Showing 72 of total 72 results (show query)
kwstat
agridat:Agricultural Datasets
Datasets from books, papers, and websites related to agriculture. Example graphics and analyses are included. Data come from small-plot trials, multi-environment trials, uniformity trials, yield monitors, and more.
Maintained by Kevin Wright. Last updated 28 days ago.
84.6 match 125 stars 11.02 score 1.7k scripts 2 dependents
maarten14c
rice:Radiocarbon Equations
Provides functions for the calibration of radiocarbon dates, as well as options to calculate different radiocarbon realms (C14 age, F14C, pMC, D14C) and estimating the effects of contamination or local reservoir offsets (Reimer and Reimer 2001 <doi:10.1017/S0033822200038339>). The methods follow long-established recommendations such as Stuiver and Polach (1977) <doi:10.1017/S0033822200003672> and Reimer et al. (2004) <doi:10.1017/S0033822200033154>. This package complements the data package 'rintcal'.
Maintained by Maarten Blaauw. Last updated 2 months ago.
60.3 match 1 stars 6.13 score 13 scripts 4 dependents
alstat
ALUES:Agricultural Land Use Evaluation System
Evaluates land suitability for different crops production. The package is based on the Food and Agriculture Organization (FAO) and the International Rice Research Institute (IRRI) methodology for land evaluation. Development of ALUES is inspired by similar tool for land evaluation, Land Use Suitability Evaluation Tool (LUSET). The package uses fuzzy logic approach to evaluate land suitability of a particular area based on inputs such as rainfall, temperature, topography, and soil properties. The membership functions used for fuzzy modeling are the following: Triangular, Trapezoidal and Gaussian. The methods for computing the overall suitability of a particular area are also included, and these are the Minimum, Maximum and Average. Finally, ALUES is a highly optimized library with core algorithms written in C++.
Maintained by Al-Ahmadgaid B. Asaad. Last updated 3 years ago.
agricultural-modellingagriculturecpp
55.4 match 11 stars 6.38 score 55 scripts
wasquith
lmomco:L-Moments, Censored L-Moments, Trimmed L-Moments, L-Comoments, and Many Distributions
Extensive functions for Lmoments (LMs) and probability-weighted moments (PWMs), distribution parameter estimation, LMs for distributions, LM ratio diagrams, multivariate Lcomoments, and asymmetric (asy) trimmed LMs (TLMs). Maximum likelihood and maximum product spacings estimation are available. Right-tail and left-tail LM censoring by threshold or indicator variable are available. LMs of residual (resid) and reversed (rev) residual life are implemented along with 13 quantile operators for reliability analyses. Exact analytical bootstrap estimates of order statistics, LMs, and LM var-covars are available. Harri-Coble Tau34-squared Normality Test is available. Distributions with L, TL, and added (+) support for right-tail censoring (RC) encompass: Asy Exponential (Exp) Power [L], Asy Triangular [L], Cauchy [TL], Eta-Mu [L], Exp. [L], Gamma [L], Generalized (Gen) Exp Poisson [L], Gen Extreme Value [L], Gen Lambda [L, TL], Gen Logistic [L], Gen Normal [L], Gen Pareto [L+RC, TL], Govindarajulu [L], Gumbel [L], Kappa [L], Kappa-Mu [L], Kumaraswamy [L], Laplace [L], Linear Mean Residual Quantile Function [L], Normal [L], 3p log-Normal [L], Pearson Type III [L], Polynomial Density-Quantile 3 and 4 [L], Rayleigh [L], Rev-Gumbel [L+RC], Rice [L], Singh Maddala [L], Slash [TL], 3p Student t [L], Truncated Exponential [L], Wakeby [L], and Weibull [L].
Maintained by William Asquith. Last updated 1 months ago.
flood-frequency-analysisl-momentsmle-estimationmps-estimationprobability-distributionrainfall-frequency-analysisreliability-analysisrisk-analysissurvival-analysis
16.3 match 2 stars 8.06 score 458 scripts 38 dependents
covaruber
sommer:Solving Mixed Model Equations in R
Structural multivariate-univariate linear mixed model solver for estimation of multiple random effects with unknown variance-covariance structures (e.g., heterogeneous and unstructured) and known covariance among levels of random effects (e.g., pedigree and genomic relationship matrices) (Covarrubias-Pazaran, 2016 <doi:10.1371/journal.pone.0156744>; Maier et al., 2015 <doi:10.1016/j.ajhg.2014.12.006>; Jensen et al., 1997). REML estimates can be obtained using the Direct-Inversion Newton-Raphson and Direct-Inversion Average Information algorithms for the problems r x r (r being the number of records) or using the Henderson-based average information algorithm for the problem c x c (c being the number of coefficients to estimate). Spatial models can also be fitted using the two-dimensional spline functionality available.
