|
r-hac
|
public |
Package provides the estimation of the structure and the parameters, sampling methods and structural plots of Hierarchical Archimedean Copulae (HAC).
|
2023-06-16 |
|
r-gsheet
|
public |
Simple package to download Google Sheets using just the sharing link. Spreadsheets can be downloaded as a data frame, or as plain text to parse manually. Google Sheets is the new name for Google Docs Spreadsheets.
|
2023-06-16 |
|
r-grape
|
public |
Gene-Ranking Analysis of Pathway Expression (GRAPE) is a tool for summarizing the consensus behavior of biological pathways in the form of a template, and for quantifying the extent to which individual samples deviate from the template. GRAPE templates are based only on the relative rankings of the genes within the pathway and can be used for classification of tissue types or disease subtypes. GRAPE can be used to represent gene-expression samples as vectors of pathway scores, where each pathway score indicates the departure from a given collection of reference samples. The resulting pathway- space representation can be used as the feature set for various applications, including survival analysis and drug-response prediction. Users of GRAPE should use the following citation: Klein MI, Stern DF, and Zhao H. GRAPE: A pathway template method to characterize tissue-specific functionality from gene expression profiles. BMC Bioinformatics, 18:317 (June 2017).
|
2023-06-16 |
|
r-graddescent
|
public |
An implementation of various learning algorithms based on Gradient Descent for dealing with regression tasks. The variants of gradient descent algorithm are : Mini-Batch Gradient Descent (MBGD), which is an optimization to use training data partially to reduce the computation load. Stochastic Gradient Descent (SGD), which is an optimization to use a random data in learning to reduce the computation load drastically. Stochastic Average Gradient (SAG), which is a SGD-based algorithm to minimize stochastic step to average. Momentum Gradient Descent (MGD), which is an optimization to speed-up gradient descent learning. Accelerated Gradient Descent (AGD), which is an optimization to accelerate gradient descent learning. Adagrad, which is a gradient-descent-based algorithm that accumulate previous cost to do adaptive learning. Adadelta, which is a gradient-descent-based algorithm that use hessian approximation to do adaptive learning. RMSprop, which is a gradient-descent-based algorithm that combine Adagrad and Adadelta adaptive learning ability. Adam, which is a gradient-descent-based algorithm that mean and variance moment to do adaptive learning. Stochastic Variance Reduce Gradient (SVRG), which is an optimization SGD-based algorithm to accelerates the process toward converging by reducing the gradient. Semi Stochastic Gradient Descent (SSGD),which is a SGD-based algorithm that combine GD and SGD to accelerates the process toward converging by choosing one of the gradients at a time. Stochastic Recursive Gradient Algorithm (SARAH), which is an optimization algorithm similarly SVRG to accelerates the process toward converging by accumulated stochastic information. Stochastic Recursive Gradient Algorithm+ (SARAHPlus), which is a SARAH practical variant algorithm to accelerates the process toward converging provides a possibility of earlier termination.
|
2023-06-16 |
|
r-gparotation
|
public |
Gradient Projection Algorithm Rotation for Factor Analysis. See ?GPArotation.Intro for more details.
|
2023-06-16 |
|
r-gogarch
|
public |
Implementation of the GO-GARCH model class
|
2023-06-16 |
|
r-gmt
|
public |
Interface between the GMT map-making software and R, enabling the user to manipulate geographic data within R and call GMT commands to draw and annotate maps in postscript format. The gmt package is about interactive data analysis, rapidly visualizing subsets and summaries of geographic data, while performing statistical analysis in the R console.
|
2023-06-16 |
|
r-glmmlasso
|
public |
A variable selection approach for generalized linear mixed models by L1-penalized estimation is provided.
|
2023-06-16 |
|
r-glm2
|
public |
Fits generalized linear models using the same model specification as glm in the stats package, but with a modified default fitting method that provides greater stability for models that may fail to converge using glm.
|
2023-06-16 |
|
r-gipfrm
|
public |
Maximum likelihood estimation under relational models, with or without the overall effect.
|
2023-06-16 |
|
r-ggversa
|
public |
A collection of datasets for the upcoming book "Graficas versatiles con ggplot: Analisis visuales de datos", by Raymond L. Tremblay and Julian Hernandez-Serano.
