This package provides an Arduino grammar for the Tree-sitter library.
This package provides a Verilog grammar for the Tree-sitter library.
This package provides a Fortran grammar for the Tree-sitter library.
This package provides a Clojure grammar for the Tree-sitter library.
This package provides a TLA+ grammar for the Tree-sitter library.
This package provides a Kconfig grammar for the Tree-sitter library.
This package provides a Haskell grammar for the Tree-sitter library.
This package provides a JSONNET grammar for the Tree-sitter library.
This package provides a Clarity grammar for the Tree-sitter library.
This package provides a Chatito grammar for the Tree-sitter library.
This package provides a NetLinx grammar for the Tree-sitter library.
This package provides a Doxygen grammar for the Tree-sitter library.
This package provides a module for avoiding global state in Elixir applications.
This package provides a C# grammar for the Tree-sitter library.
cl-draw-cons-tree draws a cons tree in ASCII-art style.
This package provides a comment tags (like TODO, FIXME) grammar for the Tree-sitter library.
libagent is a library that allows using TREZOR, Keepkey and Ledger Nano as a hardware SSH/GPG agent.
Computes treatment patterns within a given cohort using the Observational Medical Outcomes Partnership (OMOP) common data model (CDM). As described in Markus, Verhamme, Kors, and Rijnbeek (2022) <doi:10.1016/j.cmpb.2022.107081>.
Testing for trajectory presence and heterogeneity on multivariate data. Two statistical methods (Tenha & Song 2022) <doi:10.1371/journal.pcbi.1009829> are implemented. The tree dimension test quantifies the statistical evidence for trajectory presence. The subset specificity measure summarizes pattern heterogeneity using the minimum subtree cover. There is no user tunable parameters for either method. Examples are included to illustrate how to use the methods on single-cell data for studying gene and pathway expression dynamics and pathway expression specificity.
This package provides a classification (decision) tree is constructed from survival data with high-dimensional covariates. The method is a robust version of the logrank tree, where the variance is stabilized. The main function "uni.tree" returns a classification tree for a given survival dataset. The inner nodes (splitting criterion) are selected by minimizing the P-value of the two-sample the score tests. The decision of declaring terminal nodes (stopping criterion) is the P-value threshold given by an argument (specified by user). This tree construction algorithm is proposed by Emura et al. (2021, in review).
Documentation at https://melpa.org/#/treemacs-magit
Documentation at https://melpa.org/#/treemacs-persp
Documentation at https://melpa.org/#/treesit-ispell
Documentation at https://melpa.org/#/evil-tree-edit