Maintained by Giovanny Covarrubias-Pazaran. Last updated 22 days ago.
average-informationmixed-modelsrcpparmadilloopenblascppopenmp
8.0 match 43 stars 12.70 score 300 scripts 9 dependents
bioc
BioNERO:Biological Network Reconstruction Omnibus
BioNERO aims to integrate all aspects of biological network inference in a single package, including data preprocessing, exploratory analyses, network inference, and analyses for biological interpretations. BioNERO can be used to infer gene coexpression networks (GCNs) and gene regulatory networks (GRNs) from gene expression data. Additionally, it can be used to explore topological properties of protein-protein interaction (PPI) networks. GCN inference relies on the popular WGCNA algorithm. GRN inference is based on the "wisdom of the crowds" principle, which consists in inferring GRNs with multiple algorithms (here, CLR, GENIE3 and ARACNE) and calculating the average rank for each interaction pair. As all steps of network analyses are included in this package, BioNERO makes users avoid having to learn the syntaxes of several packages and how to communicate between them. Finally, users can also identify consensus modules across independent expression sets and calculate intra and interspecies module preservation statistics between different networks.
Maintained by Fabricio Almeida-Silva. Last updated 5 months ago.
softwaregeneexpressiongeneregulationsystemsbiologygraphandnetworkpreprocessingnetworknetworkinference
7.4 match 27 stars 7.78 score 50 scripts 1 dependents
omarbenites
cropdatape:Open Data of Agricultural Production of Crops of Peru
Provides peruvian agricultural production data from the Agriculture Minestry of Peru (MINAGRI). The first version includes 6 crops: rice, quinoa, potato, sweet potato, tomato and wheat; all of them across 24 departments. Initially, in excel files which has been transformed and assembled using tidy data principles, i.e. each variable is in a column, each observation is a row and each value is in a cell. The variables variables are sowing and harvest area per crop, yield, production and price per plot, every one year, from 2004 to 2014.
Maintained by Omar Benites-Alfaro. Last updated 6 years ago.
agricultureagroinformaticscropsdatabaseopendataperupotatoquinoaricesweettomatounalmwheat
11.5 match 1 stars 4.30 score 2 scripts
kosukehamazaki
RAINBOWR:Genome-Wide Association Study with SNP-Set Methods
By using 'RAINBOWR' (Reliable Association INference By Optimizing Weights with R), users can test multiple SNPs (Single Nucleotide Polymorphisms) simultaneously by kernel-based (SNP-set) methods. This package can also be applied to haplotype-based GWAS (Genome-Wide Association Study). Users can test not only additive effects but also dominance and epistatic effects. In detail, please check our paper on PLOS Computational Biology: Kosuke Hamazaki and Hiroyoshi Iwata (2020) <doi:10.1371/journal.pcbi.1007663>.
Maintained by Kosuke Hamazaki. Last updated 3 months ago.
7.4 match 22 stars 5.99 score 22 scripts
valentint
rrcov:Scalable Robust Estimators with High Breakdown Point
Robust Location and Scatter Estimation and Robust Multivariate Analysis with High Breakdown Point: principal component analysis (Filzmoser and Todorov (2013), <doi:10.1016/j.ins.2012.10.017>), linear and quadratic discriminant analysis (Todorov and Pires (2007)), multivariate tests (Todorov and Filzmoser (2010) <doi:10.1016/j.csda.2009.08.015>), outlier detection (Todorov et al. (2010) <doi:10.1007/s11634-010-0075-2>). See also Todorov and Filzmoser (2009) <urn:isbn:978-3838108148>, Todorov and Filzmoser (2010) <doi:10.18637/jss.v032.i03> and Boudt et al. (2019) <doi:10.1007/s11222-019-09869-x>.
Maintained by Valentin Todorov. Last updated 7 months ago.
4.0 match 2 stars 10.57 score 484 scripts 96 dependentscovaruber
lme4breeding:Relationship-Based Mixed-Effects Models
Fit relationship-based and customized mixed-effects models with complex variance-covariance structures using the 'lme4' machinery. The core computational algorithms are implemented using the 'Eigen' 'C++' library for numerical linear algebra and 'RcppEigen' 'glue'.
Maintained by Giovanny Covarrubias-Pazaran. Last updated 22 days ago.
8.0 match 6 stars 5.23 score 7 scripts
friendly
heplots:Visualizing Hypothesis Tests in Multivariate Linear Models
Provides HE plot and other functions for visualizing hypothesis tests in multivariate linear models. HE plots represent sums-of-squares-and-products matrices for linear hypotheses and for error using ellipses (in two dimensions) and ellipsoids (in three dimensions). The related 'candisc' package provides visualizations in a reduced-rank canonical discriminant space when there are more than a few response variables.
Maintained by Michael Friendly. Last updated 9 days ago.
linear-hypothesesmatricesmultivariate-linear-modelsplotrepeated-measure-designsvisualizing-hypothesis-tests
3.5 match 9 stars 11.49 score 1.1k scripts 7 dependents
robjhyndman
fpp2:Data for "Forecasting: Principles and Practice" (2nd Edition)
All data sets required for the examples and exercises in the book "Forecasting: principles and practice" (2nd ed, 2018) by Rob J Hyndman and George Athanasopoulos <https://otexts.com/fpp2/>. All packages required to run the examples are also loaded.
