Showing 54 of total 54 results (show query)
braverock
PerformanceAnalytics:Econometric Tools for Performance and Risk Analysis
Collection of econometric functions for performance and risk analysis. In addition to standard risk and performance metrics, this package aims to aid practitioners and researchers in utilizing the latest research in analysis of non-normal return streams. In general, it is most tested on return (rather than price) data on a regular scale, but most functions will work with irregular return data as well, and increasing numbers of functions will work with P&L or price data where possible.
Maintained by Brian G. Peterson. Last updated 3 months ago.
6.4 match 222 stars 15.93 score 4.8k scripts 20 dependents
mlopez-ibanez
eaf:Plots of the Empirical Attainment Function
Computation and visualization of the empirical attainment function (EAF) for the analysis of random sets in multi-criterion optimization. M. López-Ibáñez, L. Paquete, and T. Stützle (2010) <doi:10.1007/978-3-642-02538-9_9>.
Maintained by Manuel López-Ibáñez. Last updated 7 months ago.
eafeaf-differencesepsilonhypervolumeinverted-generational-distancemultiobjective-optimizationsummary-attainment-surfacesvisualizationgsl
13.3 match 17 stars 5.31 score 32 scripts 1 dependents
mrc-ide
EpiEstim:Estimate Time Varying Reproduction Numbers from Epidemic Curves
Tools to quantify transmissibility throughout an epidemic from the analysis of time series of incidence as described in Cori et al. (2013) <doi:10.1093/aje/kwt133> and Wallinga and Teunis (2004) <doi:10.1093/aje/kwh255>.
Maintained by Anne Cori. Last updated 7 months ago.
3.6 match 95 stars 12.00 score 1.0k scripts 7 dependents
r-forge
mlogit:Multinomial Logit Models
Maximum Likelihood estimation of random utility discrete choice models, as described in Kenneth Train (2009) Discrete Choice Methods with Simulations <doi:10.1017/CBO9780511805271>.
Maintained by Yves Croissant. Last updated 5 years ago.
4.1 match 9.81 score 1.2k scripts 14 dependents
eami91
BGPhazard:Markov Beta and Gamma Processes for Modeling Hazard Rates
Computes the hazard rate estimate as described by Nieto-Barajas & Walker (2002), Nieto-Barajas (2003), Nieto-Barajas & Walker (2007) and Nieto-Barajas & Yin (2008).
Maintained by Emilio Akira Morones Ishikawa. Last updated 2 years ago.
8.5 match 1 stars 4.32 score 21 scripts
emmanuelparadis
ape:Analyses of Phylogenetics and Evolution
Functions for reading, writing, plotting, and manipulating phylogenetic trees, analyses of comparative data in a phylogenetic framework, ancestral character analyses, analyses of diversification and macroevolution, computing distances from DNA sequences, reading and writing nucleotide sequences as well as importing from BioConductor, and several tools such as Mantel's test, generalized skyline plots, graphical exploration of phylogenetic data (alex, trex, kronoviz), estimation of absolute evolutionary rates and clock-like trees using mean path lengths and penalized likelihood, dating trees with non-contemporaneous sequences, translating DNA into AA sequences, and assessing sequence alignments. Phylogeny estimation can be done with the NJ, BIONJ, ME, MVR, SDM, and triangle methods, and several methods handling incomplete distance matrices (NJ*, BIONJ*, MVR*, and the corresponding triangle method). Some functions call external applications (PhyML, Clustal, T-Coffee, Muscle) whose results are returned into R.
Maintained by Emmanuel Paradis. Last updated 21 hours ago.
