Showing 22 of total 22 results (show query)
easystats
correlation:Methods for Correlation Analysis
Lightweight package for computing different kinds of correlations, such as partial correlations, Bayesian correlations, multilevel correlations, polychoric correlations, biweight correlations, distance correlations and more. Part of the 'easystats' ecosystem. References: Makowski et al. (2020) <doi:10.21105/joss.02306>.
Maintained by Brenton M. Wiernik. Last updated 28 days ago.
bayesianbayesian-correlationsbiserialcorcorrelationcorrelation-analysiscorrelationseasystatsgammagaussian-graphical-modelshacktoberfestmatrixmultilevel-correlationsoutlierspartialpartial-correlationsregressionrobustspearman
439 stars 14.23 score 672 scripts 10 dependents
kkholst
lava:Latent Variable Models
A general implementation of Structural Equation Models with latent variables (MLE, 2SLS, and composite likelihood estimators) with both continuous, censored, and ordinal outcomes (Holst and Budtz-Joergensen (2013) <doi:10.1007/s00180-012-0344-y>). Mixture latent variable models and non-linear latent variable models (Holst and Budtz-Joergensen (2020) <doi:10.1093/biostatistics/kxy082>). The package also provides methods for graph exploration (d-separation, back-door criterion), simulation of general non-linear latent variable models, and estimation of influence functions for a broad range of statistical models.
Maintained by Klaus K. Holst. Last updated 3 months ago.
latent-variable-modelssimulationstatisticsstructural-equation-models
33 stars 12.87 score 610 scripts 478 dependents
salvatoremangiafico
rcompanion:Functions to Support Extension Education Program Evaluation
Functions and datasets to support Summary and Analysis of Extension Program Evaluation in R, and An R Companion for the Handbook of Biological Statistics. Vignettes are available at <https://rcompanion.org>.
Maintained by Salvatore Mangiafico. Last updated 2 months ago.
4 stars 8.01 score 2.4k scripts 5 dependents
sciviews
SciViews:'SciViews' - Data Processing and Visualization with the 'SciViews::R' Dialect
The 'SciViews::R' dialect provides a set of functions that streamlines data input, process, analysis and visualization especially, but not exclusively, for beginners or occasional users. It mixes base R and tidyverse, plus another set of CRAN packages for an easy and coherent use of R.
Maintained by Philippe Grosjean. Last updated 7 months ago.
8 stars 7.62 score 116 scripts 1 dependents
cran
agricolae:Statistical Procedures for Agricultural Research
Original idea was presented in the thesis "A statistical analysis tool for agricultural research" to obtain the degree of Master on science, National Engineering University (UNI), Lima-Peru. Some experimental data for the examples come from the CIP and others research. Agricolae offers extensive functionality on experimental design especially for agricultural and plant breeding experiments, which can also be useful for other purposes. It supports planning of lattice, Alpha, Cyclic, Complete Block, Latin Square, Graeco-Latin Squares, augmented block, factorial, split and strip plot designs. There are also various analysis facilities for experimental data, e.g. treatment comparison procedures and several non-parametric tests comparison, biodiversity indexes and consensus cluster.
Maintained by Felipe de Mendiburu. Last updated 1 years ago.
7 stars 7.01 score 15 dependents
bioc
rexposome:Exposome exploration and outcome data analysis
Package that allows to explore the exposome and to perform association analyses between exposures and health outcomes.
Maintained by Xavier Escribà Montagut. Last updated 5 months ago.
softwarebiologicalquestioninfrastructuredataimportdatarepresentationbiomedicalinformaticsexperimentaldesignmultiplecomparisonclassificationclustering
5.70 score 28 scripts 1 dependents
radiant-rstats
radiant.basics:Basics Menu for Radiant: Business Analytics using R and Shiny
The Radiant Basics menu includes interfaces for probability calculation, central limit theorem simulation, comparing means and proportions, goodness-of-fit testing, cross-tabs, and correlation. The application extends the functionality in 'radiant.data'.
Maintained by Vincent Nijs. Last updated 11 months ago.
