R-universe search: rune

Showing 12 of total 12 results (show query)

tallguyjenks

runes:Convert Strings to Elder Futhark Runes

Convert a string of text characters to Elder Futhark Runes <https://en.wikipedia.org/wiki/Elder_Futhark>.

Maintained by Bryan Jenks. Last updated 4 years ago.

bryan-jenks elder-futhark-runes futhark futhark-runes linguistics nordic rstudio rune runes

94.7 match 10 stars 3.70 score 2 scripts

runehaubo

lmerTest:Tests in Linear Mixed Effects Models

Provides p-values in type I, II or III anova and summary tables for lmer model fits (cf. lme4) via Satterthwaite's degrees of freedom method. A Kenward-Roger method is also available via the pbkrtest package. Model selection methods include step, drop1 and anova-like tables for random effects (ranova). Methods for Least-Square means (LS-means) and tests of linear contrasts of fixed effects are also available.

Maintained by Rune Haubo Bojesen Christensen. Last updated 4 years ago.

7.8 match 51 stars 13.00 score 13k scripts 90 dependents

runehaubo

ordinal:Regression Models for Ordinal Data

Implementation of cumulative link (mixed) models also known as ordered regression models, proportional odds models, proportional hazards models for grouped survival times and ordered logit/probit/... models. Estimation is via maximum likelihood and mixed models are fitted with the Laplace approximation and adaptive Gauss-Hermite quadrature. Multiple random effect terms are allowed and they may be nested, crossed or partially nested/crossed. Restrictions of symmetry and equidistance can be imposed on the thresholds (cut-points/intercepts). Standard model methods are available (summary, anova, drop-methods, step, confint, predict etc.) in addition to profile methods and slice methods for visualizing the likelihood function and checking convergence.

Maintained by Rune Haubo Bojesen Christensen. Last updated 3 months ago.

8.0 match 38 stars 12.41 score 1.3k scripts 178 dependents

lme4

lme4:Linear Mixed-Effects Models using 'Eigen' and S4

Fit linear and generalized linear mixed-effects models. The models and their components are represented using S4 classes and methods. The core computational algorithms are implemented using the 'Eigen' C++ library for numerical linear algebra and 'RcppEigen' "glue".

Maintained by Ben Bolker. Last updated 3 days ago.

cpp

1.5 match 647 stars 20.69 score 35k scripts 1.5k dependents

singmann

afex:Analysis of Factorial Experiments

Convenience functions for analyzing factorial experiments using ANOVA or mixed models. aov_ez(), aov_car(), and aov_4() allow specification of between, within (i.e., repeated-measures), or mixed (i.e., split-plot) ANOVAs for data in long format (i.e., one observation per row), automatically aggregating multiple observations per individual and cell of the design. mixed() fits mixed models using lme4::lmer() and computes p-values for all fixed effects using either Kenward-Roger or Satterthwaite approximation for degrees of freedom (LMM only), parametric bootstrap (LMMs and GLMMs), or likelihood ratio tests (LMMs and GLMMs). afex_plot() provides a high-level interface for interaction or one-way plots using ggplot2, combining raw data and model estimates. afex uses type 3 sums of squares as default (imitating commercial statistical software).

Maintained by Henrik Singmann. Last updated 7 months ago.

1.5 match 123 stars 14.50 score 1.4k scripts 15 dependents

kaz-yos

tableone:Create 'Table 1' to Describe Baseline Characteristics with or without Propensity Score Weights

Creates 'Table 1', i.e., description of baseline patient characteristics, which is essential in every medical research. Supports both continuous and categorical variables, as well as p-values and standardized mean differences. Weighted data are supported via the 'survey' package.

Maintained by Kazuki Yoshida. Last updated 3 years ago.

baseline-characteristics descriptive-statistics statistics

1.5 match 221 stars 13.55 score 2.3k scripts 12 dependents

julienvollering

MIAmaxent:A Modular, Integrated Approach to Maximum Entropy Distribution Modeling

Tools for training, selecting, and evaluating maximum entropy (and standard logistic regression) distribution models. This package provides tools for user-controlled transformation of explanatory variables, selection of variables by nested model comparison, and flexible model evaluation and projection. It follows principles based on the maximum- likelihood interpretation of maximum entropy modeling, and uses infinitely- weighted logistic regression for model fitting. The package is described in Vollering et al. (2019; <doi:10.1002/ece3.5654>).

Maintained by Julien Vollering. Last updated 7 months ago.

1.6 match 14 stars 6.53 score 30 scripts

aigorahub

sensR:Thurstonian Models for Sensory Discrimination

Provides methods for sensory discrimination methods; duotrio, tetrad, triangle, 2-AFC, 3-AFC, A-not A, same-different, 2-AC and degree-of-difference. This enables the calculation of d-primes, standard errors of d-primes, sample size and power computations, and comparisons of different d-primes. Methods for profile likelihood confidence intervals and plotting are included. Most methods are described in Brockhoff, P.B. and Christensen, R.H.B. (2010) <doi:10.1016/j.foodqual.2009.04.003>.