2.0 match 64 stars 17.22 score 13k scripts 599 dependents
eguidotti
calculus:High Dimensional Numerical and Symbolic Calculus
Efficient C++ optimized functions for numerical and symbolic calculus as described in Guidotti (2022) <doi:10.18637/jss.v104.i05>. It includes basic arithmetic, tensor calculus, Einstein summing convention, fast computation of the Levi-Civita symbol and generalized Kronecker delta, Taylor series expansion, multivariate Hermite polynomials, high-order derivatives, ordinary differential equations, differential operators (Gradient, Jacobian, Hessian, Divergence, Curl, Laplacian) and numerical integration in arbitrary orthogonal coordinate systems: cartesian, polar, spherical, cylindrical, parabolic or user defined by custom scale factors.
Maintained by Emanuele Guidotti. Last updated 2 years ago.
calculuscoordinate-systemscurldivergenceeinsteinfinite-differencegradienthermitehessianjacobianlaplaciannumerical-derivationnumerical-derivativesnumerical-differentiationsymbolic-computationsymbolic-differentiationtaylorcpp
3.3 match 47 stars 8.92 score 66 scripts 7 dependents
doomlab
MOTE:Effect Size and Confidence Interval Calculator
Measure of the Effect ('MOTE') is an effect size calculator, including a wide variety of effect sizes in the mean differences family (all versions of d) and the variance overlap family (eta, omega, epsilon, r). 'MOTE' provides non-central confidence intervals for each effect size, relevant test statistics, and output for reporting in APA Style (American Psychological Association, 2010, <ISBN:1433805618>) with 'LaTeX'. In research, an over-reliance on p-values may conceal the fact that a study is under-powered (Halsey, Curran-Everett, Vowler, & Drummond, 2015 <doi:10.1038/nmeth.3288>). A test may be statistically significant, yet practically inconsequential (Fritz, Scherndl, & Kühberger, 2012 <doi:10.1177/0959354312436870>). Although the American Psychological Association has long advocated for the inclusion of effect sizes (Wilkinson & American Psychological Association Task Force on Statistical Inference, 1999 <doi:10.1037/0003-066X.54.8.594>), the vast majority of peer-reviewed, published academic studies stop short of reporting effect sizes and confidence intervals (Cumming, 2013, <doi:10.1177/0956797613504966>). 'MOTE' simplifies the use and interpretation of effect sizes and confidence intervals. For more information, visit <https://www.aggieerin.com/shiny-server>.
Maintained by Erin M. Buchanan. Last updated 3 years ago.
confidenceeffectintervalsizestatistics
4.3 match 17 stars 6.69 score 320 scripts 1 dependents
bioc
ternarynet:Ternary Network Estimation
Gene-regulatory network (GRN) modeling seeks to infer dependencies between genes and thereby provide insight into the regulatory relationships that exist within a cell. This package provides a computational Bayesian approach to GRN estimation from perturbation experiments using a ternary network model, in which gene expression is discretized into one of 3 states: up, unchanged, or down). The ternarynet package includes a parallel implementation of the replica exchange Monte Carlo algorithm for fitting network models, using MPI.
Maintained by McCall N. Matthew. Last updated 5 months ago.
softwarecellbiologygraphandnetworknetworkbayesiancpp
6.3 match 3.30 score 3 scripts
easystats
effectsize:Indices of Effect Size
Provide utilities to work with indices of effect size for a wide variety of models and hypothesis tests (see list of supported models using the function 'insight::supported_models()'), allowing computation of and conversion between indices such as Cohen's d, r, odds, etc. References: Ben-Shachar et al. (2020) <doi:10.21105/joss.02815>.
Maintained by Mattan S. Ben-Shachar. Last updated 1 months ago.
anovacohens-dcomputeconversioncorrelationeffect-sizeeffectsizehacktoberfesthedges-ginterpretationstandardizationstandardizedstatistics
1.3 match 344 stars 16.38 score 1.8k scripts 29 dependents
r4ss
r4ss:R Code for Stock Synthesis
A collection of R functions for use with Stock Synthesis, a fisheries stock assessment modeling platform written in ADMB by Dr. Richard D. Methot at the NOAA Northwest Fisheries Science Center. The functions include tools for summarizing and plotting results, manipulating files, visualizing model parameterizations, and various other common stock assessment tasks. This version of '{r4ss}' is compatible with Stock Synthesis versions 3.24 through 3.30 (specifically version 3.30.23.1, from December 2024). Support for 3.24 models is only through the core functions for reading output and plotting.