Maintained by Rob Hyndman. Last updated 2 years ago.
4.0 match 106 stars 8.57 score 1.8k scripts 1 dependentsrobjhyndman
fpp3:Data for "Forecasting: Principles and Practice" (3rd Edition)
All data sets required for the examples and exercises in the book "Forecasting: principles and practice" by Rob J Hyndman and George Athanasopoulos <https://OTexts.com/fpp3/>. All packages required to run the examples are also loaded. Additional data sets not used in the book are also included.
Maintained by Rob Hyndman. Last updated 6 months ago.
4.0 match 142 stars 8.47 score 2.5k scripts
wjbraun
DAAG:Data Analysis and Graphics Data and Functions
Functions and data sets used in examples and exercises in the text Maindonald, J.H. and Braun, W.J. (2003, 2007, 2010) "Data Analysis and Graphics Using R", and in an upcoming Maindonald, Braun, and Andrews text that builds on this earlier text.
Maintained by W. John Braun. Last updated 11 months ago.
3.5 match 8.25 score 1.2k scripts 1 dependents
silvadenisson
politicsR:Calculating Political System Metrics
A toolbox to facilitate the calculation of political system indicators for researchers. This package offers a variety of basic indicators related to electoral systems, party systems, elections, and parliamentary studies, as well as others. Main references are: Loosemore and Hanby (1971) <doi:10.1017/S000712340000925X>; Gallagher (1991) <doi:10.1016/0261-3794(91)90004-C>; Laakso and Taagepera (1979) <doi:10.1177/001041407901200101>; Rae (1968) <doi:10.1177/001041406800100305>; Hirschmaņ (1945) <ISBN:0-520-04082-1>; Kesselman (1966) <doi:10.2307/1953769>; Jones and Mainwaring (2003) <doi:10.1177/13540688030092002>; Rice (1925) <doi:10.2307/2142407>; Pedersen (1979) <doi:10.1111/j.1475-6765.1979.tb01267.x>; SANTOS (2002) <ISBN:85-225-0395-8>.
Maintained by Denisson Silva. Last updated 2 years ago.
6.1 match 8 stars 4.08 score 4 scripts
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.
3.5 match 7 stars 7.01 score 15 dependentscran
splm:Econometric Models for Spatial Panel Data
ML and GM estimation and diagnostic testing of econometric models for spatial panel data.
Maintained by Giovanni Millo. Last updated 1 years ago.
7.5 match 10 stars 3.01 score
rspatial
geodata:Download Geographic Data
Functions for downloading of geographic data for use in spatial analysis and mapping. The package facilitates access to climate, crops, elevation, land use, soil, species occurrence, accessibility, administrative boundaries and other data.
Maintained by Robert J. Hijmans. Last updated 1 months ago.
2.0 match 162 stars 10.75 score 1.5k scripts 7 dependents
lukece
CoDaImpact:Interpreting CoDa Regression Models
Provides methods for interpreting CoDa (Compositional Data) regression models along the lines of "Pairwise share ratio interpretations of compositional regression models" (Dargel and Thomas-Agnan 2024) <doi:10.1016/j.csda.2024.107945>. The new methods include variation scenarios, elasticities, elasticity differences and share ratio elasticities. These tools are independent of log-ratio transformations and allow an interpretation in the original space of shares. 'CoDaImpact' is designed to be used with the 'compositions' package and its ecosystem.
Maintained by Lukas Dargel. Last updated 12 months ago.
4.9 match 4.30 scorebioc
VaSP:Quantification and Visualization of Variations of Splicing in Population
Discovery of genome-wide variable alternative splicing events from short-read RNA-seq data and visualizations of gene splicing information for publication-quality multi-panel figures in a population. (Warning: The visualizing function is removed due to the dependent package Sushi deprecated. If you want to use it, please change back to an older version.)
Maintained by Huihui Yu. Last updated 5 months ago.
rnaseqalternativesplicingdifferentialsplicingstatisticalmethodvisualizationpreprocessingclusteringdifferentialexpressionkeggimmunooncology3s-scoresalternative-splicingballgownrna-seqsplicingsqtlstatistics
4.0 match 3 stars 4.78 score 3 scripts
bioc
GENESIS:GENetic EStimation and Inference in Structured samples (GENESIS): Statistical methods for analyzing genetic data from samples with population structure and/or relatedness
The GENESIS package provides methodology for estimating, inferring, and accounting for population and pedigree structure in genetic analyses. The current implementation provides functions to perform PC-AiR (Conomos et al., 2015, Gen Epi) and PC-Relate (Conomos et al., 2016, AJHG). PC-AiR performs a Principal Components Analysis on genome-wide SNP data for the detection of population structure in a sample that may contain known or cryptic relatedness. Unlike standard PCA, PC-AiR accounts for relatedness in the sample to provide accurate ancestry inference that is not confounded by family structure. PC-Relate uses ancestry representative principal components to adjust for population structure/ancestry and accurately estimate measures of recent genetic relatedness such as kinship coefficients, IBD sharing probabilities, and inbreeding coefficients. Additionally, functions are provided to perform efficient variance component estimation and mixed model association testing for both quantitative and binary phenotypes.
