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r-probhat
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public |
Computes nonparametric probability distributions (probability density functions, cumulative distribution functions and quantile functions) using kernel smoothing. Supports univariate, multivariate and conditional distributions, and weighted data (possibly useful mixed with fuzzy clustering or frequency data). Also, supports empirical continuous cumulative distribution functions and their inverses, and random number generation.
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2023-06-16 |
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r-pkpdmodels
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Provides functions to evaluate common pharmacokinetic/pharmacodynamic models and their gradients.
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2023-06-16 |
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r-powerlmm
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Calculate power for the 'time x treatment' effect in two- and three-level multilevel longitudinal studies with missing data. Both the third-level factor (e.g. therapists, schools, or physicians), and the second-level factor (e.g. subjects), can be assigned random slopes. Studies with partially nested designs, unequal cluster sizes, unequal allocation to treatment arms, and different dropout patterns per treatment are supported. For all designs power can be calculated both analytically and via simulations. The analytical calculations extends the method described in Galbraith et al. (2002) <doi:10.1016/S0197-2456(02)00205-2>, to three-level models. Additionally, the simulation tools provides flexible ways to investigate bias, Type I errors and the consequences of model misspecification.
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2023-06-16 |
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r-plrasch
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public |
Fit Log Linear by Linear Association models and Rasch family models by pseudolikelihood estimation
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2023-06-16 |
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r-pathlibr
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public |
An OO Interface for path manipulation, emulating pythons "pathlib".
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2023-06-16 |
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r-pastis
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A pre-processor for mrBayes that assimilates sequences, taxonomic information and tree constraints as per xxx. The main functions of interest for most users will be pastis_simple, pastis_main and conch. The main analysis is conducted with pastis_simple or pastis_main followed by a manual execution of mrBayes (>3.2). The placement of taxa not contained in the tree constraint can be investigated using conch.
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2023-06-16 |
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r-parentoffspring
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Conduct the Parent-Offspring Test Using Monomorphic SNP Markers. The similarity to the parents is computed for each offspring, and a plot of similarity for all offspring is produced. One can keep the offspring above some threshold for the similarity for further studies.
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2023-06-16 |
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r-phtt
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The package provides estimation procedures for panel data with large dimensions n, T, and general forms of unobservable heterogeneous effects. Particularly, the estimation procedures are those of Bai (2009) and Kneip, Sickles, and Song (2012), which complement one another very well: both models assume the unobservable heterogeneous effects to have a factor structure. The method of Bai (2009) assumes that the factors are stationary, whereas the method of Kneip et al. (2012) allows the factors to be non-stationary. Additionally, the 'phtt' package provides a wide range of dimensionality criteria in order to estimate the number of the unobserved factors simultaneously with the remaining model parameters.
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2023-06-16 |
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r-pairedci
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The package contains two functions: paired.Loc and paired.Scale. A parametric and nonparametric confidence interval can be computed for the ratio of locations (paired.Loc) and the ratio of scales (paired.Scale). The samples must be paired and expected values must be positive.
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2023-06-16 |
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r-outlierdc
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This package provides three algorithms to detect outlying observations for censored survival data.
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2023-06-16 |
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r-oncomodel
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Computing probabilistic tree models for oncogenesis based on genetic data using maximum likelihood.
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2023-06-16 |
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r-nortara
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An implementation of a specific method for generating n-dimensional random vectors with given marginal distributions and correlation matrix. The method uses the NORTA (NORmal To Anything) approach which generates a standard normal random vector and then transforms it into a random vector with specified marginal distributions and the RA (Retrospective Approximation) algorithm which is a generic stochastic root-finding algorithm. The marginals can be continuous or discrete. See the vignette of package for more details.
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2023-06-16 |
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r-normwhn.test
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Includes Omnibus Univariate and Multivariate Normality Tests (See Doornik and Hansen (1994)). One variation allows for the possibility of weak dependence rather than independence in the variable(s). Also included is an univariate white noise test where the null hypothesis is "white noise" rather than strict "white noise".
