Showing 53 of total 53 results (show query)
nandp1
gpbStat:Comprehensive Statistical Analysis of Plant Breeding Experiments
Performs statistical data analysis of various Plant Breeding experiments. Contains functions for Line by Tester analysis as per Arunachalam, V.(1974) <http://repository.ias.ac.in/89299/> and Diallel analysis as per Griffing, B. (1956) <https://www.publish.csiro.au/bi/pdf/BI9560463>.
Maintained by Nandan Patil. Last updated 4 months ago.
biometricsgeneticsplantbreeding
10.0 match 3 stars 6.08 score 27 scripts
wlenhard
cNORM:Continuous Norming
A comprehensive toolkit for generating continuous test norms in psychometrics and biometrics, and analyzing model fit. The package offers both distribution-free modeling using Taylor polynomials and parametric modeling using the beta-binomial distribution. Originally developed for achievement tests, it is applicable to a wide range of mental, physical, or other test scores dependent on continuous or discrete explanatory variables. The package provides several advantages: It minimizes deviations from representativeness in subsamples, interpolates between discrete levels of explanatory variables, and significantly reduces the required sample size compared to conventional norming per age group. cNORM enables graphical and analytical evaluation of model fit, accommodates a wide range of scales including those with negative and descending values, and even supports conventional norming. It generates norm tables including confidence intervals. It also includes methods for addressing representativeness issues through Iterative Proportional Fitting.
Maintained by Wolfgang Lenhard. Last updated 4 months ago.
beta-binomialbiometricscontinuous-norminggrowth-curvenorm-scoresnorm-tablesnormalization-techniquespercentilepsychometricsregression-based-normingtaylor-series
10.5 match 2 stars 5.49 score 75 scripts
openpharma
DoseFinding:Planning and Analyzing Dose Finding Experiments
The DoseFinding package provides functions for the design and analysis of dose-finding experiments (with focus on pharmaceutical Phase II clinical trials). It provides functions for: multiple contrast tests, fitting non-linear dose-response models (using Bayesian and non-Bayesian estimation), calculating optimal designs and an implementation of the MCPMod methodology (Pinheiro et al. (2014) <doi:10.1002/sim.6052>).
Maintained by Marius Thomas. Last updated 5 days ago.
3.8 match 8 stars 10.32 score 98 scripts 10 dependents
r-computing-lab
discord:Functions for Discordant Kinship Modeling
Functions for discordant kinship modeling (and other sibling-based quasi-experimental designs). Currently, the package contains data restructuring functions and functions for generating biometrically informed data for kin pairs.
Maintained by S. Mason Garrison. Last updated 1 years ago.
4.1 match 4.83 score 34 scripts
bioc
scMerge:scMerge: Merging multiple batches of scRNA-seq data
Like all gene expression data, single-cell data suffers from batch effects and other unwanted variations that makes accurate biological interpretations difficult. The scMerge method leverages factor analysis, stably expressed genes (SEGs) and (pseudo-) replicates to remove unwanted variations and merge multiple single-cell data. This package contains all the necessary functions in the scMerge pipeline, including the identification of SEGs, replication-identification methods, and merging of single-cell data.
Maintained by Yingxin Lin. Last updated 5 months ago.
batcheffectgeneexpressionnormalizationrnaseqsequencingsinglecellsoftwaretranscriptomicsbioinformaticssingle-cell
1.6 match 67 stars 9.52 score 137 scripts 1 dependents
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.
1.3 match 125 stars 11.02 score 1.7k scripts 2 dependents
mhunter1
EasyMx:Easy Model-Builder Functions for 'OpenMx'
Utilities for building certain kinds of common matrices and models in the extended structural equation modeling package, 'OpenMx'.
Maintained by Michael D. Hunter. Last updated 2 years ago.
5.3 match 2.32 score 21 scripts
danniyugithub
BEACH:Biometric Exploratory Analysis Creation House
A platform is provided for interactive analyses with a goal of totally easy to develop, deploy, interact, and explore (TEDDIE). Using this package, users can create customized analyses and make them available to end users who can perform interactive analyses and save analyses to RTF or HTML files. It allows developers to focus on R code for analysis, instead of dealing with html or shiny code.