Maintained by Ian G. Taylor. Last updated 5 days ago.
fisheriesfisheries-stock-assessmentstock-synthesis
1.6 match 43 stars 11.38 score 1.0k scripts 2 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 1 months ago.
2.3 match 4 stars 8.01 score 2.4k scripts 5 dependents
multi-objective
moocore:Core Mathematical Functions for Multi-Objective Optimization
Fast implementation of mathematical operations and performance metrics for multi-objective optimization, including filtering and ranking of dominated vectors according to Pareto optimality, computation of the empirical attainment function, V.G. da Fonseca, C.M. Fonseca, A.O. Hall (2001) <doi:10.1007/3-540-44719-9_15>, hypervolume metric, C.M. Fonseca, L. Paquete, M. López-Ibáñez (2006) <doi:10.1109/CEC.2006.1688440>, epsilon indicator, inverted generational distance, and Vorob'ev threshold, expectation and deviation, M. Binois, D. Ginsbourger, O. Roustant (2015) <doi:10.1016/j.ejor.2014.07.032>, among others.
Maintained by Manuel López-Ibáñez. Last updated 6 days ago.
2.8 match 11 stars 6.27 score 7 scripts 4 dependents
dcousin3
superb:Summary Plots with Adjusted Error Bars
Computes standard error and confidence interval of various descriptive statistics under various designs and sampling schemes. The main function, superb(), return a plot. It can also be used to obtain a dataframe with the statistics and their precision intervals so that other plotting environments (e.g., Excel) can be used. See Cousineau and colleagues (2021) <doi:10.1177/25152459211035109> or Cousineau (2017) <doi:10.5709/acp-0214-z> for a review as well as Cousineau (2005) <doi:10.20982/tqmp.01.1.p042>, Morey (2008) <doi:10.20982/tqmp.04.2.p061>, Baguley (2012) <doi:10.3758/s13428-011-0123-7>, Cousineau & Laurencelle (2016) <doi:10.1037/met0000055>, Cousineau & O'Brien (2014) <doi:10.3758/s13428-013-0441-z>, Calderini & Harding <doi:10.20982/tqmp.15.1.p001> for specific references.
Maintained by Denis Cousineau. Last updated 2 months ago.
error-barsplottingstatisticssummary-plotssummary-statisticsvisualization
1.8 match 19 stars 9.55 score 155 scripts 2 dependents
kornl
gMCP:Graph Based Multiple Comparison Procedures
Functions and a graphical user interface for graphical described multiple test procedures.
Maintained by Kornelius Rohmeyer. Last updated 12 months ago.
2.3 match 10 stars 7.28 score 105 scripts 2 dependents
jlmelville
rnndescent:Nearest Neighbor Descent Method for Approximate Nearest Neighbors
The Nearest Neighbor Descent method for finding approximate nearest neighbors by Dong and co-workers (2010) <doi:10.1145/1963405.1963487>. Based on the 'Python' package 'PyNNDescent' <https://github.com/lmcinnes/pynndescent>.
Maintained by James Melville. Last updated 8 months ago.
approximate-nearest-neighbor-searchcpp
2.2 match 11 stars 7.31 score 75 scripts
davidbajnai
isogeochem:Tools for Stable Isotope Geochemistry
This toolbox makes working with oxygen, carbon, and clumped isotope data reproducible and straightforward. Use it to quickly calculate isotope fractionation factors, and apply paleothermometry equations.
