nnsrl
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| Name | Latest Version | Summary | Updated | License |
|---|
| r-acepack | 1.4.1 | Two nonparametric methods for multiple regression transform selection are provided. The first, Alternative Conditional Expectations (ACE), is an algorithm to find the fixed point of maximal correlation, i.e. it finds a set of transformed response variables that maximizes R^2 using smoothing functions [see Breiman, L., and J.H. Friedman. 1985. "Estimating Optimal Transformations for Multiple Regression and Correlation". Journal of the American Statistical Association. 80:580-598. <doi:10.1080/01621459.1985.10478157>]. Also included is the Additivity Variance Stabilization (AVAS) method which works better than ACE when correlation is low [see Tibshirani, R.. 1986. "Estimating Transformations for Regression via Additivity and Variance Stabilization". Journal of the American Statistical Association. 83:394-405. <doi:10.1080/01621459.1988.10478610>]. A good introduction to these two methods is in chapter 16 of Frank Harrel's "Regression Modeling Strategies" in the Springer Series in Statistics. | Mar 25, 2025 | MIT + file LICENSE |
| r-ada | 2.0_5 | Performs discrete, real, and gentle boost under both exponential and logistic loss on a given data set. The package ada provides a straightforward, well-documented, and broad boosting routine for classification, ideally suited for small to moderate-sized data sets. | Mar 25, 2025 | GPL |
| r-adabag | 4.2 | It implements Freund and Schapire's Adaboost.M1 algorithm and Breiman's Bagging algorithm using classification trees as individual classifiers. Once these classifiers have been trained, they can be used to predict on new data. Also, cross validation estimation of the error can be done. Since version 2.0 the function margins() is available to calculate the margins for these classifiers. Also a higher flexibility is achieved giving access to the rpart.control() argument of 'rpart'. Four important new features were introduced on version 3.0, AdaBoost-SAMME (Zhu et al., 2009) is implemented and a new function errorevol() shows the error of the ensembles as a function of the number of iterations. In addition, the ensembles can be pruned using the option 'newmfinal' in the predict.bagging() and predict.boosting() functions and the posterior probability of each class for observations can be obtained. Version 3.1 modifies the relative importance measure to take into account the gain of the Gini index given by a variable in each tree and the weights of these trees. Version 4.0 includes the margin-based ordered aggregation for Bagging pruning (Guo and Boukir, 2013) and a function to auto prune the 'rpart' tree. Moreover, three new plots are also available importanceplot(), plot.errorevol() and plot.margins(). Version 4.1 allows to predict on unlabeled data. Version 4.2 includes the parallel computation option for some of the functions. | Mar 25, 2025 | GPL (>= 2) |
| r-ade4 | 1.7_13 | Tools for multivariate data analysis. Several methods are provided for the analysis (i.e., ordination) of one-table (e.g., principal component analysis, correspondence analysis), two-table (e.g., coinertia analysis, redundancy analysis), three-table (e.g., RLQ analysis) and K-table (e.g., STATIS, multiple coinertia analysis). The philosophy of the package is described in Dray and Dufour (2007) <doi:10.18637/jss.v022.i04>. | Mar 25, 2025 | GPL (>= 2) |
| r-aplpack | 1.3.2 | Some functions for drawing some special plots: The function 'bagplot' plots a bagplot, 'faces' plots chernoff faces, 'iconplot' plots a representation of a frequency table or a data matrix, 'plothulls' plots hulls of a bivariate data set, 'plotsummary' plots a graphical summary of a data set, 'puticon' adds icons to a plot, 'skyline.hist' combines several histograms of a one dimensional data set in one plot, 'slider' functions supports some interactive graphics, 'spin3R' helps an inspection of a 3-dim point cloud, 'stem.leaf' plots a stem and leaf plot, 'stem.leaf.backback' plots back-to-back versions of stem and leaf plot. | Mar 25, 2025 | GPL (>= 2) |
| r-arm | 1.10_1 | Functions to accompany A. Gelman and J. Hill, Data Analysis Using Regression and Multilevel/Hierarchical Models, Cambridge University Press, 2007. | Mar 25, 2025 | GPL (>= 3) |
| r-arules | 1.6_2 | Provides the infrastructure for representing, manipulating and analyzing transaction data and patterns (frequent itemsets and association rules). Also provides C implementations of the association mining algorithms Apriori and Eclat. | Mar 25, 2025 | GPL-3 |
| r-arulesviz | 1.3_2 | Extends package 'arules' with various visualization techniques for association rules and itemsets. The package also includes several interactive visualizations for rule exploration. | Mar 25, 2025 | GPL-3 |
| r-backports | 1.1.3 | Functions introduced or changed since R v3.0.0 are re-implemented in this package. The backports are conditionally exported in order to let R resolve the function name to either the implemented backport, or the respective base version, if available. Package developers can make use of new functions or arguments by selectively importing specific backports to support older installations. | Mar 25, 2025 | GPL-2 |
| r-bayesplot | 1.6.0 | Plotting functions for posterior analysis, model checking, and MCMC diagnostics. The package is designed not only to provide convenient functionality for users, but also a common set of functions that can be easily used by developers working on a variety of R packages for Bayesian modeling, particularly (but not exclusively) packages interfacing with 'Stan'. | Mar 25, 2025 | GPL (>= 3) |