Maintained by Danni Yu. Last updated 6 years ago.
5.7 match 1 stars 2.00 score 3 scripts
laurimeh
lmfor:Functions for Forest Biometrics
Functions for different purposes related to forest biometrics, including illustrative graphics, numerical computation, modeling height-diameter relationships, prediction of tree volumes, modelling of diameter distributions and estimation off stand density using ITD. Several empirical datasets are also included.
Maintained by Lauri Mehtatalo. Last updated 3 years ago.
3.8 match 3 stars 2.42 score 29 scripts 1 dependents
cran
MCPMod:Design and Analysis of Dose-Finding Studies
Implements a methodology for the design and analysis of dose-response studies that combines aspects of multiple comparison procedures and modeling approaches (Bretz, Pinheiro and Branson, 2005, Biometrics 61, 738-748, <doi: 10.1111/j.1541-0420.2005.00344.x>). The package provides tools for the analysis of dose finding trials as well as a variety of tools necessary to plan a trial to be conducted with the MCP-Mod methodology. Please note: The 'MCPMod' package will not be further developed, all future development of the MCP-Mod methodology will be done in the 'DoseFinding' R-package.
Maintained by Bjoern Bornkamp. Last updated 5 years ago.
4.3 match 1.60 score
mlampros
OpenImageR:An Image Processing Toolkit
Incorporates functions for image preprocessing, filtering and image recognition. The package takes advantage of 'RcppArmadillo' to speed up computationally intensive functions. The histogram of oriented gradients descriptor is a modification of the 'findHOGFeatures' function of the 'SimpleCV' computer vision platform, the average_hash(), dhash() and phash() functions are based on the 'ImageHash' python library. The Gabor Feature Extraction functions are based on 'Matlab' code of the paper, "CloudID: Trustworthy cloud-based and cross-enterprise biometric identification" by M. Haghighat, S. Zonouz, M. Abdel-Mottaleb, Expert Systems with Applications, vol. 42, no. 21, pp. 7905-7916, 2015, <doi:10.1016/j.eswa.2015.06.025>. The 'SLIC' and 'SLICO' superpixel algorithms were explained in detail in (i) "SLIC Superpixels Compared to State-of-the-art Superpixel Methods", Radhakrishna Achanta, Appu Shaji, Kevin Smith, Aurelien Lucchi, Pascal Fua, and Sabine Suesstrunk, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 34, num. 11, p. 2274-2282, May 2012, <doi:10.1109/TPAMI.2012.120> and (ii) "SLIC Superpixels", Radhakrishna Achanta, Appu Shaji, Kevin Smith, Aurelien Lucchi, Pascal Fua, and Sabine Suesstrunk, EPFL Technical Report no. 149300, June 2010.
Maintained by Lampros Mouselimis. Last updated 2 years ago.
filteringgabor-feature-extractiongabor-filtershog-featuresimageimage-hashingprocessingrcpparmadillorecognitionslicslicosuperpixelsopenblascppopenmp
0.5 match 60 stars 9.86 score 358 scripts 8 dependents
cran
coxme:Mixed Effects Cox Models
Fit Cox proportional hazards models containing both fixed and random effects. The random effects can have a general form, of which familial interactions (a "kinship" matrix) is a particular special case. Note that the simplest case of a mixed effects Cox model, i.e. a single random per-group intercept, is also called a "frailty" model. The approach is based on Ripatti and Palmgren, Biometrics 2002.
Maintained by Terry M. Therneau. Last updated 7 months ago.
0.5 match 2 stars 8.78 score 562 scripts 15 dependents
bdwilliamson
vimp:Perform Inference on Algorithm-Agnostic Variable Importance
Calculate point estimates of and valid confidence intervals for nonparametric, algorithm-agnostic variable importance measures in high and low dimensions, using flexible estimators of the underlying regression functions. For more information about the methods, please see Williamson et al. (Biometrics, 2020), Williamson et al. (JASA, 2021), and Williamson and Feng (ICML, 2020).