|
2023-06-16 |
|
r-geotech
|
public |
A compilation of functions for performing calculations and creating plots that commonly arise in geotechnical engineering and soil mechanics. The types of calculations that are currently included are: (1) phase diagrams and index parameters, (2) grain-size distributions, (3) plasticity, (4) soil classification, (5) compaction, (6) groundwater, (7) subsurface stresses (geostatic and induced), (8) Mohr circle analyses, (9) consolidation settlement and rate, (10) shear strength, (11) bearing capacity, (12) lateral earth pressures, (13) slope stability, and (14) subsurface explorations. Geotechnical engineering students, educators, researchers, and practitioners will find this package useful.
|
2023-06-16 |
|
r-latexpdf
|
public |
Converts table-like objects to stand-alone PDF or PNG. Can be used to embed tables and arbitrary content in PDF or Word documents. Provides a low-level R interface for creating 'LaTeX' code, e.g. command() and a high-level interface for creating PDF documents, e.g. as.pdf.data.frame(). Extensive customization is available via mid-level functions, e.g. as.tabular(). See also 'package?latexpdf'. Support for PNG is experimental; see 'as.png.data.frame'. Adapted from 'metrumrg' <http://r-forge.r-project.org/R/?group_id=1215>. Requires a compatible installation of 'pdflatex', e.g. <https://miktex.org/>.
|
2023-06-16 |
|
r-knoema
|
public |
Using this package, users can access to the largest collection of public data and statistics on the Internet featuring about 2.5 billion time series from thousands of sources collected in 'Knoema' repository and use rich R calculations in order to analyze the data. Because data in 'Knoema' is time series data, 'Knoema' function offers data in a number of formats usable in R such as 'ts', 'xts' or 'zoo'. For more information about 'Knoema' API go to <https://knoema.com/dev/docs>.
|
2023-06-16 |
|
r-jointnmix
|
public |
Fits univariate and joint N-mixture models for data on two unmarked site-associated species. Includes functions to estimate latent abundances through empirical Bayes methods.
|
2023-06-16 |
|
r-joint.cox
|
public |
Perform likelihood estimation and dynamic prediction under joint frailty-copula models for tumour progression and death in meta-analysis. A penalized likelihood method is employed for estimating model parameters, where the baseline hazard functions are modeled by smoothing splines. The methods are applicable for meta-analytic data combining several studies. The methods can analyze data having information on both terminal event time (e.g., time-to-death) and non-terminal event time (e.g., time-to-tumour progression). See Emura et al. (2017) <doi:10.1177/0962280215604510> for likelihood estimation, and Emura et al. (2018) <doi:10.1177/0962280216688032> for dynamic prediction. More details on these methods can also be found in a book of Emura et al. (2019) <10.1007/978-981-13-3516-7>. Survival data from ovarian cancer patients are also available.
|
2023-06-16 |
|
r-jm
|
public |
Shared parameter models for the joint modeling of longitudinal and time-to-event data.
|
2023-06-16 |
|
r-jgl
|
public |
The Joint Graphical Lasso is a generalized method for estimating Gaussian graphical models/ sparse inverse covariance matrices/ biological networks on multiple classes of data. We solve JGL under two penalty functions: The Fused Graphical Lasso (FGL), which employs a fused penalty to encourage inverse covariance matrices to be similar across classes, and the Group Graphical Lasso (GGL), which encourages similar network structure between classes. FGL is recommended over GGL for most applications. Reference: Danaher P, Wang P, Witten DM. (2013) <doi:10.1111/rssb.12033>.
|
2023-06-16 |
|
r-islr
|
public |
We provide the collection of data-sets used in the book 'An Introduction to Statistical Learning with Applications in R'.
|
2023-06-16 |
|
r-ipmpack
|
public |
IPMpack takes demographic vital rates and (optionally) environmental data to build integral projection models. A number of functional forms for growth and survival can be incorporated, as well as a range of reproductive strategies. The package also includes a suite of diagnostic routines, provides classic matrix model output (e.g., lambda, elasticities, sensitivities), and produces post-hoc metrics (e.g., passage time and life expectancy).
|
2023-06-16 |
|
r-invasioncorrection
|
public |
The correction is achieved under the assumption that non-migrating cells of the essay approximately form a quadratic flow profile due to frictional effects, compare law of Hagen-Poiseuille for flow in a tube. The script fits a conical plane to give xyz-coordinates of the cells. It outputs the number of migrated cells and the new corrected coordinates.
|
2023-06-16 |
|
r-interactiveigraph
|
public |
An extension of the package 'igraph'. This package create possibly to work with 'igraph' objects interactively.