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2023-06-16 |
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r-probsvm
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This package provides multiclass conditional probability estimation for the SVM, which is distributional assumption free.
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2023-06-16 |
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r-ppls
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Contains linear and nonlinear regression methods based on Partial Least Squares and Penalization Techniques. Model parameters are selected via cross-validation, and confidence intervals ans tests for the regression coefficients can be conducted via jackknifing.
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2023-06-16 |
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r-peakpick
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Biologically inspired methods for detecting peaks in one-dimensional data, such as time series or genomics data. The algorithms were originally designed by Weber, Ramachandran, and Henikoff, see documentation.
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2023-06-16 |
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r-parallelml
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By sampling your data, running the provided classifier on these samples in parallel on your own machine and letting your models vote on a prediction, we return much faster predictions than the regular machine learning algorithm and possibly even more accurate predictions.
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2023-06-16 |
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r-osmose
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The multispecies and individual-based model (IBM) 'OSMOSE' (Shin and Curry (2001) <doi:10.1016/S0990-7440(01)01106-8> and Shin and Curry (2004) <doi:10.1139/f03-154>) focuses on fish species. This model assumes opportunistic predation based on spatial co-occurrence and size adequacy between a predator and its prey (size-based opportunistic predation). It represents fish individuals grouped into schools, which are characterized by their size, weight, age, taxonomy and geographical location (2D model), and which undergo major processes of fish life cycle (growth, explicit predation, natural and starvation mortalities, reproduction and migration) and fishing exploitation. The model needs basic biological parameters that are often available for a wide range of species, and which can be found in 'FishBase' for instance (see <http://www.fishbase.org/search.php>), and fish spatial distribution data. This package provides tools to build and run simulations using the 'OSMOSE' model.
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2023-06-16 |
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r-pragma
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pragma allows for the use of pragma (also sometimes called directives or keywords. These allow assigning arbitrary functionality to a word without requiring the standard function call syntax i.e. with parens.
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2023-06-16 |
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r-pinnacle.api
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An interface to the API by Pinnacle that allows Pinnacle customers to interact with the sports market data in R.See <https://www.pinnacle.com/en/api> for more information. The Pinnacle API can be used to place wagers, retrieve line information, retrieve account information.Please be aware that the TOC of Pinnacle apply <https://www.pinnacle.com/en/termsandconditions>. An account with Pinnacle is necessary to use the Pinnacle API.
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2023-06-16 |
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r-parse
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Model-based clustering and identifying informative features based on regularization methods. The package includes three regularization methods - PAirwise Reciprocal fuSE (PARSE) penalty proposed by Wang, Zhou and Hoeting (2016), the adaptive L1 penalty (APL1) and the adaptive pairwise fusion penalty (APFP). Heatmaps are included to shown the identification of informative features.
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2023-06-16 |
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r-packclassic
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This package comes to illustrate the book "Petit Manuel de Programmation Orientee Objet sous R"
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2023-06-16 |
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r-predictiveregression
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Three prediction algorithms described in the paper "On-line predictive linear regression" Annals of Statistics 37, 1566 - 1590 (2009)
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2023-06-16 |
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r-plsbiplot1
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Principal Component Analysis (PCA) biplots, Covariance monoplots and biplots, Partial Least Squares (PLS) biplots, Partial Least Squares for Generalized Linear Model (PLS-GLM) biplots, Sparse Partial Least Squares (SPLS) biplots and Sparse Partial Least Squares for Generalized Linear Model (SPLS-GLM) biplots.
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2023-06-16 |
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r-order2parent
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This package uses B-spline based nonparametric smooth estimators to estimate parent distributions given observations on multiple order statistics.