| r-bbmisc | 1.11 | Miscellaneous helper functions for and from B. Bischl and some other guys, mainly for package development. | Mar 25, 2025 | BSD_2_clause + file LICENSE |
| r-bibtex | 0.4.2 | Utility to parse a bibtex file. | Mar 25, 2025 | GPL (>= 2) |
| r-bit | 1.1_14 | True boolean datatype (no NAs), coercion from and to logicals, integers and integer subscripts; fast boolean operators and fast summary statistics. With 'bit' vectors you can store true binary booleans {FALSE,TRUE} at the expense of 1 bit only, on a 32 bit architecture this means factor 32 less RAM and ~ factor 32 more speed on boolean operations. Due to overhead of R calls, actual speed gain depends on the size of the vector: expect gains for vectors of size > 10000 elements. Even for one-time boolean operations it can pay-off to convert to bit, the pay-off is obvious, when such components are used more than once. Reading from and writing to bit is approximately as fast as accessing standard logicals - mostly due to R's time for memory allocation. The package allows to work with pre-allocated memory for return values by calling .Call() directly: when evaluating the speed of C-access with pre-allocated vector memory, coping from bit to logical requires only 70% of the time for copying from logical to logical; and copying from logical to bit comes at a performance penalty of 150%. the package now contains further classes for representing logical selections: 'bitwhich' for very skewed selections and 'ri' for selecting ranges of values for chunked processing. All three index classes can be used for subsetting 'ff' objects (ff-2.1-0 and higher). | Mar 25, 2025 | GPL-2 |
| r-blme | 1.0_4 | Maximum a posteriori estimation for linear and generalized linear mixed-effects models in a Bayesian setting. Extends 'lme4' by Douglas Bates, Martin Maechler, Ben Bolker, and Steve Walker. | Mar 25, 2025 | GPL (>= 2) |
| r-bmp | 0.3 | Reads Windows BMP format images. Currently limited to 8 bit greyscale images and 24,32 bit (A)RGB images. Pure R implementation without external dependencies. | Mar 25, 2025 | GPL (>= 2) |
| r-brew | 1.0_6 | brew implements a templating framework for mixing text and R code for report generation. brew template syntax is similar to PHP, Ruby's erb module, Java Server Pages, and Python's psp module. | Mar 25, 2025 | GPL-2 |
| r-bridgesampling | 0.6_0 | Provides functions for estimating marginal likelihoods, Bayes factors, posterior model probabilities, and normalizing constants in general, via different versions of bridge sampling (Meng & Wong, 1996, <http://www3.stat.sinica.edu.tw/statistica/j6n4/j6n43/j6n43.htm>). | Mar 25, 2025 | GPL (>= 2) |
| r-brms | 2.7.0 | Fit Bayesian generalized (non-)linear multivariate multilevel models using 'Stan' for full Bayesian inference. A wide range of distributions and link functions are supported, allowing users to fit -- among others -- linear, robust linear, count data, survival, response times, ordinal, zero-inflated, hurdle, and even self-defined mixture models all in a multilevel context. Further modeling options include non-linear and smooth terms, auto-correlation structures, censored data, meta-analytic standard errors, and quite a few more. In addition, all parameters of the response distribution can be predicted in order to perform distributional regression. Prior specifications are flexible and explicitly encourage users to apply prior distributions that actually reflect their beliefs. Model fit can easily be assessed and compared with posterior predictive checks and leave-one-out cross-validation. References: B端rkner (2017) <doi:10.18637/jss.v080.i01>; Carpenter et al. (2017) <doi:10.18637/jss.v076.i01>. | Mar 25, 2025 | GPL (>= 3) |
| r-brnn | 0.7 | Bayesian regularization for feed-forward neural networks. | Mar 25, 2025 | GPL-2 |
| r-brobdingnag | 1.2_6 | Handles very large numbers in R. Real numbers are held using their natural logarithms, plus a logical flag indicating sign. The package includes a vignette that gives a step-by-step introduction to using S4 methods. | Mar 25, 2025 | GPL |
| r-broom | 0.5.1 | Summarizes key information about statistical objects in tidy tibbles. This makes it easy to report results, create plots and consistently work with large numbers of models at once. Broom provides three verbs that each provide different types of information about a model. tidy() summarizes information about model components such as coefficients of a regression. glance() reports information about an entire model, such as goodness of fit measures like AIC and BIC. augment() adds information about individual observations to a dataset, such as fitted values or influence measures. | Mar 25, 2025 | MIT + file LICENSE |
| r-bsda | 1.2.0 | Data sets for book "Basic Statistics and Data Analysis" by Larry J. Kitchens. | Mar 25, 2025 | GPL (>= 2) |
| r-bst | 0.3_15 | Functional gradient descent algorithm for a variety of convex and non-convex loss functions, for both classical and robust regression and classification problems. See Wang (2011) <doi:10.2202/1557-4679.1304>, Wang (2012) <doi:10.3414/ME11-02-0020>, Wang (2018) <doi:10.1080/10618600.2018.1424635>, Wang (2018) <doi:10.1214/18-EJS1404>. | Mar 25, 2025 | GPL (>= 2) |
| r-c50 | 0.1.2 | C5.0 decision trees and rule-based models for pattern recognition that extend the work of Quinlan (1993, ISBN:1-55860-238-0). | Mar 25, 2025 | GPL-3 |
| r-callr | 3.1.1 | It is sometimes useful to perform a computation in a separate R process, without affecting the current R process at all. This packages does exactly that. | Mar 25, 2025 | MIT + file LICENSE |