+  comment = "#>",
+  fig.path = "man/figures/README-",
+  out.width = "100%"
+)
+```
+
+# GeneTrajectory
+
+<!-- badges: start -->
+<!-- badges: end -->
+
+The goal of GeneTrajectory is to ...
+
+## Installation
+
+You can install the development version of GeneTrajectory like so:
+
+``` r
+# FILL THIS IN! HOW CAN PEOPLE INSTALL YOUR DEV PACKAGE?
+```
+
+## Example
+
+This is a basic example which shows you how to solve a common problem:
+
+```{r example}
+library(GeneTrajectory)
+## basic example code
+```
+
+What is special about using `README.Rmd` instead of just `README.md`? You can include R chunks like so:
+
+```{r cars}
+summary(cars)
+```
+
+You'll still need to render `README.Rmd` regularly, to keep `README.md` up-to-date. `devtools::build_readme()` is handy for this.
+
+You can also embed plots, for example:
+
+```{r pressure, echo = FALSE}
+plot(pressure)
+```
+
+In that case, don't forget to commit and push the resulting figure files, so they display on GitHub and CRAN.

docs/pkgdown.yml

@@ -3,5 +3,5 @@ pkgdown: 2.0.7
 pkgdown_sha: ~
 articles:
   GeneTrajectory: GeneTrajectory.html
-last_built: 2024-04-04T08:48Z
+last_built: 2024-04-05T03:46Z