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2023-06-16 |
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r-orddom
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Computes ordinal, statistics and effect sizes as an alternative to mean comparison: Cliff's delta or success rate difference (SRD), Vargha and Delaney's A or the Area Under a Receiver Operating Characteristic Curve (AUC), the discrete type of McGraw & Wong's Common Language Effect Size (CLES) or Grissom & Kim's Probability of Superiority (PS), and the Number needed to treat (NNT) effect size. Moreover, comparisons to Cohen's d are offered based on Huberty & Lowman's Percentage of Group (Non-)Overlap considerations.
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2023-06-16 |
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r-optauc
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Searches for optimal linear combination of multiple diagnostic tests (markers) that maximizes the area under the receiver operating characteristic curve (AUC); performs an approximated cross-validation for estimating the AUC associated with the estimated coefficients.
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2023-06-16 |
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r-praktikum
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Kasulikud funktsioonid kvantitatiivsete mudelite kursuse (SHPH.00.004) jaoks
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2023-06-16 |
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r-poweranalysis
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Basic functions for power analysis and effect size calculation.
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2023-06-16 |
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r-politicaldata
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Provides useful functions for obtaining commonly-used data in political analysis and political science, including from sources such as the Comparative Agendas Project <https://www.comparativeagendas.net>, which provides data on politics and policy from 20+ countries, the MIT Election and Data Science Lab <https://www.electionlab.mit.edu>, and FiveThirtyEight <https://www.FiveThirtyEight.com>.
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2023-06-16 |
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r-poissonseq
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This package implements a method for normalization, testing, and false discovery rate estimation for RNA-sequencing data. The description of the method is in Li J, Witten DM, Johnstone I, Tibshirani R (2012). Normalization, testing, and false discovery rate estimation for RNA-sequencing data. Biostatistics 13(3): 523-38. We estimate the sequencing depths of experiments using a new method based on Poisson goodness-of-fit statistic, calculate a score statistic on the basis of a Poisson log-linear model, and then estimate the false discovery rate using a modified version of permutation plug-in method. A more detailed instruction as well as sample data is available at http://www.stanford.edu/~junli07/research.html. In this version, we changed the way of calculating log foldchange for two-class data. The FDR estimation part remains unchanged.
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2023-06-16 |
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r-pairwised
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Pairing observations according to a chosen formula and facilitates bilateral analysis of the panel data. Paring is possible for observations, as well as for vectors of observations ordered with respect to time.
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2023-06-16 |
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r-pacbpred
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This package is intended to perform estimation and prediction in high-dimensional additive models, using a sparse PAC-Bayesian point of view and a MCMC algorithm. The method is fully described in Guedj and Alquier (2013), 'PAC-Bayesian Estimation and Prediction in Sparse Additive Models', Electronic Journal of Statistics, 7, 264--291.
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2023-06-16 |
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r-prinsimp
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Provides capabilities beyond principal components analysis to focus on finding structure in low variability subspaces. Constructs and plots simple basis vectors for pre-defined and user-defined measures of simplicity.
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2023-06-16 |
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r-phuse
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Make it easy to review, download and execute scripts stored in Github 'phuse-scripts' repository <https://github.com/phuse-org/phuse-scripts>. Some examples included show the web application framework using the script metadata. The 'PhUSE' is Pharmaceutical Users Software Exchange <http://www.phuse.eu>.
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2023-06-16 |
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r-outrankingtools
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Functions to process ''outranking'' ELECTRE methods existing in the literature. See, e.g., <http://en.wikipedia.org/wiki/ELECTRE> about the outranking approach and the foundations of ELECTRE methods.
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2023-06-16 |
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r-onetr
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Provides a series of functions designed to enable users to easily search and interact with occupational data from the O*NET API <www.onetonline.org>. The package produces parsed and listed XML data for custom interactions, or pre-packaged functions for easy extraction of specific data (e.g., Knowledge, Skills, Abilities, Work Styles, etc.).
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2023-06-16 |
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r-omd
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This package including two useful function, which can be used for filter the molecular descriptors matrix for QSAR.
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2023-06-16 |
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r-oak
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Functions and classes to create and manipulate trees and nodes.
