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r-dfcrm
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public |
Provides functions to run the CRM and TITE-CRM in phase I trials and calibration tools for trial planning purposes.
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2024-01-16 |
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r-dfcompare
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public |
Compares two dataframes with a common key and returns the delta records. The package will return three dataframes that contain the added, changed, and deleted records.
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2024-01-16 |
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r-dfadjust
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public |
Computes small-sample degrees of freedom adjustment for heteroskedasticity robust standard errors, and for clustered standard errors in linear regression. See Imbens and Kolesár (2016) <doi:10.1162/REST_a_00552> for a discussion of these adjustments.
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2024-01-16 |
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r-devrate
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public |
A set of functions to quantify the relationship between development rate and temperature and to build phenological models. The package comprises a set of models and estimated parameters borrowed from a literature review in ectotherms. The methods and literature review are described in Rebaudo et al. (2018) <doi:10.1111/2041-210X.12935>, Rebaudo and Rabhi (2018) <doi:10.1111/eea.12693>, and Regnier et al. (2021) <doi:10.1093/ee/nvab115>. An example can be found in Rebaudo et al. (2017) <doi:10.1007/s13355-017-0480-5>.
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2024-01-16 |
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r-devtools
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None |
Collection of package development tools.
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2024-01-16 |
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r-devore7
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public |
Data sets and sample analyses from Jay L. Devore (2008), "Probability and Statistics for Engineering and the Sciences (7th ed)", Thomson.
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2024-01-16 |
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r-devfunc
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public |
A concise check of the format of one or multiple input arguments (data type, length or value) is provided. Since multiple input arguments can be tested simultaneously, a lengthly list of checks at the beginning of your function can be avoided, hereby enhancing the readability and maintainability of your code.
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2024-01-16 |
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r-detpack
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public |
Density estimation for possibly large data sets and conditional/unconditional random number generation or bootstrapping with distribution element trees. The function 'det.construct' translates a dataset into a distribution element tree. To evaluate the probability density based on a previously computed tree at arbitrary query points, the function 'det.query' is available. The functions 'det1' and 'det2' provide density estimation and plotting for one- and two-dimensional datasets. Conditional/unconditional smooth bootstrapping from an available distribution element tree can be performed by 'det.rnd'. For more details on distribution element trees, see: Meyer, D.W. (2016) <arXiv:1610.00345> or Meyer, D.W., Statistics and Computing (2017) <doi:10.1007/s11222-017-9751-9> and Meyer, D.W. (2017) <arXiv:1711.04632> or Meyer, D.W., Journal of Computational and Graphical Statistics (2018) <doi:10.1080/10618600.2018.1482768>.
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2024-01-16 |
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r-desplot
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public |
A function for plotting maps of agricultural field experiments that are laid out in grids. See Ryder (1981) <doi:10.1017/S0014479700011601>.
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2024-01-16 |
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r-descriptio
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public |
Description of statistical associations between two variables : measures of local and global association between variables (phi, Cramér V, correlations, eta-squared, Goodman and Kruskal tau, permutation tests, etc.), multiple graphical representations of the associations between two variables (using 'ggplot2') and weighted statistics.
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2024-01-16 |
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r-detector
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public |
Allows users to quickly and easily detect data containing Personally Identifiable Information (PII) through convenience functions.
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2024-01-16 |
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r-designmatch
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public |
Includes functions for the construction of matched samples that are balanced and representative by design. Among others, these functions can be used for matching in observational studies with treated and control units, with cases and controls, in related settings with instrumental variables, and in discontinuity designs. Also, they can be used for the design of randomized experiments, for example, for matching before randomization. By default, 'designmatch' uses the 'highs' optimization solver, but its performance is greatly enhanced by the 'Gurobi' optimization solver and its associated R interface. For their installation, please follow the instructions at <https://www.gurobi.com/documentation/quickstart.html> and <https://www.gurobi.com/documentation/7.0/refman/r_api_overview.html>. We have also included directions in the gurobi_installation file in the inst folder.
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2024-01-16 |
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r-descriptr
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public |
Generate descriptive statistics such as measures of location, dispersion, frequency tables, cross tables, group summaries and multiple one/two way tables.
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2024-01-16 |
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r-desirability
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public |
S3 classes for multivariate optimization using the desirability function by Derringer and Suich (1980).
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2024-01-16 |
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r-desir
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public |
Functions for (1) ranking, selecting, and prioritising genes, proteins, and metabolites from high dimensional biology experiments, (2) multivariate hit calling in high content screens, and (3) combining data from diverse sources.