Maintained by David Bajnai. Last updated 2 years ago.
carbonateclumpedgeochemistrygeologyisotope
3.3 match 7 stars 4.85 score 1 scripts
brubinstein
diffpriv:Easy Differential Privacy
An implementation of major general-purpose mechanisms for privatizing statistics, models, and machine learners, within the framework of differential privacy of Dwork et al. (2006) <doi:10.1007/11681878_14>. Example mechanisms include the Laplace mechanism for releasing numeric aggregates, and the exponential mechanism for releasing set elements. A sensitivity sampler (Rubinstein & Alda, 2017) <arXiv:1706.02562> permits sampling target non-private function sensitivity; combined with the generic mechanisms, it permits turn-key privatization of arbitrary programs.
Maintained by Benjamin Rubinstein. Last updated 3 years ago.
data-sciencedifferential-privacydiffprivmachine-learningstatistics
2.0 match 66 stars 6.54 score 52 scripts
merck
gMCPLite:Lightweight Graph Based Multiple Comparison Procedures
A lightweight fork of 'gMCP' with functions for graphical described multiple test procedures introduced in Bretz et al. (2009) <doi:10.1002/sim.3495> and Bretz et al. (2011) <doi:10.1002/bimj.201000239>. Implements a flexible function using 'ggplot2' to create multiplicity graph visualizations. Contains instructions of multiplicity graph and graphical testing for group sequential design, described in Maurer and Bretz (2013) <doi:10.1080/19466315.2013.807748>, with necessary unit testing using 'testthat'.
Maintained by Nan Xiao. Last updated 1 years ago.
2.3 match 11 stars 5.79 score 14 scripts
marsicofl
mispitools:Missing Person Identification Tools
An open source software package written in R statistical language. It consists of a set of decision-making tools to conduct missing person searches. Particularly, it allows computing optimal LR threshold for declaring potential matches in DNA-based database search. More recently 'mispitools' incorporates preliminary investigation data based LRs. Statistical weight of different traces of evidence such as biological sex, age and hair color are presented. For citing mispitools please use the following references: Marsico and Caridi, 2023 <doi:10.1016/j.fsigen.2023.102891> and Marsico, Vigeland et al. 2021 <doi:10.1016/j.fsigen.2021.102519>.
Maintained by Franco Marsico. Last updated 3 months ago.
1.9 match 35 stars 6.74 score 19 scripts 1 dependents
nchenderson
daarem:Damped Anderson Acceleration with Epsilon Monotonicity for Accelerating EM-Like Monotone Algorithms
Implements the DAAREM method for accelerating the convergence of slow, monotone sequences from smooth, fixed-point iterations such as the EM algorithm. For further details about the DAAREM method, see Henderson, N.C. and Varadhan, R. (2019) <doi:10.1080/10618600.2019.1594835>.
Maintained by Nicholas Henderson. Last updated 3 years ago.
4.4 match 2.71 score 17 scripts 1 dependents
zhuwang46
mpath:Regularized Linear Models
Algorithms compute robust estimators for loss functions in the concave convex (CC) family by the iteratively reweighted convex optimization (IRCO), an extension of the iteratively reweighted least squares (IRLS). The IRCO reduces the weight of the observation that leads to a large loss; it also provides weights to help identify outliers. Applications include robust (penalized) generalized linear models and robust support vector machines. The package also contains penalized Poisson, negative binomial, zero-inflated Poisson, zero-inflated negative binomial regression models and robust models with non-convex loss functions. Wang et al. (2014) <doi:10.1002/sim.6314>, Wang et al. (2015) <doi:10.1002/bimj.201400143>, Wang et al. (2016) <doi:10.1177/0962280214530608>, Wang (2021) <doi:10.1007/s11749-021-00770-2>, Wang (2020) <arXiv:2010.02848>.
Maintained by Zhu Wang. Last updated 3 years ago.
1.8 match 1 stars 6.67 score 131 scripts 4 dependents
anik4322
minsample1:The Minimum Sample Size
Using this package, one can determine the minimum sample size required so that the absolute deviation of the sample mean and the population mean of a distribution becomes less than some pre-determined epsilon, i.e. it helps the user to determine the minimum sample size required to attain the pre-fixed precision level by minimizing the difference between the sample mean and population mean.