Maintained by Stephanie M. Gogarten. Last updated 1 months ago.
snpgeneticvariabilitygeneticsstatisticalmethoddimensionreductionprincipalcomponentgenomewideassociationqualitycontrolbiocviews
1.6 match 36 stars 10.44 score 342 scripts 1 dependents
hdakpo
sfaR:Stochastic Frontier Analysis Routines
Maximum likelihood estimation for stochastic frontier analysis (SFA) of production (profit) and cost functions. The package includes the basic stochastic frontier for cross-sectional or pooled data with several distributions for the one-sided error term (i.e., Rayleigh, gamma, Weibull, lognormal, uniform, generalized exponential and truncated skewed Laplace), the latent class stochastic frontier model (LCM) as described in Dakpo et al. (2021) <doi:10.1111/1477-9552.12422>, for cross-sectional and pooled data, and the sample selection model as described in Greene (2010) <doi:10.1007/s11123-009-0159-1>, and applied in Dakpo et al. (2021) <doi:10.1111/agec.12683>. Several possibilities in terms of optimization algorithms are proposed.
Maintained by K Hervé Dakpo. Last updated 5 months ago.
3.8 match 4 stars 4.08 score 9 scripts 1 dependents
bmcclintock
momentuHMM:Maximum Likelihood Analysis of Animal Movement Behavior Using Multivariate Hidden Markov Models
Extended tools for analyzing telemetry data using generalized hidden Markov models. Features of momentuHMM (pronounced ``momentum'') include data pre-processing and visualization, fitting HMMs to location and auxiliary biotelemetry or environmental data, biased and correlated random walk movement models, discrete- or continuous-time HMMs, continuous- or discrete-space movement models, approximate Langevin diffusion models, hierarchical HMMs, multiple imputation for incorporating location measurement error and missing data, user-specified design matrices and constraints for covariate modelling of parameters, random effects, decoding of the state process, visualization of fitted models, model checking and selection, and simulation. See McClintock and Michelot (2018) <doi:10.1111/2041-210X.12995>.
Maintained by Brett McClintock. Last updated 1 months ago.
1.8 match 43 stars 8.47 score 162 scripts
rned
agriTutorial:Tutorial Analysis of Some Agricultural Experiments
Example software for the analysis of data from designed experiments, especially agricultural crop experiments. The basics of the analysis of designed experiments are discussed using real examples from agricultural field trials. A range of statistical methods using a range of R statistical packages are exemplified . The experimental data is made available as separate data sets for each example and the R analysis code is made available as example code. The example code can be readily extended, as required.
Maintained by Rodney Edmondson. Last updated 6 years ago.
7.5 match 1 stars 2.00 score 8 scripts
sam-dfmd
GSAQ:Gene Set Analysis with QTL
Computation of Quantitative Trait Loci hits in the selected gene set. Performing gene set validation with Quantitative Trait Loci information. Performing gene set enrichment analysis with available Quantitative Trait Loci data and computation of statistical significance value from gene set analysis. Obtaining the list of Quantitative Trait Loci hit genes along with their overlapped Quantitative Trait Loci names.
Maintained by Samarendra Das. Last updated 9 years ago.
10.9 match 1.34 score 11 scripts
dustinstoltz
text2map:R Tools for Text Matrices, Embeddings, and Networks
This is a collection of functions optimized for working with with various kinds of text matrices. Focusing on the text matrix as the primary object - represented either as a base R dense matrix or a 'Matrix' package sparse matrix - allows for a consistent and intuitive interface that stays close to the underlying mathematical foundation of computational text analysis. In particular, the package includes functions for working with word embeddings, text networks, and document-term matrices. Methods developed in Stoltz and Taylor (2019) <doi:10.1007/s42001-019-00048-6>, Taylor and Stoltz (2020) <doi:10.1007/s42001-020-00075-8>, Taylor and Stoltz (2020) <doi:10.15195/v7.a23>, and Stoltz and Taylor (2021) <doi:10.1016/j.poetic.2021.101567>.
Maintained by Dustin Stoltz. Last updated 3 months ago.
3.6 match 3.82 score 22 scripts
r-forge
frontier:Stochastic Frontier Analysis
Maximum Likelihood Estimation of Stochastic Frontier Production and Cost Functions. Two specifications are available: the error components specification with time-varying efficiencies (Battese and Coelli, 1992, <doi:10.1007/BF00158774>) and a model specification in which the firm effects are directly influenced by a number of variables (Battese and Coelli, 1995, <doi:10.1007/BF01205442>).
Maintained by Arne Henningsen. Last updated 8 months ago.
