This package provides integration between LSP mode and treemacs, and implementation of treeview controls using treemacs as a tree renderer.
Automatically install and use tree-sitter major modes in Emacs 29+. If the tree-sitter version can’t be used, fall back to the original major mode
The package defines a mechanism for specifying connected trees that uses a tabular
environment to generate node positions. The package uses PostScript code, loaded by Dvips, so output can only be generated by use of Dvips.
The package lingmacros.sty
defines a few macros for linguists: \enumsentence
for enumerating sentence examples, simple tabular
-based non-connected tree macros, and gloss macros.
An R re-implementation of the treeinterpreter package on PyPI
<https://pypi.org/project/treeinterpreter/>. Each prediction can be decomposed as prediction = bias + feature_1_contribution + ... + feature_n_contribution'. This decomposition is then used to calculate the Mean Decrease Impurity (MDI) and Mean Decrease Impurity using out-of-bag samples (MDI-oob) feature importance measures based on the work of Li et al. (2019) <doi:10.48550/arXiv.1906.10845>
.
Documentation at https://melpa.org/#/treemacs-evil
Documentation at https://melpa.org/#/org-treescope
Documentation at https://melpa.org/#/interval-tree
Documentation at https://melpa.org/#/org-treeusage
Documentation at https://melpa.org/#/org-side-tree
This package provides a GraphQL grammar for the Tree-sitter library.
This package provides a TLA+ grammar for the Tree-sitter library.
This package provides a Verilog grammar for the Tree-sitter library.
This package provides a Haskell grammar for the Tree-sitter library.
This package provides a Clojure grammar for the Tree-sitter library.
This package provides a C# grammar for the Tree-sitter library.
cl-draw-cons-tree
draws a cons tree in ASCII-art style.
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-persp
Documentation at https://melpa.org/#/treemacs-magit
Documentation at https://melpa.org/#/treesit-ispell
Documentation at https://melpa.org/#/evil-tree-edit
Documentation at https://melpa.org/#/org-tree-slide
React JSON Viewer Component, Extracted from redux-devtools