Maintained by Anik Paul. Last updated 2 years ago.
5.7 match 2.00 score 3 scriptsanik4322
minsample2:The Minimum Sample Size
Using this package, one can determine the minimum sample size required so that the mean square error of the sample mean and the population mean of a distribution becomes less than some pre-determined epsilon, i.e. it helps the user to determine the minimum sample size required to attain the pre-fixed precision level by minimizing the difference between the sample mean and population mean.
Maintained by Anik Paul. Last updated 2 years ago.
5.7 match 2.00 score 2 scripts
shaelebrown
TDApplied:Machine Learning and Inference for Topological Data Analysis
Topological data analysis is a powerful tool for finding non-linear global structure in whole datasets. The main tool of topological data analysis is persistent homology, which computes a topological shape descriptor of a dataset called a persistence diagram. 'TDApplied' provides useful and efficient methods for analyzing groups of persistence diagrams with machine learning and statistical inference, and these functions can also interface with other data science packages to form flexible and integrated topological data analysis pipelines.
Maintained by Shael Brown. Last updated 5 months ago.
1.7 match 16 stars 6.60 score 8 scripts
welch-lab
cytosignal:What the Package Does (One Line, Title Case)
What the package does (one paragraph).
Maintained by Jialin Liu. Last updated 7 days ago.
1.8 match 16 stars 5.95 score 6 scripts
kestrel99
pmxTools:Pharmacometric and Pharmacokinetic Toolkit
Pharmacometric tools for common data analytical tasks; closed-form solutions for calculating concentrations at given times after dosing based on compartmental PK models (1-compartment, 2-compartment and 3-compartment, covering infusions, zero- and first-order absorption, and lag times, after single doses and at steady state, per Bertrand & Mentre (2008) <http://lixoft.com/wp-content/uploads/2016/03/PKPDlibrary.pdf>); parametric simulation from NONMEM-generated parameter estimates and other output; and parsing, tabulating and plotting results generated by Perl-speaks-NONMEM (PsN).
Maintained by Justin Wilkins. Last updated 7 months ago.
nonmempharmacokineticssimulation
1.6 match 32 stars 6.43 score 84 scripts
mhahsler
markovDP:Infrastructure for Discrete-Time Markov Decision Processes (MDP)
Provides the infrastructure to work with Markov Decision Processes (MDPs) in R. The focus is on convenience in formulating MDPs, the support of sparse representations (using sparse matrices, lists and data.frames) and visualization of results. Some key components are implemented in C++ to speed up computation. Several popular solvers are implemented.
Maintained by Michael Hahsler. Last updated 4 days ago.
control-theorymarkov-decision-processoptimizationcpp
1.8 match 7 stars 5.51 score 4 scripts
cmerow
meteR:Fitting and Plotting Tools for the Maximum Entropy Theory of Ecology (METE)
Fit and plot macroecological patterns predicted by the Maximum Entropy Theory of Ecology (METE).
Maintained by Cory Merow. Last updated 6 years ago.
1.7 match 11 stars 5.35 score 41 scripts
eagerai
tfaddons:Interface to 'TensorFlow SIG Addons'
'TensorFlow SIG Addons' <https://www.tensorflow.org/addons> is a repository of community contributions that conform to well-established API patterns, but implement new functionality not available in core 'TensorFlow'. 'TensorFlow' natively supports a large number of operators, layers, metrics, losses, optimizers, and more. However, in a fast moving field like Machine Learning, there are many interesting new developments that cannot be integrated into core 'TensorFlow' (because their broad applicability is not yet clear, or it is mostly used by a smaller subset of the community).