4.0 match 2.90 score 61 scripts
cmollica
PLMIX:Bayesian Analysis of Finite Mixture of Plackett-Luce Models
Fit finite mixtures of Plackett-Luce models for partial top rankings/orderings within the Bayesian framework. It provides MAP point estimates via EM algorithm and posterior MCMC simulations via Gibbs Sampling. It also fits MLE as a special case of the noninformative Bayesian analysis with vague priors. In addition to inferential techniques, the package assists other fundamental phases of a model-based analysis for partial rankings/orderings, by including functions for data manipulation, simulation, descriptive summary, model selection and goodness-of-fit evaluation. Main references on the methods are Mollica and Tardella (2017) <doi.org/10.1007/s11336-016-9530-0> and Mollica and Tardella (2014) <doi/10.1002/sim.6224>.
Maintained by Cristina Mollica. Last updated 4 years ago.
3.6 match 3.15 score 28 scripts
briencj
imageData:Aids in Processing and Plotting Data from a Lemna-Tec Scananalyzer
Note that 'imageData' has been superseded by 'growthPheno'. The package 'growthPheno' incorporates all the functionality of 'imageData' and has functionality not available in 'imageData', but some 'imageData' functions have been renamed. The 'imageData' package is no longer maintained, but is retained for legacy purposes.
Maintained by Chris Brien. Last updated 2 years ago.
3.5 match 3.19 score 39 scripts
taniguchinaro
phenolocrop:Time-Series Models to the Crop Phenology
Fit a time-series model to a crop phenology data, such as time-series rice canopy height. This package returns the model parameters as the summary statistics of crop phenology, and these parameters will be useful to characterize the growth pattern of each cultivar and predict manually-measured traits, such as days to heading and biomass. Please see Taniguchi et al. (2022) <doi:10.3389/fpls.2022.998803> for detail. This package has been designed for scientific use. Use for commercial purposes shall not be allowed.
Maintained by Shoji Taniguchi. Last updated 2 years ago.
4.1 match 1 stars 2.70 score 7 scripts
henrikbengtsson
startup:Friendly R Startup Configuration
Adds support for R startup configuration via '.Renviron.d' and '.Rprofile.d' directories in addition to '.Renviron' and '.Rprofile' files. This makes it possible to keep private / secret environment variables separate from other environment variables. It also makes it easier to share specific startup settings by simply copying a file to a directory.
Maintained by Henrik Bengtsson. Last updated 3 months ago.
configurationenvironment-variablesstartuputility
1.7 match 166 stars 6.54 score 16 scriptscran
support.CEs:Basic Functions for Supporting an Implementation of Choice Experiments
Provides basic functions that support an implementation of (discrete) choice experiments (CEs). CEs is a question-based survey method measuring people's preferences for goods/services and their characteristics. Refer to Louviere et al. (2000) <doi:10.1017/CBO9780511753831> for details on CEs, and Aizaki (2012) <doi:10.18637/jss.v050.c02> for the package.
Maintained by Hideo Aizaki. Last updated 1 years ago.
3.4 match 3 stars 3.06 score 4 dependents
haydarde
dLagM:Time Series Regression Models with Distributed Lag Models
Provides time series regression models with one predictor using finite distributed lag models, polynomial (Almon) distributed lag models, geometric distributed lag models with Koyck transformation, and autoregressive distributed lag models. It also consists of functions for computation of h-step ahead forecasts from these models. See Demirhan (2020)(<doi:10.1371/journal.pone.0228812>) and Baltagi (2011)(<doi:10.1007/978-3-642-20059-5>) for more information.
Maintained by Haydar Demirhan. Last updated 1 years ago.
3.3 match 2 stars 3.18 score 127 scriptscran
shotGroups:Analyze Shot Group Data
Analyzes shooting data with respect to group shape, precision, and accuracy. This includes graphical methods, descriptive statistics, and inference tests using standard, but also non-parametric and robust statistical methods. Implements distributions for radial error in bivariate normal variables. Works with files exported by 'OnTarget PC/TDS', 'Silver Mountain' e-target, 'ShotMarker' e-target, or 'Taran', as well as with custom data files in text format. Supports inference from range statistics such as extreme spread. Includes a set of web-based graphical user interfaces.
Maintained by Daniel Wollschlaeger. Last updated 2 years ago.
4.0 match 2.48 score 1 dependents
corneliusfritz
bigergm:Fit, Simulate, and Diagnose Hierarchical Exponential-Family Models for Big Networks
A toolbox for analyzing and simulating large networks based on hierarchical exponential-family random graph models (HERGMs).'bigergm' implements the estimation for large networks efficiently building on the 'lighthergm' and 'hergm' packages. Moreover, the package contains tools for simulating networks with local dependence to assess the goodness-of-fit.
Maintained by Cornelius Fritz. Last updated 20 days ago.
3.6 match 2.60 score 4 scripts
ekstroem
isdals:Datasets for Introduction to Statistical Data Analysis for the Life Sciences
Provides datasets for the book "Introduction to Statistical Data Analysis for the Life Sciences, Second edition" by Ekstrøm and Sørensen (2014).
