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Python and R again in same environment. R is now 4.4.0. Installed and updated packages.

- After separating R and python was not successfull, I went back to having python and R in the same conda environment.
- Python and python packages are installed via conda if possible else pip.
- R is not installed via conda (unreliable) but compiled within the conda environment. System dependencies are managed via conda however.
- R packages are installed via the R package pak. System dependencies are managed again via conda.
- R is now 4.4.0. All R packages were updated accordingly.
- RStudio server and quarto were updated to the most recent version.
- For spatial analysis, additional packages were installed (R: Voyager, VoltRon, Banksy, ..-; python: spatialdata, sopa, squidpy, ...).

- update rstudio-server and quarto
- added cellxgene-census and singler

added cellxgene

Fixed python installations. Added R package loupeR.

- Updated RStudio and quarto
- Rewrote RStudio/conda integration
- Added authentication method for RStudio
- Updated to official Seurat v5 version
- Added R packages scuttle, SoupX, scDblFinder, sampleQC, harmony, lintr

Added R packages gt and gtsummary.

Added R packages styler and tictoc. Updated rstudio-server.

- Updated R to version 4.3
- Updated all R CRAN, Bioconductor and github packages to their most recent versions
- Switched to Seurat 5 for support of large datasets (using on-disk BPcells) and multimodal analyses (incl signac for scATAC-seq)
- Updated quarto
- Updated RStudio
- Added bedtools and samtools

- reproducibility of PCA is now tested
- cellrank v2 update
- several issues fixed

built gdal library from source to fix compilation problems of depending r packages

added sccode, pydesq2, and pypath-omipath

added scATAC packages Signac and SCENIC+
+some more fixes and tests

- fixed cellrank

update R and python packages

added liana for cell-cell communication