Maintained by Turgut Abdullayev. Last updated 3 years ago.
deep-learningkerasneural-networkstensorflowtensorflow-addonstfa
1.7 match 20 stars 5.20 score 16 scripts
edhofman
ReSurv:Machine Learning Models For Predicting Claim Counts
Prediction of claim counts using the feature based development factors introduced in the manuscript <doi:10.48550/arXiv.2312.14549>. Implementation of Neural Networks, Extreme Gradient Boosting, and Cox model with splines to optimise the partial log-likelihood of proportional hazard models.
Maintained by Emil Hofman. Last updated 4 months ago.
1.5 match 2 stars 5.87 score 21 scripts
rudjer
REBayes:Empirical Bayes Estimation and Inference
Kiefer-Wolfowitz maximum likelihood estimation for mixture models and some other density estimation and regression methods based on convex optimization. See Koenker and Gu (2017) REBayes: An R Package for Empirical Bayes Mixture Methods, Journal of Statistical Software, 82, 1--26, <DOI:10.18637/jss.v082.i08>.
Maintained by Roger Koenker. Last updated 9 months ago.
2.3 match 3 stars 3.90 score 27 scripts 1 dependents
bertcarnell
triangle:Distribution Functions and Parameter Estimates for the Triangle Distribution
Provides the "r, q, p, and d" distribution functions for the triangle distribution. Also includes maximum likelihood estimation of parameters.
Maintained by Rob Carnell. Last updated 7 months ago.
1.1 match 3 stars 8.01 score 293 scripts 21 dependents
meadhbh-oneill
smoothic:Variable Selection Using a Smooth Information Criterion
Implementation of the SIC epsilon-telescope method, either using single or distributional (multiparameter) regression. Includes classical regression with normally distributed errors and robust regression, where the errors are from the Laplace distribution. The "smooth generalized normal distribution" is used, where the estimation of an additional shape parameter allows the user to move smoothly between both types of regression. See O'Neill and Burke (2022) "Robust Distributional Regression with Automatic Variable Selection" for more details. <arXiv:2212.07317>. This package also contains the data analyses from O'Neill and Burke (2023). "Variable selection using a smooth information criterion for distributional regression models". <doi:10.1007/s11222-023-10204-8>.
Maintained by Meadhbh ONeill. Last updated 2 years ago.
2.3 match 1 stars 3.70 score 3 scripts
university-of-newcastle-research
pmwg:Particle Metropolis Within Gibbs
Provides an R implementation of the Particle Metropolis within Gibbs sampler for model parameter, covariance matrix and random effect estimation. A more general implementation of the sampler based on the paper by Gunawan, D., Hawkins, G. E., Tran, M. N., Kohn, R., & Brown, S. D. (2020) <doi:10.1016/j.jmp.2020.102368>. An HTML tutorial document describing the package is available at <https://university-of-newcastle-research.github.io/samplerDoc/> and includes several detailed examples, some background and troubleshooting steps.
Maintained by Gavin Cooper. Last updated 1 years ago.
1.7 match 3 stars 4.94 score 29 scriptsbioc
HGC:A fast hierarchical graph-based clustering method
HGC (short for Hierarchical Graph-based Clustering) is an R package for conducting hierarchical clustering on large-scale single-cell RNA-seq (scRNA-seq) data. The key idea is to construct a dendrogram of cells on their shared nearest neighbor (SNN) graph. HGC provides functions for building graphs and for conducting hierarchical clustering on the graph. The users with old R version could visit https://github.com/XuegongLab/HGC/tree/HGC4oldRVersion to get HGC package built for R 3.6.
Maintained by XGlab. Last updated 5 months ago.
singlecellsoftwareclusteringrnaseqgraphandnetworkdnaseqcpp
1.8 match 4.70 score 25 scripts
rcannood
diffusionMap:Diffusion Map
Implements diffusion map method of data parametrization, including creation and visualization of diffusion map, clustering with diffusion K-means and regression using adaptive regression model. Richards (2009) <doi:10.1088/0004-637X/691/1/32>.
Maintained by Robrecht Cannoodt. Last updated 6 years ago.