Maintained by Brian D. Williamson. Last updated 1 months ago.
machine-learningnonparametric-statisticsstatistical-inferencevariable-importance
0.5 match 23 stars 6.79 score 67 scripts
paramita-sc
survivalROC:Time-Dependent ROC Curve Estimation from Censored Survival Data
Compute time-dependent ROC curve from censored survival data using Kaplan-Meier (KM) or Nearest Neighbor Estimation (NNE) method of Heagerty, Lumley & Pepe (Biometrics, Vol 56 No 2, 2000, PP 337-344).
Maintained by Paramita Saha-Chaudhuri. Last updated 2 years ago.
0.5 match 6 stars 6.37 score 266 scripts 16 dependents
ocbe-uio
psbcSpeedUp:Penalized Semiparametric Bayesian Cox Models
Algorithms to speed up the Bayesian Lasso Cox model (Lee et al., Int J Biostat, 2011 <doi:10.2202/1557-4679.1301>) and the Bayesian Lasso Cox with mandatory variables (Zucknick et al. Biometrical J, 2015 <doi:10.1002/bimj.201400160>).
Maintained by Zhi Zhao. Last updated 9 months ago.
bayesian-cox-modelsomics-datasurvival-analysisopenblascppopenmp
0.5 match 3 stars 4.65 score
sigbertklinke
andrews:Various Andrews Curves
Visualisation of multidimensional data through different Andrews curves: Andrews, D. F. (1972) Plots of High-Dimensional Data. Biometrics, 28(1), 125-136. <doi:10.2307/2528964>.
Maintained by Sigbert Klinke. Last updated 1 years ago.
0.5 match 1 stars 4.00 score 20 scripts
paramita-sc
risksetROC:Riskset ROC Curve Estimation from Censored Survival Data
Compute time-dependent Incident/dynamic accuracy measures (ROC curve, AUC, integrated AUC )from censored survival data under proportional or non-proportional hazard assumption of Heagerty & Zheng (Biometrics, Vol 61 No 1, 2005, PP 92-105).
Maintained by Paramita Saha-Chaudhuri. Last updated 3 years ago.
0.5 match 3.71 score 57 scripts 3 dependents
svetlanaeden
survSpearman:Nonparametric Spearman's Correlation for Survival Data
Nonparametric estimation of Spearman's rank correlation with bivariate survival (right-censored) data as described in Eden, S.K., Li, C., Shepherd B.E. (2021), Nonparametric Estimation of Spearman's Rank Correlation with Bivariate Survival Data, Biometrics (under revision). The package also provides functions that visualize bivariate survival data and bivariate probability mass function.
Maintained by Svetlana Eden. Last updated 2 years ago.
0.5 match 3.70 score 4 scripts
wzhang17
lchemix:A Bayesian Multi-Dimensional Couple-Based Latent Risk Model
A joint latent class model where a hierarchical structure exists, with an interaction between female and male partners of a couple. A Bayesian perspective to inference and Markov chain Monte Carlo algorithms to obtain posterior estimates of model parameters. The reference paper is: Beom Seuk Hwang, Zhen Chen, Germaine M.Buck Louis, Paul S. Albert, (2018) "A Bayesian multi-dimensional couple-based latent risk model with an application to infertility". Biometrics, 75, 315-325. <doi:10.1111/biom.12972>.
Maintained by Weimin Zhang. Last updated 5 years ago.
0.5 match 3.70 score 2 scriptscran
ExpImage:Analysis of Images in Experiments
Tools created for image analysis in researches. There are functions associated with image editing, segmentation, and obtaining biometric measurements (Este pacote foi idealizado para para a analise de imagens em pesquisas. Ha funcoes associadas a edicao de imagens, segmentacao, e obtencao de medidas biometricas) <https://www.expstat.com/pacotes-do-r/expimage>.
Maintained by Alcinei Mistico Azevedo. Last updated 10 months ago.
0.5 match 3.08 scorewzhang17
sorocs:A Bayesian Semiparametric Approach to Correlated ROC Surfaces
A Bayesian semiparametric Dirichlet process mixtures to estimate correlated receiver operating characteristic (ROC) surfaces and the associated volume under the surface (VUS) with stochastic order constraints. The reference paper is:Zhen Chen, Beom Seuk Hwang, (2018) "A Bayesian semiparametric approach to correlated ROC surfaces with stochastic order constraints". Biometrics, 75, 539-550. <doi:10.1111/biom.12997>.