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2024-01-16 |
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r-dendextend
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public |
Offers a set of functions for extending 'dendrogram' objects in R, letting you visualize and compare trees of 'hierarchical clusterings'. You can (1) Adjust a tree's graphical parameters - the color, size, type, etc of its branches, nodes and labels. (2) Visually and statistically compare different 'dendrograms' to one another.
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2024-01-16 |
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r-describer
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public |
Allows users to quickly and easily describe data using common descriptive statistics.
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2024-01-16 |
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r-derezende.ferreira
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public |
Modeling the zero coupon yield curve using the dynamic De Rezende and Ferreira (2011) <doi:10.1002/for.1256> five factor model with variable or fixed decaying parameters. For explanatory purposes, the package also includes various short datasets of interest rates for the BRICS countries.
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2024-01-16 |
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r-descomponer
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public |
Decompose a time series into seasonal, trend and irregular components using transformations to amplitude-frequency domain.
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2024-01-16 |
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r-desc
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public |
Tools to read, write, create, and manipulate DESCRIPTION files. It is intended for packages that create or manipulate other packages.
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2024-01-16 |
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r-des
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public |
Discrete event simulation (DES) involves modeling of systems having discrete, i.e. abrupt, state changes. For instance, when a job arrives to a queue, the queue length abruptly increases by 1. This package is an R implementation of the event-oriented approach to DES; see the tutorial in Matloff (2008) <http://heather.cs.ucdavis.edu/~matloff/156/PLN/DESimIntro.pdf>.
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2024-01-16 |
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r-deriv
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public |
R-based solution for symbolic differentiation. It admits user-defined function as well as function substitution in arguments of functions to be differentiated. Some symbolic simplification is part of the work.
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2024-01-16 |
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r-derivmkts
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public |
A set of pricing and expository functions that should be useful in teaching a course on financial derivatives.
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2024-01-16 |
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r-depthtools
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public |
Implementation of different statistical tools for the description and analysis of gene expression data based on the concept of data depth, namely, the scale curves for visualizing the dispersion of one or various groups of samples (e.g. types of tumors), a rank test to decide whether two groups of samples come from a single distribution and two methods of supervised classification techniques, the DS and TAD methods. All these techniques are based on the Modified Band Depth, which is a recent notion of depth with a low computational cost, what renders it very appropriate for high dimensional data such as gene expression data.
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2024-01-16 |
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r-depth.plot
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public |
Could be used to obtain spatial depths, spatial ranks and outliers of multivariate random variables. Could also be used to visualize DD-plots (a multivariate generalization of QQ-plots).
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2024-01-16 |
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r-deplogo
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public |
Plots dependency logos from a set of aligned input sequences.
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2024-01-16 |
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r-demography
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public |
Functions for demographic analysis including lifetable calculations; Lee-Carter modelling; functional data analysis of mortality rates, fertility rates, net migration numbers; and stochastic population forecasting.
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2024-01-16 |
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r-depend.truncation
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public |
Estimation and testing methods for dependently truncated data. Semi-parametric methods are based on Emura et al. (2011)<Stat Sinica 21:349-67>, Emura & Wang (2012)<doi:10.1016/j.jmva.2012.03.012>, and Emura & Murotani (2015)<doi:10.1007/s11749-015-0432-8>. Parametric approaches are based on Emura & Konno (2012)<doi:10.1007/s00362-014-0626-2> and Emura & Pan (2017)<doi:10.1007/s00362-017-0947-z>. A regression approach is based on Emura & Wang (2016)<doi:10.1007/s10463-015-0526-9>. Quasi-independence tests are based on Emura & Wang (2010)<doi:10.1016/j.jmva.2009.07.006>. Right-truncated data for Japanese male centenarians are given by Emura & Murotani (2015)<doi:10.1007/s11749-015-0432-8>.
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2024-01-16 |
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r-denvax
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public |
Provides the mathematical model described by "Serostatus Testing & Dengue Vaccine Cost-Benefit Thresholds" in <doi:10.1098/rsif.2019.0234>. Using the functions in the package, that analysis can be repeated using sample life histories, either synthesized from local seroprevalence data using other functions in this package (as in the manuscript) or from some other source. The package provides a vignette which walks through the analysis in the publication, as well as a function to generate a project skeleton for such an analysis.
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2024-01-16 |
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r-denstrip
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public |
Graphical methods for compactly illustrating probability distributions, including density strips, density regions, sectioned density plots and varying width strips.