Maintained by Claus Ekstrom. Last updated 2 years ago.
3.5 match 2.51 score 108 scripts 1 dependentsbioc
DeMixT:Cell type-specific deconvolution of heterogeneous tumor samples with two or three components using expression data from RNAseq or microarray platforms
DeMixT is a software package that performs deconvolution on transcriptome data from a mixture of two or three components.
Maintained by Ruonan Li. Last updated 5 months ago.
softwarestatisticalmethodclassificationgeneexpressionsequencingmicroarraytissuemicroarraycoveragecppopenmp
1.6 match 5.27 score 25 scripts
speakeasy-2
speakeasyR:Fast and Robust Multi-Scale Graph Clustering
A graph community detection algorithm that aims to be performant on large graphs and robust, returning consistent results across runs. SpeakEasy 2 (SE2), the underlying algorithm, is described in Chris Gaiteri, David R. Connell & Faraz A. Sultan et al. (2023) <doi:10.1186/s13059-023-03062-0>. The core algorithm is written in 'C', providing speed and keeping the memory requirements low. This implementation can take advantage of multiple computing cores without increasing memory usage. SE2 can detect community structure across scales, making it a good choice for biological data, which often has hierarchical structure. Graphs can be passed to the algorithm as adjacency matrices using base 'R' matrices, the 'Matrix' library, 'igraph' graphs, or any data that can be coerced into a matrix.
Maintained by David Connell. Last updated 5 months ago.
1.5 match 3 stars 5.45 score 1 scripts
sujit-sahu
ipsRdbs:Introduction to Probability, Statistics and R for Data-Based Sciences
Contains data sets, programmes and illustrations discussed in the book, "Introduction to Probability, Statistics and R: Foundations for Data-Based Sciences." Sahu (2024, isbn:9783031378645) describes the methods in detail.
Maintained by Sujit K. Sahu. Last updated 11 months ago.
2.1 match 1 stars 3.70 score 2 scripts
cran
support.BWS:Tools for Case 1 Best-Worst Scaling
Provides basic functions that support an implementation of object case (Case 1) best-worst scaling: a function for converting a two-level orthogonal main-effect design/balanced incomplete block design into questions; two functions for creating a data set suitable for analysis; a function for calculating count-based scores; a function for calculating shares of preference; and a function for generating artificial responses to questions. See Louviere et al. (2015) <doi:10.1017/CBO9781107337855> for details on best-worst scaling, and Aizaki and Fogarty (2023) <doi:10.1016/j.jocm.2022.100394> for the package.
Maintained by Hideo Aizaki. Last updated 2 years ago.
3.8 match 1 stars 1.95 score 1 dependentscran
ricegeneann:Gene Annotation of Rice (Oryza Sativa L.spp.japonica)
Gene annotation of rice (Oryza Sativa L.spp.japonica). The package is based on the annotation file from the website <http://plants.ensembl.org/Oryza_sativa/Info/Index>. Input gene's name then return some information, including the from position, the end position, the position type and the chromosome number.
Maintained by Xiang LI. Last updated 5 years ago.
6.9 match 1.00 score
jrvanderdoes
funkycells:Functional Data Analysis for Multiplexed Cell Images
Compare variables of interest between (potentially large numbers of) spatial interactions and meta-variables. Spatial variables are summarized using K, or other, functions, and projected for use in a modified random forest model. The model allows comparison of functional and non-functional variables to each other and to noise, giving statistical significance to the results. Included are preparation, modeling, and interpreting tools along with example datasets, as described in VanderDoes et al., (2023) <doi:10.1101/2023.07.18.549619>.
Maintained by Jeremy VanderDoes. Last updated 2 years ago.
1.6 match 1 stars 4.44 score 11 scripts
samarendra88
dhga:Differential Hub Gene Analysis
Identification of hub genes in a gene co-expression network from gene expression data. The differential network analysis for two contrasting conditions leads to the identification of various types of hubs like Housekeeping, Unique to stress (Disease) and Unique to control (Normal) hub genes.
Maintained by Samarendra Das. Last updated 9 years ago.
6.9 match 1.00 score 10 scripts
nategarton13
bulletcp:Automatic Groove Identification via Bayesian Changepoint Detection
Provides functionality to automatically detect groove locations via a Bayesian changepoint detection method to be used in the data preprocessing step of forensic bullet matching algorithms. The methods in this package are based on those in Stephens (1994) <doi:10.2307/2986119>. Bayesian changepoint detection will simply be an option in the function from the package 'bulletxtrctr' which identifies the groove locations.
Maintained by Nathaniel Garton. Last updated 5 years ago.
1.7 match 3.90 score 16 scripts
ktabelow
neuRosim:Simulate fMRI Data
Generates functional Magnetic Resonance Imaging (fMRI) time series or 4D data. Some high-level functions are created for fast data generation with only a few arguments and a diversity of functions to define activation and noise. For more advanced users it is possible to use the low-level functions and manipulate the arguments. See Welvaert et al. (2011) <doi:10.18637/jss.v044.i10>.
