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-jenkselder-futhark-runesfutharkfuthark-runeslinguisticsnordicrstudiorunerunes
94.7 match 10 stars 3.70 score 2 scriptsrunehaubo
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 dependentsrunehaubo
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 dependentslme4
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.
1.5 match 647 stars 20.69 score 35k scripts 1.5k dependentssingmann
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 dependentskaz-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-characteristicsdescriptive-statisticsstatistics
1.5 match 221 stars 13.55 score 2.3k scripts 12 dependentsjulienvollering
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 scriptsaigorahub
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 scriptsbioc
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.
geneexpressiontranscriptomicsrnaseqtwochanneltranscriptiongenetargetnetworkenrichmentdifferentialexpressionnetworknetworkinferencegenesetenrichmentfunctionalprediction
1.2 match 3.82 score 22 scriptsxijianzheng
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