Maintained by Dominik Rafacz. Last updated 1 years ago.

1.6 match 7 stars 4.92 score 77 scripts

weiguonimh

mMARCH.AC:Processing of Accelerometry Data with 'GGIR' in mMARCH

Mobile Motor Activity Research Consortium for Health (mMARCH) is a collaborative network of studies of clinical and community samples that employ common clinical, biological, and digital mobile measures across involved studies. One of the main scientific goals of mMARCH sites is developing a better understanding of the inter-relationships between accelerometry-measured physical activity (PA), sleep (SL), and circadian rhythmicity (CR) and mental and physical health in children, adolescents, and adults. Currently, there is no consensus on a standard procedure for a data processing pipeline of raw accelerometry data, and few open-source tools to facilitate their development. The R package 'GGIR' is the most prominent open-source software package that offers great functionality and tremendous user flexibility to process raw accelerometry data. However, even with 'GGIR', processing done in a harmonized and reproducible fashion requires a non-trivial amount of expertise combined with a careful implementation. In addition, novel accelerometry-derived features of PA/SL/CR capturing multiscale, time-series, functional, distributional and other complimentary aspects of accelerometry data being constantly proposed and become available via non-GGIR R implementations. To address these issues, mMARCH developed a streamlined harmonized and reproducible pipeline for loading and cleaning raw accelerometry data, extracting features available through 'GGIR' as well as through non-GGIR R packages, implementing several data and feature quality checks, merging all features of PA/SL/CR together, and performing multiple analyses including Joint Individual Variation Explained (JIVE), an unsupervised machine learning dimension reduction technique that identifies latent factors capturing joint across and individual to each of three domains of PA/SL/CR. In detail, the pipeline generates all necessary R/Rmd/shell files for data processing after running 'GGIR' (v2.4.0) for accelerometer data. In module 1, all csv files in the 'GGIR' output directory were read, transformed and then merged. In module 2, the 'GGIR' output files were checked and summarized in one excel sheet. In module 3, the merged data was cleaned according to the number of valid hours on each night and the number of valid days for each subject. In module 4, the cleaned activity data was imputed by the average Euclidean norm minus one (ENMO) over all the valid days for each subject. Finally, a comprehensive report of data processing was created using Rmarkdown, and the report includes few exploratory plots and multiple commonly used features extracted from minute level actigraphy data. Reference: Guo W, Leroux A, Shou S, Cui L, Kang S, Strippoli MP, Preisig M, Zipunnikov V, Merikangas K (2022) Processing of accelerometry data with GGIR in Motor Activity Research Consortium for Health (mMARCH) Journal for the Measurement of Physical Behaviour, 6(1): 37-44.

Maintained by Wei Guo. Last updated 2 years ago.

openjdk

1.7 match 2 stars 4.41 score 26 scripts

bioc

RegEnrich:Gene regulator enrichment analysis

This package is a pipeline to identify the key gene regulators in a biological process, for example in cell differentiation and in cell development after stimulation. There are four major steps in this pipeline: (1) differential expression analysis; (2) regulator-target network inference; (3) enrichment analysis; and (4) regulators scoring and ranking.

Maintained by Weiyang Tao. Last updated 5 months ago.

geneexpression transcriptomics rnaseq twochannel transcription genetarget networkenrichment differentialexpression network networkinference genesetenrichment functionalprediction

1.2 match 3.82 score 22 scripts

weiguonimh

postGGIR:Data Processing after Running 'GGIR' for Accelerometer Data

Generate all necessary R/Rmd/shell files for data processing after running 'GGIR' (v2.4.0) for accelerometer data. In part 1, all csv files in the GGIR output directory were read, transformed and then merged. In part 2, the GGIR output files were checked and summarized in one excel sheet. In part 3, the merged data was cleaned according to the number of valid hours on each night and the number of valid days for each subject. In part 4, the cleaned activity data was imputed by the average Euclidean norm minus one (ENMO) over all the valid days for each subject. Finally, a comprehensive report of data processing was created using Rmarkdown, and the report includes few exploratory plots and multiple commonly used features extracted from minute level actigraphy data.

Maintained by Wei Guo. Last updated 3 years ago.

openjdk

1.7 match 2.20 score 16 scripts

xijianzheng

coefa:Meta Analysis of Factor Analysis Based on CO-Occurrence Matrices

Provide a series of functions to conduct a meta analysis of factor analysis based on co-occurrence matrices. The tool can be used to solve the factor structure (i.e. inner structure of a construct, or scale) debate in several disciplines, such as psychology, psychiatry, management, education so on. References: Shafer (2005) <doi:10.1037/1040-3590.17.3.324>; Shafer (2006) <doi:10.1002/jclp.20213>; Loeber and Schmaling (1985) <doi:10.1007/BF00910652>.

Maintained by Xijian Zheng. Last updated 2 years ago.

1.2 match 2.70 score 4 scripts