Maintained by Karsten Tabelow. Last updated 1 years ago.
2.3 match 2.93 score 143 scripts
nitzzzzzzz
IGST:Informative Gene Selection Tool
Mining informative genes with certain biological meanings are important for clinical diagnosis of disease and discovery of disease mechanisms in plants and animals. This process involves identification of relevant genes and removal of redundant genes as much as possible from a whole gene set. This package selects the informative genes related to a specific trait using gene expression dataset. These trait specific genes are considered as informative genes. This package returns the informative gene set from the high dimensional gene expression data using a combination of methods SVM and MRMR (for feature selection) with bootstrapping procedure.
Maintained by Nitesh Kumar Sharma. Last updated 5 years ago.
3.5 match 1.85 score 6 scripts
prabhanjan-tattar
gpk:100 Data Sets for Statistics Education
Collection of datasets as prepared by Profs. A.P. Gore, S.A. Paranjape, and M.B. Kulkarni of Department of Statistics, Poona University, India. With their permission, first letter of their names forms the name of this package, the package has been built by me and made available for the benefit of R users. This collection requires a rich class of models and can be a very useful building block for a beginner.
Maintained by Prabhanjan Tattar. Last updated 12 years ago.
3.8 match 1.69 score 49 scriptssamarendra88
BootMRMR:Bootstrap-MRMR Technique for Informative Gene Selection
Selection of informative features like genes, transcripts, RNA seq, etc. using Bootstrap Maximum Relevance and Minimum Redundancy technique from a given high dimensional genomic dataset. Informative gene selection involves identification of relevant genes and removal of redundant genes as much as possible from a large gene space. Main applications in high-dimensional expression data analysis (e.g. microarray data, NGS expression data and other genomics and proteomics applications).
Maintained by Samarendra Das. Last updated 9 years ago.
3.5 match 1.65 score 15 scripts 1 dependents
gmestrem
fdaACF:Autocorrelation Function for Functional Time Series
Quantify the serial correlation across lags of a given functional time series using the autocorrelation function and a partial autocorrelation function for functional time series proposed in Mestre et al. (2021) <doi:10.1016/j.csda.2020.107108>. The autocorrelation functions are based on the L2 norm of the lagged covariance operators of the series. Functions are available for estimating the distribution of the autocorrelation functions under the assumption of strong functional white noise.
Maintained by Guillermo Mestre Marcos. Last updated 4 years ago.
1.6 match 8 stars 3.64 score 11 scripts
jeffreyblewis
wnominate:Roll Call Analysis Software
Estimates Poole and Rosenthal's (1985 <doi:10.2307/2111172>, 1991 <doi:10.2307/2111445>) W-NOMINATE scores from roll call votes supplied though a 'rollcall' object from the 'pscl' package.
Maintained by Jeffrey B. Lewis. Last updated 9 months ago.
1.6 match 3.37 score 78 scripts 1 dependents
yixuan
rARPACK:Solvers for Large Scale Eigenvalue and SVD Problems
Previously an R wrapper of the 'ARPACK' library <http://www.caam.rice.edu/software/ARPACK/>, and now a shell of the R package 'RSpectra', an R interface to the 'Spectra' library <http://yixuan.cos.name/spectra/> for solving large scale eigenvalue/vector problems. The current version of 'rARPACK' simply imports and exports the functions provided by 'RSpectra'. New users of 'rARPACK' are advised to switch to the 'RSpectra' package.
Maintained by Yixuan Qiu. Last updated 9 years ago.
0.5 match 45 stars 9.16 score 177 scripts 51 dependents
fbrun-acta
ZeBook:Working with Dynamic Models for Agriculture and Environment
R package accompanying the book Working with dynamic models for agriculture and environment, by Daniel Wallach (INRA), David Makowski (INRA), James W. Jones (U.of Florida), Francois Brun (ACTA). 3rd edition 2018-09-27.
Maintained by Francois Brun. Last updated 6 years ago.
1.8 match 4 stars 2.37 score 59 scriptscran
AUtests:Approximate Unconditional and Permutation Tests
Performs approximate unconditional and permutation testing for 2x2 contingency tables. Motivated by testing for disease association with rare genetic variants in case-control studies. When variants are extremely rare, these tests give better control of Type I error than standard tests.
Maintained by Arjun Sondhi. Last updated 5 years ago.
1.9 match 2.00 score
ntguardian
CPAT:Change Point Analysis Tests
Implements several statistical tests for structural change, specifically the tests featured in Horváth, Rice and Miller (in press): CUSUM (with weighted/trimmed variants), Darling-Erdös, Hidalgo-Seo, Andrews, and the new Rényi-type test.
Maintained by Curtis Miller. Last updated 6 years ago.
0.5 match 11 stars 5.37 score 43 scriptscran
serieslcb:Lower Confidence Bounds for Binomial Series System
Calculate and compare lower confidence bounds for binomial series system reliability. The R 'shiny' application, launched by the function launch_app(), weaves together a workflow of customized simulations and delta coverage calculations to output recommended lower confidence bound methods.