Maintained by Weimin Zhang. Last updated 5 years ago.
0.5 match 3.00 score 2 scriptscran
sensitivitymv:Sensitivity Analysis in Observational Studies
The package performs a sensitivity analysis in an observational study using an M-statistic, for instance, the mean. The main function in the package is senmv(), but amplify() and truncatedP() are also useful. The method is developed in Rosenbaum Biometrics, 2007, 63, 456-464, <doi:10.1111/j.1541-0420.2006.00717.x>.
Maintained by Paul R. Rosenbaum. Last updated 7 years ago.
0.5 match 2.86 score 60 scripts 4 dependents
shug0131
mreg:Fits Regression Models When the Outcome is Partially Missing
Implements the methods described in Bond S, Farewell V, 2006, Exact Likelihood Estimation for a Negative Binomial Regression Model with Missing Outcomes, Biometrics.
Maintained by Simon Bond. Last updated 1 years ago.
0.5 match 2.70 score 6 scripts
yunanwu123
DTRKernSmooth:Estimate and Make Inference About Optimal Treatment Regimes via Smoothed Methods
Methods to estimate the optimal treatment regime among all linear regimes via smoothed estimation methods, and construct element-wise confidence intervals for the optimal linear treatment regime vector, as well as the confidence interval for the optimal value via wild bootstrap procedures, if the population follows treatments recommended by the optimal linear regime. See more details in: Wu, Y. and Wang, L. (2021), "Resampling-based Confidence Intervals for Model-free Robust Inference on Optimal Treatment Regimes", Biometrics, 77: 465– 476, <doi:10.1111/biom.13337>.
Maintained by Yunan Wu. Last updated 1 years ago.
0.5 match 2.70 score
xss55
GLMMselect:Bayesian Model Selection for Generalized Linear Mixed Models
A Bayesian model selection approach for generalized linear mixed models. Currently, 'GLMMselect' can be used for Poisson GLMM and Bernoulli GLMM. 'GLMMselect' can select fixed effects and random effects simultaneously. Covariance structures for the random effects are a product of a unknown scalar and a known semi-positive definite matrix. 'GLMMselect' can be widely used in areas such as longitudinal studies, genome-wide association studies, and spatial statistics. 'GLMMselect' is based on Xu, Ferreira, Porter, and Franck (202X), Bayesian Model Selection Method for Generalized Linear Mixed Models, Biometrics, under review.
Maintained by Shuangshuang Xu. Last updated 2 years ago.
0.5 match 2.70 score 9 scripts
laylaparast
landpred:Landmark Prediction of a Survival Outcome
Provides functions for landmark prediction of a survival outcome incorporating covariate and short-term event information. For more information about landmark prediction please see: Parast, Layla, Su-Chun Cheng, and Tianxi Cai. Incorporating short-term outcome information to predict long-term survival with discrete markers. Biometrical Journal 53.2 (2011): 294-307, <doi:10.1002/bimj.201000150>.
Maintained by Layla Parast. Last updated 2 years ago.
0.5 match 1.82 score 11 scripts 2 dependentscran
fugue:Sensitivity Analysis Optimized for Matched Sets of Varied Sizes
As in music, a fugue statistic repeats a theme in small variations. Here, the psi-function that defines an m-statistic is slightly altered to maintain the same design sensitivity in matched sets of different sizes. The main functions in the package are sen() and senCI(). For sensitivity analyses for m-statistics, see Rosenbaum (2007) Biometrics 63 456-464 <doi:10.1111/j.1541-0420.2006.00717.x>.
Maintained by Paul R. Rosenbaum. Last updated 6 years ago.
0.5 match 1.70 score
cran
sensitivityfull:Sensitivity Analysis for Full Matching in Observational Studies
Sensitivity to unmeasured biases in an observational study that is a full match. Function senfm() performs tests and function senfmCI() creates confidence intervals. The method uses Huber's M-statistics, including least squares, and is described in Rosenbaum (2007, Biometrics) <DOI:10.1111/j.1541-0420.2006.00717.x>.