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2024-01-16 |
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r-densratio
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public |
Density ratio estimation. The estimated density ratio function can be used in many applications such as anomaly detection, change-point detection, covariate shift adaptation. The implemented methods are uLSIF (Hido et al. (2011) <doi:10.1007/s10115-010-0283-2>), RuLSIF (Yamada et al. (2011) <doi:10.1162/NECO_a_00442>), and KLIEP (Sugiyama et al. (2007) <doi:10.1007/s10463-008-0197-x>).
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2024-01-16 |
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r-densparcorr
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public |
Provide a Dens-based method for estimating functional connection in large scale brain networks using partial correlation.
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2024-01-16 |
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r-denseflmm
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public |
Estimation of functional linear mixed models for densely sampled data based on functional principal component analysis.
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2024-01-16 |
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r-dendsort
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public |
An implementation of functions to optimize ordering of nodes in a dendrogram, without affecting the meaning of the dendrogram. A dendrogram can be sorted based on the average distance of subtrees, or based on the smallest distance value. These sorting methods improve readability and interpretability of tree structure, especially for tasks such as comparison of different distance measures or linkage types and identification of tight clusters and outliers. As a result, it also introduces more meaningful reordering for a coupled heatmap visualization. This method is described in "dendsort: modular leaf ordering methods for dendrogram representations in R", F1000Research 2014, 3: 177 <doi:10.12688/f1000research.4784.1>.
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2024-01-16 |
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r-denoiseq
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public |
Given count data from two conditions, it determines which transcripts are differentially expressed across the two conditions using Bayesian inference of the parameters of a bottom-up model for PCR amplification. This model is developed in Ndifon Wilfred, Hilah Gal, Eric Shifrut, Rina Aharoni, Nissan Yissachar, Nir Waysbort, Shlomit Reich Zeliger, Ruth Arnon, and Nir Friedman (2012), <http://www.pnas.org/content/109/39/15865.full>, and results in a distribution for the counts that is a superposition of the binomial and negative binomial distribution.
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2024-01-16 |
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r-dendroextras
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public |
Provides extra functions to manipulate dendrograms that build on the base functions provided by the 'stats' package. The main functionality it is designed to add is the ability to colour all the edges in an object of class 'dendrogram' according to cluster membership i.e. each subtree is coloured, not just the terminal leaves. In addition it provides some utility functions to cut 'dendrogram' and 'hclust' objects and to set/get labels.
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2024-01-16 |
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r-deeplr
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public |
A wrapper for the 'DeepL' Pro API <https://www.deepl.com/docs-api>, a web service for translating texts between different languages. A DeepL API developer account is required to use the service (see <https://www.deepl.com/pro#developer).
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2024-01-16 |
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r-deducer
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public |
An intuitive, cross-platform graphical data analysis system. It uses menus and dialogs to guide the user efficiently through the data manipulation and analysis process, and has an excel like spreadsheet for easy data frame visualization and editing. Deducer works best when used with the Java based R GUI JGR, but the dialogs can be called from the command line. Dialogs have also been integrated into the Windows Rgui.
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2024-01-16 |
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r-demova
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public |
Tool for the development of multi-linear QSPR/QSAR models (Quantitative structure-property/activity relationship). Theses models are used in chemistry, biology and pharmacy to find a relationship between the structure of a molecule and its property (such as activity, toxicology but also physical properties). The various functions of this package allows: selection of descriptors based of variances, intercorrelation and user expertise; selection of the best multi-linear regression in terms of correlation and robustness; methods of internal validation (Leave-One-Out, Leave-Many-Out, Y-scrambling) and external using test sets.
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2024-01-16 |
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r-demokde
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public |
Demonstration code showing how (univariate) kernel density estimates are computed, at least conceptually, and allowing users to experiment with different kernels, should they so wish. The method used follows directly the definition, but gains efficiency by replacing the observations by frequencies in a very fine grid covering the sample range. A canonical reference is B. W. Silverman, (1998) <doi: 10.1201/9781315140919>. NOTE: the density function in the stats package uses a more sophisticated method based on the fast Fourier transform and that function should be used if computational efficiency is a prime consideration.
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2024-01-16 |
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r-demogr
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public |
Construction and analysis of matrix population models in R.
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2024-01-16 |
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r-deming
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public |
Generalized Deming regression, Theil-Sen regression and Passing-Bablock regression functions.
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2024-01-16 |
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r-deltd
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public |
A collection of asymmetrical kernels belong to lifetime distributions for kernel density estimation is presented. Mean Squared Errors (MSE) are calculated for estimated curves. For this purpose, R functions allow the distribution to be Gamma, Exponential or Weibull. For details see Chen (2000a,b), Jin and Kawczak (2003) and Salha et al. (2014) <doi:10.12988/pms.2014.4616>.
