Showing 11 of total 11 results (show query)
rstudio
renv:Project Environments
A dependency management toolkit for R. Using 'renv', you can create and manage project-local R libraries, save the state of these libraries to a 'lockfile', and later restore your library as required. Together, these tools can help make your projects more isolated, portable, and reproducible.
Maintained by Kevin Ushey. Last updated 2 days ago.
1.0k stars 18.59 score 1.5k scripts 114 dependents
jtextor
dagitty:Graphical Analysis of Structural Causal Models
A port of the web-based software 'DAGitty', available at <https://dagitty.net>, for analyzing structural causal models (also known as directed acyclic graphs or DAGs). This package computes covariate adjustment sets for estimating causal effects, enumerates instrumental variables, derives testable implications (d-separation and vanishing tetrads), generates equivalent models, and includes a simple facility for data simulation.
Maintained by Johannes Textor. Last updated 4 months ago.
302 stars 12.83 score 1.7k scripts 11 dependents
hputter
mstate:Data Preparation, Estimation and Prediction in Multi-State Models
Contains functions for data preparation, descriptives, hazard estimation and prediction with Aalen-Johansen or simulation in competing risks and multi-state models, see Putter, Fiocco, Geskus (2007) <doi:10.1002/sim.2712>.
Maintained by Hein Putter. Last updated 1 months ago.
11 stars 12.20 score 322 scripts 57 dependents
predictiveecology
SpaDES.core:Core Utilities for Developing and Running Spatially Explicit Discrete Event Models
Provides the core framework for a discrete event system to implement a complete data-to-decisions, reproducible workflow. The core components facilitate the development of modular pieces, and enable the user to include additional functionality by running user-built modules. Includes conditional scheduling, restart after interruption, packaging of reusable modules, tools for developing arbitrary automated workflows, automated interweaving of modules of different temporal resolution, and tools for visualizing and understanding the within-project dependencies. The suggested package 'NLMR' can be installed from the repository (<https://PredictiveEcology.r-universe.dev>).
Maintained by Eliot J B McIntire. Last updated 1 months ago.
discrete-events-simulationssimulation-frameworksimulation-modeling
10 stars 10.61 score 142 scripts 6 dependents
ropensci
jqr:Client for 'jq', a 'JSON' Processor
Client for 'jq', a 'JSON' processor (<https://jqlang.github.io/jq/>), written in C. 'jq' allows the following with 'JSON' data: index into, parse, do calculations, cut up and filter, change key names and values, perform conditionals and comparisons, and more.
Maintained by Jeroen Ooms. Last updated 3 months ago.
144 stars 10.04 score 95 scripts 28 dependents
sem-in-r
seminr:Building and Estimating Structural Equation Models
A powerful, easy to syntax for specifying and estimating complex Structural Equation Models. Models can be estimated using Partial Least Squares Path Modeling or Covariance-Based Structural Equation Modeling or covariance based Confirmatory Factor Analysis. Methods described in Ray, Danks, and Valdez (2021).
Maintained by Nicholas Patrick Danks. Last updated 3 years ago.
common-factorscompositesconstructpls-models
62 stars 7.46 score 284 scripts
gsucarrat
gets:General-to-Specific (GETS) Modelling and Indicator Saturation Methods
Automated General-to-Specific (GETS) modelling of the mean and variance of a regression, and indicator saturation methods for detecting and testing for structural breaks in the mean, see Pretis, Reade and Sucarrat (2018) <doi:10.18637/jss.v086.i03> for an overview of the package. In advanced use, the estimator and diagnostics tests can be fully user-specified, see Sucarrat (2021) <doi:10.32614/RJ-2021-024>.
Maintained by Genaro Sucarrat. Last updated 8 months ago.
9 stars 6.79 score 73 scripts 3 dependents
mbq
vistla:Detecting Influence Paths with Information Theory
Traces information spread through interactions between features, utilising information theory measures and a higher-order generalisation of the concept of widest paths in graphs. In particular, 'vistla' can be used to better understand the results of high-throughput biomedical experiments, by organising the effects of the investigated intervention in a tree-like hierarchy from direct to indirect ones, following the plausible information relay circuits. Due to its higher-order nature, 'vistla' can handle multi-modality and assign multiple roles to a single feature.
Maintained by Miron B. Kursa. Last updated 1 months ago.
4.70 score 3 scripts
mdtrinh
paths:An Imputation Approach to Estimating Path-Specific Causal Effects
In causal mediation analysis with multiple causally ordered mediators, a set of path-specific effects are identified under standard ignorability assumptions. This package implements an imputation approach to estimating these effects along with a set of bias formulas for conducting sensitivity analysis (Zhou and Yamamoto <doi:10.31235/osf.io/2rx6p>). It contains two main functions: paths() for estimating path-specific effects and sens() for conducting sensitivity analysis. Estimation uncertainty is quantified using the nonparametric bootstrap.
Maintained by Minh Trinh. Last updated 4 years ago.
3.71 score 102 scripts
otryakhin-dmitry
rlfsm:Simulations and Statistical Inference for Linear Fractional Stable Motions
Contains functions for simulating the linear fractional stable motion according to the algorithm developed by Mazur and Otryakhin <doi:10.32614/RJ-2020-008> based on the method from Stoev and Taqqu (2004) <doi:10.1142/S0218348X04002379>, as well as functions for estimation of parameters of these processes introduced by Mazur, Otryakhin and Podolskij (2018) <arXiv:1802.06373>, and also different related quantities.
Maintained by Dmitry Otryakhin. Last updated 3 years ago.
3.00 score 20 scripts
schaubert
BTLLasso:Modelling Heterogeneity in Paired Comparison Data
Performs 'BTLLasso' as described by Schauberger and Tutz (2019) <doi:10.18637/jss.v088.i09> and Schauberger and Tutz (2017) <doi:10.1177/1471082X17693086>. BTLLasso is a method to include different types of variables in paired comparison models and, therefore, to allow for heterogeneity between subjects. Variables can be subject-specific, object-specific and subject-object-specific and can have an influence on the attractiveness/strength of the objects. Suitable L1 penalty terms are used to cluster certain effects and to reduce the complexity of the models.
Maintained by Gunther Schauberger. Last updated 1 years ago.
1 stars 1.32 score 21 scripts