8 stars 5.56 score 79 scripts 3 dependents
simonyansenzhao
wsrf:Weighted Subspace Random Forest for Classification
A parallel implementation of Weighted Subspace Random Forest. The Weighted Subspace Random Forest algorithm was proposed in the International Journal of Data Warehousing and Mining by Baoxun Xu, Joshua Zhexue Huang, Graham Williams, Qiang Wang, and Yunming Ye (2012) <DOI:10.4018/jdwm.2012040103>. The algorithm can classify very high-dimensional data with random forests built using small subspaces. A novel variable weighting method is used for variable subspace selection in place of the traditional random variable sampling.This new approach is particularly useful in building models from high-dimensional data.
Maintained by He Zhao. Last updated 2 years ago.
14 stars 4.89 score 11 scripts
lpfgarcia
ECoL:Complexity Measures for Supervised Problems
Provides measures to characterize the complexity of classification and regression problems based on aspects that quantify the linearity of the data, the presence of informative feature, the sparsity and dimensionality of the datasets. This package provides bug fixes, generalizations and implementations of many state of the art measures. The measures are described in the papers: Lorena et al. (2019) <doi:10.1145/3347711> and Lorena et al. (2018) <doi:10.1007/s10994-017-5681-1>.
Maintained by Luis Garcia. Last updated 4 years ago.
complexity-measurespattern-recognition
57 stars 4.78 score 21 scriptsbioc
MetNet:Inferring metabolic networks from untargeted high-resolution mass spectrometry data
MetNet contains functionality to infer metabolic network topologies from quantitative data and high-resolution mass/charge information. Using statistical models (including correlation, mutual information, regression and Bayes statistics) and quantitative data (intensity values of features) adjacency matrices are inferred that can be combined to a consensus matrix. Mass differences calculated between mass/charge values of features will be matched against a data frame of supplied mass/charge differences referring to transformations of enzymatic activities. In a third step, the two levels of information are combined to form a adjacency matrix inferred from both quantitative and structure information.
Maintained by Thomas Naake. Last updated 5 months ago.
immunooncologymetabolomicsmassspectrometrynetworkregression
4.70 score 1 scriptsbioc
TIN:Transcriptome instability analysis
The TIN package implements a set of tools for transcriptome instability analysis based on exon expression profiles. Deviating exon usage is studied in the context of splicing factors to analyse to what degree transcriptome instability is correlated to splicing factor expression. In the transcriptome instability correlation analysis, the data is compared to both random permutations of alternative splicing scores and expression of random gene sets.
Maintained by Bjarne Johannessen. Last updated 5 months ago.
exonarraymicroarraygeneexpressionalternativesplicinggeneticsdifferentialsplicing
4.30 score 1 scriptsbioc
flowCyBar:Analyze flow cytometric data using gate information
A package to analyze flow cytometric data using gate information to follow population/community dynamics
Maintained by Joachim Schumann. Last updated 5 months ago.
immunooncologycellbasedassaysclusteringflowcytometrysoftwarevisualization
4.15 score 1 scripts
paulnorthrop
smovie:Some Movies to Illustrate Concepts in Statistics
Provides movies to help students to understand statistical concepts. The 'rpanel' package <https://cran.r-project.org/package=rpanel> is used to create interactive plots that move to illustrate key statistical ideas and methods. There are movies to: visualise probability distributions (including user-supplied ones); illustrate sampling distributions of the sample mean (central limit theorem), the median, the sample maximum (extremal types theorem) and (the Fisher transformation of the) product moment correlation coefficient; examine the influence of an individual observation in simple linear regression; illustrate key concepts in statistical hypothesis testing. Also provided are dpqr functions for the distribution of the Fisher transformation of the correlation coefficient under sampling from a bivariate normal distribution.
Maintained by Paul J. Northrop. Last updated 1 years ago.
central-limit-theoremcorrelation-coefficientextremal-types-theoremextremeshypothesis-testinglikelihood-ratio-testlinear-regressionlog-likelihoodmovieprobability-distributionsregressionscore-teststatistical-conceptsstatisticsstatistics-learningteachingteaching-materialstest-statisticwald-test
1 stars 3.70 score 10 scripts
dannyarends
ctl:Correlated Trait Locus Mapping
Identification and network inference of genetic loci associated with correlation changes in quantitative traits (called correlated trait loci, CTLs). Arends et al. (2016) <doi:10.21105/joss.00087>.