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2024-01-16 |
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r-deltaplotr
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public |
The deltaPlotR package implements Angoff's Delta Plot method to detect dichotomous DIF. Several detection thresholds are included, either from multivariate normality assumption or by prior determination. Item purification is supported (Magis and Facon (2014) <doi:10.18637/jss.v059.c01>).
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2024-01-16 |
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r-delta
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public |
Measure of agreement delta was originally by MartÃn & Femia (2004) <DOI:10.1348/000711004849268>. Since then has been considered as agreement measure for different fields, since their behavior is usually better than the usual kappa index by Cohen (1960) <DOI:10.1177/001316446002000104>. The main issue with delta is that can not be computed by hand contrary to kappa. The current algorithm is based on the Version 5 of the delta windows program that can be found on <https://www.ugr.es/~bioest/software/delta/cmd.php?seccion=downloads>.
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2024-01-16 |
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r-default
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public |
A simple syntax to change the default values for function arguments, whether they are in packages or defined locally.
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2024-01-16 |
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r-deepnn
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public |
Implementation of some Deep Learning methods. Includes multilayer perceptron, different activation functions, regularisation strategies, stochastic gradient descent and dropout. Thanks go to the following references for helping to inspire and develop the package: Ian Goodfellow, Yoshua Bengio, Aaron Courville, Francis Bach (2016, ISBN:978-0262035613) Deep Learning. Terrence J. Sejnowski (2018, ISBN:978-0262038034) The Deep Learning Revolution. Grant Sanderson (3brown1blue) <https://www.youtube.com/playlist?list=PLZHQObOWTQDNU6R1_67000Dx_ZCJB-3pi> Neural Networks YouTube playlist. Michael A. Nielsen <http://neuralnetworksanddeeplearning.com/> Neural Networks and Deep Learning.
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2024-01-16 |
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r-declaredesign
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public |
Researchers can characterize and learn about the properties of research designs before implementation using `DeclareDesign`. Ex ante declaration and diagnosis of designs can help researchers clarify the strengths and limitations of their designs and to improve their properties, and can help readers evaluate a research strategy prior to implementation and without access to results. It can also make it easier for designs to be shared, replicated, and critiqued.
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2024-01-16 |
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r-deepnet
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public |
Implement some deep learning architectures and neural network algorithms, including BP,RBM,DBN,Deep autoencoder and so on.
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2024-01-16 |
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r-ddoutlier
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public |
Outlier detection in multidimensional domains. Implementation of notable distance and density-based outlier algorithms. Allows users to identify local outliers by comparing observations to their nearest neighbors, reverse nearest neighbors, shared neighbors or natural neighbors. For distance-based approaches, see Knorr, M., & Ng, R. T. (1997) <doi:10.1145/782010.782021>, Angiulli, F., & Pizzuti, C. (2002) <doi:10.1007/3-540-45681-3_2>, Hautamaki, V., & Ismo, K. (2004) <doi:10.1109/ICPR.2004.1334558> and Zhang, K., Hutter, M. & Jin, H. (2009) <doi:10.1007/978-3-642-01307-2_84>. For density-based approaches, see Tang, J., Chen, Z., Fu, A. W. C., & Cheung, D. W. (2002) <doi:10.1007/3-540-47887-6_53>, Jin, W., Tung, A. K. H., Han, J., & Wang, W. (2006) <doi:10.1007/11731139_68>, Schubert, E., Zimek, A. & Kriegel, H-P. (2014) <doi:10.1137/1.9781611973440.63>, Latecki, L., Lazarevic, A. & Prokrajac, D. (2007) <doi:10.1007/978-3-540-73499-4_6>, Papadimitriou, S., Gibbons, P. B., & Faloutsos, C. (2003) <doi:10.1109/ICDE.2003.1260802>, Breunig, M. M., Kriegel, H.-P., Ng, R. T., & Sander, J. (2000) <doi:10.1145/342009.335388>, Kriegel, H.-P., Kröger, P., Schubert, E., & Zimek, A. (2009) <doi:10.1145/1645953.1646195>, Zhu, Q., Feng, Ji. & Huang, J. (2016) <doi:10.1016/j.patrec.2016.05.007>, Huang, J., Zhu, Q., Yang, L. & Feng, J. (2015) <doi:10.1016/j.knosys.2015.10.014>, Tang, B. & Haibo, He. (2017) <doi:10.1016/j.neucom.2017.02.039> and Gao, J., Hu, W., Zhang, X. & Wu, Ou. (2011) <doi:10.1007/978-3-642-20847-8_23>.
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2024-01-16 |