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2023-06-16 |
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r-npmlencc
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To compute the non-parametric maximum likelihood estimates (NPMLEs) and penalized NPMLEs with SCAD, HARD and LASSO penalties for nested case-control or case-cohort sampling design with time matching under Cox's regression model. It also proposes the standard error formula for estimator using observed profile likelihood. For details about (penalized) NPNLEs see the original paper "Penalized Full Likelihood Approach to Variable Selection for Cox's Regression Model under Nested Case-Control Sampling" by Wang et al. (2019) <doi:10.1007/s10985-019-09475-z>.
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2023-06-16 |
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r-prognosticroc
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Prognostic ROC curve is an alternative graphical approach to represent the discriminative capacity of the marker: a receiver operating characteristic (ROC) curve by plotting 1 minus the survival in the high-risk group against 1 minus the survival in the low-risk group. This package contains functions to assess prognostic ROC curve. The user can enter the survival according to a model previously estimated or the user can also enter individual survival data for estimating the prognostic ROC curve by using Kaplan-Meier estimator. The area under the curve (AUC) corresponds to the probability that a patient in the low-risk group has a longer lifetime than a patient in the high-risk group. The prognostic ROC curve provides complementary information compared to survival curves. The AUC is assessed by using the trapezoidal rules. When survival curves do not reach 0, the prognostic ROC curve is incomplete and the extrapolations of the AUC are performed by assuming pessimist, optimist and non-informative situations.
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2023-06-16 |
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r-pnn
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The program pnn implements the algorithm proposed by Specht (1990). It is written in the R statistical language. It solves a common problem in automatic learning. Knowing a set of observations described by a vector of quantitative variables, we classify them in a given number of groups. Then, the algorithm is trained with this datasets and should guess afterwards the group of any new observation. This neural network has the main advantage to begin generalization instantaneously even with a small set of known observations. It is delivered with four functions (learn, smooth, perf and guess) and a dataset. The functions are documented with examples and provided with unit tests.
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2023-06-16 |
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r-plusser
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plusser provides an API interface to Google+ so that posts, profiles and pages can be automatically retrieved.
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2023-06-16 |
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r-patchplot
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Functions to generate scatterplots with images patches instead of usual glyphs, with associated utilities.
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2023-06-16 |
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r-powerfulmaxeigenpair
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An implementation for using powerful algorithm to compute the maximal eigenpair of Hermitizable tridiagonal matrices in R. It provides two algorithms to find the maximal and the next to maximal eigenpairs under the tridiagonal matrix. Besides, it also provides two auxiliary algorithms to generate tridiagonal matrix and solve the linear equation by Thomas algorithm. Several examples are included in the vignettes to illustrate the usage of the functions.
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2023-06-16 |
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r-popdemog
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Plot demographic graphs for single/multiple populations from coalescent simulation program input. Currently, this package can support the 'ms', 'msHot', 'MaCS', 'msprime', 'SCRM', and 'Cosi2' simulation programs. It does not check the simulation program input for correctness, but assumes the simulation program input has been validated by the simulation program. More features will be added to this package in the future, please check the 'GitHub' page for the latest updates: <https://github.com/YingZhou001/POPdemog>.
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2023-06-16 |
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r-polidata
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This package provides easy access to various political data APIs directly from R. For example, you can access Google Civic Information API <https://developers.google.com/civic-information/> or Sunlight Congress API <https://sunlightlabs.github.io/congress/> for US Congress data, and POPONG API <http://data.popong.com/> for South Korea National Assembly data.
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2023-06-16 |
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r-plus
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Efficient procedures for fitting an entire regression sequences with different model types.
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2023-06-16 |
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r-partitionmap
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Low-dimensional embedding, using Random Forests for multiclass classification
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2023-06-16 |
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r-nsrr
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Allows users to access data from the National Sleep Research Resource ('NSRR') <https://sleepdata.org/>.
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2023-06-16 |