|
2023-06-16 |
|
r-infiniumpurify
|
public |
The proportion of cancer cells in solid tumor sample, known as the tumor purity, has adverse impact on a variety of data analyses if not properly accounted for. We develop 'InfiniumPurify', which is a comprehensive R package for estimating and accounting for tumor purity based on DNA methylation Infinium 450k array data. 'InfiniumPurify' provides functionalities for tumor purity estimation. In addition, it can perform differential methylation detection and tumor sample clustering with the consideration of tumor purities.
|
2023-06-16 |
|
r-inference
|
public |
Collection of functions to extract inferential values (point estimates, confidence intervals, p-values, etc) of a fitted model object into a matrix-like object that can be used for table/report generation; transform point estimates via the delta method.
|
2023-06-16 |
|
r-indirect
|
public |
Functions are provided to facilitate prior elicitation for Bayesian generalised linear models using independent conditional means priors. The package supports the elicitation of multivariate normal priors for generalised linear models. The approach can be applied to indirect elicitation for a generalised linear model that is linear in the parameters. The package is designed such that the facilitator executes functions within the R console during the elicitation session to provide graphical and numerical feedback at each design point. Various methodologies for eliciting fractiles (equivalently, percentiles or quantiles) are supported, including versions of the approach of Hosack et al. (2017) <doi:10.1016/j.ress.2017.06.011>. For example, experts may be asked to provide central credible intervals that correspond to a certain probability. Or experts may be allowed to vary the probability allocated to the central credible interval for each design point. Additionally, a median may or may not be elicited.
|
2023-06-16 |
|
r-indiantaxcalc
|
public |
Calculate Indian Income Tax liability for Financial years of Individual resident aged below 60 years,Senior Citizen,Super Senior Citizen, Firm, Local Authority, Any Non Resident Individual / Hindu Undivided Family / Association of Persons /Body of Individuals / Artificial Judicial Person, Co-operative Society.
|
2023-06-16 |
|
r-igorr
|
public |
Provides function to read data from the 'Igor Pro' data analysis program by Wavemetrics. The data formats supported are 'Igor' packed experiment format (pxp) and 'Igor' binary wave (ibw). See: http://www.wavemetrics.com/ for details. Also includes functions to load special pxp files produced by the 'Igor Pro' 'Neuromatic' and 'Nclamp' packages for recording and analysing neuronal data. See http://www.neuromatic.thinkrandom.com/ for details.
|
2023-06-16 |
|
r-icge
|
public |
ICGE is a package that helps to estimate the number of real clusters in data as well as to identify atypical units. The underlying methods are based on distances rather than on unit x variables.
|
2023-06-16 |
|
r-hmvd
|
public |
Perform association test between a group of variable and the outcome.
|
2023-06-16 |
|
r-hilmm
|
public |
Estimation of heritability with confidence intervals in linear mixed models.
|
2023-06-16 |
|
r-hcc
|
public |
A new diagnostic check for model adequacy in regression and generalized linear models is implemented.
|
2023-06-16 |
|
r-hcandersenr
|
public |
Texts for H.C. Andersens fairy tales, ready for text analysis. Fairy tales in German, Danish, English, Spanish and French.
|
2023-06-16 |
|
r-happytime
|
public |
There are two interesting games in this package, one is 2048 games(for windows), using up and down to control the direction until there is a 2048 figure. And the other is 'what to eat today',preparing for people who choose difficulties, including most of the delicious Cantonese cuisine.
|
2023-06-16 |
|
r-gsarima
|
public |
Write SARIMA models in (finite) AR representation and simulate generalized multiplicative seasonal autoregressive moving average (time) series with Normal / Gaussian, Poisson or negative binomial distribution.
|
2023-06-16 |
|
r-grouptest
|
public |
Contains functions for a two-stage multiple testing procedure for grouped hypothesis, aiming at controlling both the total posterior false discovery rate and within-group false discovery rate.
|
2023-06-16 |
|
r-grimport
|
public |
Functions for converting, importing, and drawing PostScript pictures in R plots.
|
2023-06-16 |
|
r-gridgraphics
|
public |
Functions to convert a page of plots drawn with the 'graphics' package into identical output drawn with the 'grid' package. The result looks like the original 'graphics'-based plot, but consists of 'grid' grobs and viewports that can then be manipulated with 'grid' functions (e.g., edit grobs and revisit viewports).
|
2023-06-16 |
|
r-gpr
|
public |
This package provides a minimalistic functionality necessary to apply Gaussian Process in R. They provide a selection of functionalities of GPML Matlab library.