Maintained by Edward Schuberg. Last updated 6 years ago.
2.0 match 1.20 score 16 scripts
ropensci
chromer:Interface to Chromosome Counts Database API
A programmatic interface to the Chromosome Counts Database (<https://taux.evolseq.net/CCDB_web/>), Rice et al. (2014) <doi:10.1111/nph.13191>. This package is part of the 'ROpenSci' suite (<https://ropensci.org>).
Maintained by Karl W Broman. Last updated 12 months ago.
0.5 match 12 stars 4.56 score 4 scripts
ljacquin
KRMM:Kernel Ridge Mixed Model
Solves kernel ridge regression, within the the mixed model framework, for the linear, polynomial, Gaussian, Laplacian and ANOVA kernels. The model components (i.e. fixed and random effects) and variance parameters are estimated using the expectation-maximization (EM) algorithm. All the estimated components and parameters, e.g. BLUP of dual variables and BLUP of random predictor effects for the linear kernel (also known as RR-BLUP), are available. The kernel ridge mixed model (KRMM) is described in Jacquin L, Cao T-V and Ahmadi N (2016) A Unified and Comprehensible View of Parametric and Kernel Methods for Genomic Prediction with Application to Rice. Front. Genet. 7:145. <doi:10.3389/fgene.2016.00145>.
Maintained by Laval Jacquin. Last updated 4 months ago.
blupgblupgenomic-predictionkernel-methodsmixed-modelsvariance-components-estimation
0.5 match 1 stars 4.08 score 27 scripts
kpkeller
eshrink:Shrinkage for Effect Estimation
Computes shrinkage estimators for regression problems. Selects penalty parameter by minimizing bias and variance in the effect estimate, where bias and variance are estimated from the posterior predictive distribution. See Keller and Rice (2017) <doi:10.1093/aje/kwx225> for more details.
Maintained by Joshua Keller. Last updated 5 years ago.
0.5 match 1 stars 2.70 score 1 scripts
omelnikov
QFRM:Pricing of Vanilla and Exotic Option Contracts
Option pricing (financial derivatives) techniques mainly following textbook 'Options, Futures and Other Derivatives', 9ed by John C.Hull, 2014. Prentice Hall. Implementations are via binomial tree option model (BOPM), Black-Scholes model, Monte Carlo simulations, etc. This package is a result of Quantitative Financial Risk Management course (STAT 449 and STAT 649) at Rice University, Houston, TX, USA, taught by Oleg Melnikov, statistics PhD student, as of Spring 2015.
Maintained by Oleg Melnikov. Last updated 10 years ago.
0.5 match 2 stars 2.67 score 47 scripts
cropmodels
phenorice:phenorice
An implementation of phenorice algorithm to detect rice crops from remote sensing data.
Maintained by Robert J. Hijmans. Last updated 5 years ago.
0.6 match 5 stars 2.40 score 2 scriptscran
wwntests:Hypothesis Tests for Functional Time Series
Provides a collection of white noise hypothesis tests for functional time series and related visualizations. These include tests based on the norms of autocovariance operators that are built under both strong and weak white noise assumptions. Additionally, tests based on the spectral density operator and on principal component dimensional reduction are included, which are built under strong white noise assumptions. Also, this package provides goodness-of-fit tests for functional autoregressive of order 1 models. These methods are described in Kokoszka et al. (2017) <doi:10.1016/j.jmva.2017.08.004>, Characiejus and Rice (2019) <doi:10.1016/j.ecosta.2019.01.003>, Gabrys and Kokoszka (2007) <doi:10.1198/016214507000001111>, and Kim et al. (2023) <doi: 10.1214/23-SS143> respectively.
Maintained by Mihyun Kim. Last updated 1 years ago.
0.5 match 2 stars 2.30 score 3 scripts
nirmalaruban
geneNR:Automated Gene Identification for Post-GWAS Analysis
Facilitates the post-Genome Wide Association Studies (GWAS) analysis of identifying candidate genes within user-defined search window, based on the identified Single Nucleotide Polymorphisms (SNPs) as given by Mazumder AK (2024) <doi:10.1038/s41598-024-66903-3>. It supports candidate gene analysis for wheat and rice. Just import your GWAS result as explained in the sample_data file and the function does all the manual search and retrieve candidate genes for you, while exporting the results into ready-to-use output.
Maintained by Rajamani Nirmalaruban. Last updated 6 days ago.
0.5 match 2.00 scorecran
SphereOptimize:Optimization on a Unit Sphere
A simple tool for numerical optimization on the unit sphere. This is achieved by combining the spherical coordinating system with L-BFGS-B optimization. This algorithm is implemented in Kolkiewicz, A., Rice, G., & Xie, Y. (2020) <doi:10.1016/j.jspi.2020.07.001>.
Maintained by Yijun Xie. Last updated 5 years ago.
0.5 match 1.70 score