Maintained by Danny Arends. Last updated 1 years ago.
3.31 score 103 scriptsbioc
diggit:Inference of Genetic Variants Driving Cellular Phenotypes
Inference of Genetic Variants Driving Cellullar Phenotypes by the DIGGIT algorithm
Maintained by Mariano J Alvarez. Last updated 5 months ago.
systemsbiologynetworkenrichmentgeneexpressionfunctionalpredictiongeneregulation
3.30 score 3 scripts
rbarkerclarke
gtexture:Generalized Application of Co-Occurrence Matrices and Haralick Texture
Generalizes application of gray-level co-occurrence matrix (GLCM) metrics to objects outside of images. The current focus is to apply GLCM metrics to the study of biological networks and fitness landscapes that are used in studying evolutionary medicine and biology, particularly the evolution of cancer resistance. The package was used in our publication, Barker-Clarke et al. (2023) <doi:10.1088/1361-6560/ace305>. A general reference to learn more about mathematical oncology can be found at Rockne et al. (2019) <doi:10.1088/1478-3975/ab1a09>.
Maintained by Rowan Barker-Clarke. Last updated 12 months ago.
3.00 score 1 scripts
ycroissant
descstat:Tools for Descriptive Statistics
A toolbox for descriptive statistics, based on the computation of frequency and contingency tables. Several statistical functions and plot methods are provided to describe univariate or bivariate distributions of factors, integer series and numerical series either provided as individual values or as bins.
Maintained by Yves Croissant. Last updated 4 years ago.
2 stars 3.00 score 1 scripts
lisaannyu
lifelogr:Life Logging
Provides a framework for combining self-data (exercise, sleep, etc.) from multiple sources (fitbit, Apple Health), creating visualizations, and experimenting on onself.
Maintained by Lisa Ann Yu. Last updated 8 years ago.
2.93 score 43 scripts
christopherkenny
divseg:Calculate Diversity and Segregation Indices
Implements common measures of diversity and spatial segregation. This package has tools to compute the majority of measures are reviewed in Massey and Denton (1988) <doi:10.2307/2579183>. Multiple common measures of within-geography diversity are implemented as well. All functions operate on data frames with a 'tidyselect' based workflow.
Maintained by Christopher T. Kenny. Last updated 10 months ago.
1 stars 2.78 score 12 scripts
gabrielshimizu
AgroReg:Regression Analysis Linear and Nonlinear for Agriculture
Linear and nonlinear regression analysis common in agricultural science articles (Archontoulis & Miguez (2015). <doi:10.2134/agronj2012.0506>). The package includes polynomial, exponential, gaussian, logistic, logarithmic, segmented, non-parametric models, among others. The functions return the model coefficients and their respective p values, coefficient of determination, root mean square error, AIC, BIC, as well as graphs with the equations automatically.
Maintained by Gabriel Danilo Shimizu. Last updated 1 years ago.
2.71 score 102 scripts
vungocbinh2009
thongke:Simple statistic package
Một gói lệnh thống kê đơn giản và dễ hiểu.
Maintained by Vu Ngọc Bình. Last updated 11 months ago.
2 stars 2.48 score 1 dependents
wernerstahel
relevance:Calculate Relevance and Significance Measures
Calculates relevance and significance values for simple models and for many types of regression models. These are introduced in 'Stahel, Werner A.' (2021) "Measuring Significance and Relevance instead of p-values." <https://stat.ethz.ch/~stahel/relevance/stahel-relevance2103.pdf>. These notions are also applied to replication studies, as described in the manuscript 'Stahel, Werner A.' (2022) "'Replicability': Terminology, Measuring Success, and Strategy" available in the documentation.
Maintained by Werner A. Stahel. Last updated 1 years ago.
2.00 score 3 scripts