Maintained by Paul R. Rosenbaum. Last updated 8 years ago.
0.5 match 1.48 score 1 dependents
cran
gsrsb:Group Sequential Refined Secondary Boundary
A gate-keeping procedure to test a primary and a secondary endpoint in a group sequential design with multiple interim looks. Computations related to group sequential primary and secondary boundaries. Refined secondary boundaries are calculated for a gate-keeping test on a primary and a secondary endpoint in a group sequential design with multiple interim looks. The choices include both the standard boundaries and the boundaries using error spending functions. See Tamhane et al. (2018), "A gatekeeping procedure to test a primary and a secondary endpoint in a group sequential design with multiple interim looks", Biometrics, 74(1), 40-48.
Maintained by Jiangtao Gou. Last updated 2 years ago.
0.5 match 1.18 score 15 scripts
laylaparast
SurrogateTest:Early Testing for a Treatment Effect using Surrogate Marker Information
Provides functions to test for a treatment effect in terms of the difference in survival between a treatment group and a control group using surrogate marker information obtained at some early time point in a time-to-event outcome setting. Nonparametric kernel estimation is used to estimate the test statistic and perturbation resampling is used for variance estimation. More details will be available in the future in: Parast L, Cai T, Tian L (2019) ``Using a Surrogate Marker for Early Testing of a Treatment Effect" Biometrics, 75(4):1253-1263. <doi:10.1111/biom.13067>.
Maintained by Layla Parast. Last updated 3 years ago.
0.5 match 1.04 score 11 scripts
francescobartolucci
LCCR:Latent Class Capture-Recapture Models
Estimation of latent class models with individual covariates for capture-recapture data. See Bartolucci, F. and Forcina, A. (2022), Estimating the size of a closed population by modeling latent and observed heterogeneity, Biometrics, 80(2), ujae017.
Maintained by Francesco Bartolucci. Last updated 4 months ago.
0.5 match 1.00 score
rvaradhan
DSBayes:Bayesian Subgroup Analysis in Clinical Trials
Calculate posterior modes and credible intervals of parameters of the Dixon-Simon model for subgroup analysis (with binary covariates) in clinical trials. For details of the methodology, please refer to D.O. Dixon and R. Simon (1991), Biometrics, 47: 871-881.
Maintained by Ravi Varadhan. Last updated 1 years ago.
0.5 match 1.00 score 1 scriptslaylaparast
hetsurr:Assessing Heterogeneity in the Utility of a Surrogate Marker
Provides a function to assess and test for heterogeneity in the utility of a surrogate marker with respect to a baseline covariate. The main function can be used for either a continuous or discrete baseline covariate. More details will be available in the future in: Parast, L., Cai, T., Tian L (2021). "Testing for Heterogeneity in the Utility of a Surrogate Marker." Biometrics, In press.
Maintained by Layla Parast. Last updated 3 years ago.
0.5 match 1.00 score
kleest0
mBvs:Bayesian Variable Selection Methods for Multivariate Data
Bayesian variable selection methods for data with multivariate responses and multiple covariates. The package contains implementations of multivariate Bayesian variable selection methods for continuous data (Lee et al., Biometrics, 2017 <doi:10.1111/biom.12557>) and zero-inflated count data (Lee et al., Biostatistics, 2020 <doi:10.1093/biostatistics/kxy067>).
Maintained by Kyu Ha Lee. Last updated 11 months ago.
0.5 match 1.00 score 4 scripts
shu-d
ipwCoxCSV:Inverse Probability Weighted Cox Model with Corrected Sandwich Variance
An implementation of corrected sandwich variance (CSV) estimation method for making inference of marginal hazard ratios (HR) in inverse probability weighted (IPW) Cox model without and with clustered data, proposed by Shu, Young, Toh, and Wang (2019) in their paper under revision for Biometrics. Both conventional inverse probability weights and stabilized weights are implemented. Logistic regression model is assumed for propensity score model.
Maintained by Di Shu. Last updated 5 years ago.
0.5 match 1.00 score