1.8 match 5 stars 4.26 score 91 scripts
apguthrie
BRcal:Boldness-Recalibration of Binary Events
Boldness-recalibration maximally spreads out probability predictions while maintaining a user specified level of calibration, facilitated the brcal() function. Supporting functions to assess calibration via Bayesian and Frequentist approaches, Maximum Likelihood Estimator (MLE) recalibration, Linear in Log Odds (LLO)-adjust via any specified parameters, and visualize results are also provided. Methodological details can be found in Guthrie & Franck (2024) <doi:10.1080/00031305.2024.2339266>.
Maintained by Adeline P. Guthrie. Last updated 4 months ago.
1.5 match 4.81 score 5 scripts
s-baumann
FixedPoint:Algorithms for Finding Fixed Point Vectors of Functions
For functions that take and return vectors (or scalars), this package provides 8 algorithms for finding fixed point vectors (vectors for which the inputs and outputs to the function are the same vector). These algorithms include Anderson (1965) acceleration <doi:10.1145/321296.321305>, epsilon extrapolation methods (Wynn 1962 <doi:10.2307/2004051>) and minimal polynomial methods (Cabay and Jackson 1976 <doi:10.1137/0713060>).
Maintained by Stuart Baumann. Last updated 2 years ago.
1.8 match 1 stars 3.69 score 33 scripts 1 dependentsbioc
TTMap:Two-Tier Mapper: a clustering tool based on topological data analysis
TTMap is a clustering method that groups together samples with the same deviation in comparison to a control group. It is specially useful when the data is small. It is parameter free.
Maintained by Rachel Jeitziner. Last updated 5 months ago.
softwaremicroarraydifferentialexpressionmultiplecomparisonclusteringclassification
2.0 match 3.00 score
ochoalab
simtrait:Simulate Complex Traits from Genotypes
Simulate complex traits given a SNP genotype matrix and model parameters (the desired heritability, optional environment group effects, number of causal loci, and either the true ancestral allele frequencies used to generate the genotypes or the mean kinship for a real dataset). Emphasis is on avoiding common biases due to the use of estimated allele frequencies. The code selects random loci to be causal, constructs coefficients for these loci and random independent non-genetic effects, and can optionally generate random group effects. Traits can follow three models: random coefficients, fixed effect sizes, and infinitesimal (multivariate normal). GWAS method benchmarking functions are also provided. Described in Yao and Ochoa (2023) <doi:10.7554/eLife.79238>.
Maintained by Alejandro Ochoa. Last updated 4 months ago.
1.0 match 5 stars 5.32 score 21 scripts
09dohkim
svrpath:The SVR Path Algorithm
Computes the entire solution paths for Support Vector Regression(SVR) with respect to the regularization parameter, lambda and epsilon in epsilon-intensive loss function, efficiently. We call each path algorithm svrpath and epspath. See Wang, G. et al (2008) <doi:10.1109/TNN.2008.2002077> for details regarding the method.
Maintained by Do Hyun Kim. Last updated 7 years ago.
4.2 match 1.26 score 18 scripts
kivanvan
ipolygrowth:Individual Growth Curve Parameter Calculation using Polynomial Functions
Calculation of key bacterial growth curve parameters using fourth degree polynomial functions. Six growth curve parameters are provided including peak growth rate, doubling time, lag time, maximum growth, and etc. 'ipolygrowth' takes time series data from individual biological samples (with technical replicates) or multiple samples.
Maintained by Jifan Wang. Last updated 6 months ago.
1.3 match 4.18 score 10 scripts
gilberto-sassi
sinar:Conditional Least Squared (CLS) Method for the Model SINAR(1,1)
Implementation of the Conditional Least Square (CLS) estimates and its covariance matrix for the first-order spatial integer-valued autoregressive model (SINAR(1,1)) proposed by Ghodsi (2012) <doi:10.1080/03610926.2011.560739>.
Maintained by Gilberto P. Sassi. Last updated 4 years ago.