|
2023-06-16 |
|
r-govstatjpn
|
public |
This package purposes to deal with public survey data of Japanese government via their Application Programming Interface (http://statdb.nstac.go.jp/)
|
2023-06-16 |
|
r-googleknowledgegraphr
|
public |
Allows you to retrieve information from the 'Google Knowledge Graph' API <https://www.google.com/intl/bn/insidesearch/features/search/knowledge.html> and process it in R in various forms. The 'Knowledge Graph Search' API lets you find entities in the 'Google Knowledge Graph'. The API uses standard 'schema.org' types and is compliant with the 'JSON-LD' specification.
|
2023-06-16 |
|
r-gnumeric
|
public |
Read data files readable by 'gnumeric' into 'R'. Can read whole sheet or a range, from several file formats, including the native format of 'gnumeric'. Reading is done by using 'ssconvert' (a file converter utility included in the 'gnumeric' distribution <http://projects.gnome.org/gnumeric/>) to convert the requested part to CSV. From 'gnumeric' files (but not other formats) can list sheet names and sheet sizes or read all sheets.
|
2023-06-16 |
|
r-glmmadaptive
|
public |
Fits generalized linear mixed models for a single grouping factor under maximum likelihood approximating the integrals over the random effects with an adaptive Gaussian quadrature rule; Jose C. Pinheiro and Douglas M. Bates (1995) <doi:10.1080/10618600.1995.10474663>.
|
2023-06-16 |
|
r-gldrm
|
public |
Fits a generalized linear density ratio model (GLDRM). A GLDRM is a semiparametric generalized linear model. In contrast to a GLM, which assumes a particular exponential family distribution, the GLDRM uses a semiparametric likelihood to estimate the reference distribution. The reference distribution may be any discrete, continuous, or mixed exponential family distribution. The model parameters, which include both the regression coefficients and the cdf of the unspecified reference distribution, are estimated by maximizing a semiparametric likelihood. Regression coefficients are estimated with no loss of efficiency, i.e. the asymptotic variance is the same as if the true exponential family distribution were known. Huang (2014) <doi:10.1080/01621459.2013.824892>. Huang and Rathouz (2012) <doi:10.1093/biomet/asr075>. Rathouz and Gao (2008) <doi:10.1093/biostatistics/kxn030>.
|
2023-06-16 |
|
r-ggroups
|
public |
Calculates additive and dominance genetic relationship matrices and their inverses, in matrix and tabular-sparse formats. It includes functions for checking and processing pedigree, as well as functions to calculate the matrix of genetic group contributions (Q), and adding those contributions to the genetic merit of animals (Quaas (1988) <doi:10.3168/jds.S0022-0302(88)79691-5>). Calculation of Q is computationally extensive. There are computationally optimized functions to calculate Q.
|
2023-06-16 |
|
r-genwin
|
public |
Defines window or bin boundaries for the analysis of genomic data. Boundaries are based on the inflection points of a cubic smoothing spline fitted to the raw data. Along with defining boundaries, a technique to evaluate results obtained from unequally-sized windows is provided. Applications are particularly pertinent for, though not limited to, genome scans for selection based on variability between populations (e.g. using Wright's fixations index, Fst, which measures variability in subpopulations relative to the total population).
|
2023-06-16 |
|
r-genef
|
public |
This package implements several generalized F-statistics. The current version includes a generalized F-statistic based on the flexible isotonic/monotonic regression or order restricted hypothesis testing.
|
2023-06-16 |
|
r-genderizer
|
public |
Utilizes the 'genderize.io' Application Programming Interface to predict gender from first names extracted from a text vector. The accuracy of prediction could be controlled by two parameters: counts of a first name in the database and probability of prediction.
|
2023-06-16 |
|
r-gen2stage
|
public |
One can find single-stage and two-stage designs for a phase II single-arm study with either efficacy or safety/toxicity endpoints as described in Kim and Wong (2019) <doi:10.29220/CSAM.2019.26.2.163>.
|
2023-06-16 |
|
r-gdistance
|
public |
Calculate distances and routes on geographic grids.
|
2023-06-16 |
|
r-lassopv
|
public |
Estimate the p-values for predictors x against target variable y in lasso regression, using the regularization strength when each predictor enters the active set of regularization path for the first time as the statistic. This is based on the assumption that predictors (of the same variance) that (first) become active earlier tend to be more significant. Three null distributions are supported: normal and spherical, which are computed separately for each predictor and analytically under approximation, which aims at efficiency and accuracy for small p-values.
|
2023-06-16 |