1.9 match 2.70 score 1 scripts
cran
RegCombin:Partially Linear Regression under Data Combination
We implement linear regression when the outcome of interest and some of the covariates are observed in two different datasets that cannot be linked, based on D'Haultfoeuille, Gaillac, Maurel (2022) <doi:10.3386/w29953>. The package allows for common regressors observed in both datasets, and for various shape constraints on the effect of covariates on the outcome of interest. It also provides the tools to perform a test of point identification. See the associated vignette <https://github.com/cgaillac/RegCombin/blob/master/RegCombin_vignette.pdf> for theory and code examples.
Maintained by Christophe Gaillac. Last updated 1 years ago.
5.0 match 1 stars 1.00 score
fernandalschumacher
skewlmm:Scale Mixture of Skew-Normal Linear Mixed Models
It fits scale mixture of skew-normal linear mixed models using either an expectation–maximization (EM) type algorithm or its accelerated version (Damped Anderson Acceleration with Epsilon Monotonicity, DAAREM), including some possibilities for modeling the within-subject dependence. Details can be found in Schumacher, Lachos and Matos (2021) <doi:10.1002/sim.8870>.
Maintained by Fernanda L. Schumacher. Last updated 2 months ago.
0.5 match 6 stars 4.43 score 10 scripts
dmarchette
HyperG:Hypergraphs in R
Implements various tools for storing and analyzing hypergraphs. Handles basic undirected, unweighted hypergraphs, and various ways of creating hypergraphs from a number of representations, and converting between graphs and hypergraphs.
Maintained by David J. Marchette. Last updated 4 years ago.
2.0 match 1.00 score 10 scriptscran
ttbary:Barycenter Methods for Spatial Point Patterns
Computes a point pattern in R^2 or on a graph that is representative of a collection of many data patterns. The result is an approximate barycenter (also known as Fréchet mean or prototype) based on a transport-transform metric. Possible choices include Optimal SubPattern Assignment (OSPA) and Spike Time metrics. Details can be found in Müller, Schuhmacher and Mateu (2020) <doi:10.1007/s11222-020-09932-y>.
Maintained by Dominic Schuhmacher. Last updated 2 years ago.
1.7 match 1.00 score
mevangelou
PAGWAS:Pathway Analysis Methods for Genomewide Association Data
Bayesian hierarchical methods for pathway analysis of genomewide association data: Normal/Bayes factors and Sparse Normal/Adaptive lasso. The Frequentist Fisher's product method is included as well.
Maintained by Marina Evangelou. Last updated 9 years ago.
1.6 match 1.04 score 11 scripts
laats
PrivateLR:Differentially Private Regularized Logistic Regression
Implements two differentially private algorithms for estimating L2-regularized logistic regression coefficients. A randomized algorithm F is epsilon-differentially private (C. Dwork, Differential Privacy, ICALP 2006 <DOI:10.1007/11681878_14>), if |log(P(F(D) in S)) - log(P(F(D') in S))| <= epsilon for any pair D, D' of datasets that differ in exactly one record, any measurable set S, and the randomness is taken over the choices F makes.
Maintained by Staal A. Vinterbo. Last updated 7 years ago.
0.8 match 1.00 score 1 scripts
pdlotko
BallMapper:The Ball Mapper Algorithm
The core algorithm is described in "Ball mapper: a shape summary for topological data analysis" by Pawel Dlotko, (2019) <arXiv:1901.07410>. Please consult the following youtube video <https://www.youtube.com/watch?v=M9Dm1nl_zSQfor> the idea of functionality. Ball Mapper provide a topologically accurate summary of a data in a form of an abstract graph. To create it, please provide the coordinates of points (in the points array), values of a function of interest at those points (can be initialized randomly if you do not have it) and the value epsilon which is the radius of the ball in the Ball Mapper construction. It can be understood as the minimal resolution on which we use to create the model of the data.
Maintained by Pawel Dlotko. Last updated 6 years ago.
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