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r-xmisc
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public |
This is Xiaobei's miscellaneous classes and functions useful when developing R packages, particularly for OOP using R Reference Class.
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2025-04-22 |
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r-nnlasso
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public |
Estimates of coefficients of lasso penalized linear regression and generalized linear models subject to non-negativity constraints on the parameters using multiplicative iterative algorithm. Entire regularization path for a sequence of lambda values can be obtained. Functions are available for creating plots of regularization path, cross validation and estimating coefficients at a given lambda value. There is also provision for obtaining standard error of coefficient estimates.
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2025-04-22 |
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r-tidymodels
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public |
The tidy modeling "verse" is a collection of packages for modeling and statistical analysis that share the underlying design philosophy, grammar, and data structures of the tidyverse.
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2025-04-22 |
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squashfs-tools
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public |
Tools for creating and unpacking squashfs filesystems
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2025-04-22 |
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go1.4-bootstrap_osx-64
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public |
The golang bootstrap compiler (use go for the full golang)
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2025-04-22 |
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go1.4-bootstrap_linux-64
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public |
The golang bootstrap compiler (use go for the full golang)
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2025-04-22 |
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r-groupedstats
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public |
Collection of functions to run statistical tests across all combinations of multiple grouping variables.
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2025-04-22 |
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r-kimisc
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public |
A collection of useful functions not found anywhere else, mainly for programming: Pretty intervals, generalized lagged differences, checking containment in an interval, and an alternative interface to assign().
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2025-04-22 |
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cw-eval
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public |
Evaluation metrics for Geospatial Machine Learning Challenges
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2025-04-22 |
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r-tmle
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public |
Targeted maximum likelihood estimation of point treatment effects (Targeted Maximum Likelihood Learning, The International Journal of Biostatistics, 2(1), 2006. This version automatically estimates the additive treatment effect among the treated (ATT) and among the controls (ATC). The tmle() function calculates the adjusted marginal difference in mean outcome associated with a binary point treatment, for continuous or binary outcomes. Relative risk and odds ratio estimates are also reported for binary outcomes. Missingness in the outcome is allowed, but not in treatment assignment or baseline covariate values. The population mean is calculated when there is missingness, and no variation in the treatment assignment. The tmleMSM() function estimates the parameters of a marginal structural model for a binary point treatment effect. Effect estimation stratified by a binary mediating variable is also available. An ID argument can be used to identify repeated measures. Default settings call 'SuperLearner' to estimate the Q and g portions of the likelihood, unless values or a user-supplied regression function are passed in as arguments.
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2025-04-22 |
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r-norm
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public |
An integrated set of functions for the analysis of multivariate normal datasets with missing values, including implementation of the EM algorithm, data augmentation, and multiple imputation.
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2025-04-22 |
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r-superlearner
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public |
Implements the super learner prediction method and contains a library of prediction algorithms to be used in the super learner.
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2025-04-22 |
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r-gmm
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public |
It is a complete suite to estimate models based on moment conditions. It includes the two step Generalized method of moments (Hansen 1982; <doi:10.2307/1912775>), the iterated GMM and continuous updated estimator (Hansen, Eaton and Yaron 1996; <doi:10.2307/1392442>) and several methods that belong to the Generalized Empirical Likelihood family of estimators (Smith 1997; <doi:10.1111/j.0013-0133.1997.174.x>, Kitamura 1997; <doi:10.1214/aos/1069362388>, Newey and Smith 2004; <doi:10.1111/j.1468-0262.2004.00482.x>, and Anatolyev 2005 <doi:10.1111/j.1468-0262.2005.00601.x>).
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2025-04-22 |
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r-ic.infer
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public |
Implements inequality constrained inference. This includes parameter estimation in normal (linear) models under linear equality and inequality constraints, as well as normal likelihood ratio tests involving inequality-constrained hypotheses. For inequality-constrained linear models, averaging over R-squared for different orderings of regressors is also included.
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2025-04-22 |
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r-naniar
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public |
Missing values are ubiquitous in data and need to be explored and handled in the initial stages of analysis. 'naniar' provides data structures and functions that facilitate the plotting of missing values and examination of imputations. This allows missing data dependencies to be explored with minimal deviation from the common work patterns of 'ggplot2' and tidy data.
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2025-04-22 |
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r-parcor
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public |
The package estimates the matrix of partial correlations based on different regularized regression methods: lasso, adaptive lasso, PLS, and Ridge Regression. In addition, the package provides model selection for lasso, adaptive lasso and Ridge regression based on cross-validation.
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2025-04-22 |
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r-simpleboot
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public |
Simple bootstrap routines.
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2025-04-22 |
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r-rbamtools
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public |
Provides an R interface to functions of the 'SAMtools' C-Library by Heng Li <http://www.htslib.org/>.
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2025-04-22 |
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r-kappalab
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public |
S4 tool box for capacity (or non-additive measure, fuzzy measure) and integral manipulation in a finite setting. It contains routines for handling various types of set functions such as games or capacities. It can be used to compute several non-additive integrals: the Choquet integral, the Sugeno integral, and the symmetric and asymmetric Choquet integrals. An analysis of capacities in terms of decision behavior can be performed through the computation of various indices such as the Shapley value, the interaction index, the orness degree, etc. The well-known Möbius transform, as well as other equivalent representations of set functions can also be computed. Kappalab further contains seven capacity identification routines: three least squares based approaches, a method based on linear programming, a maximum entropy like method based on variance minimization, a minimum distance approach and an unsupervised approach based on parametric entropies. The functions contained in Kappalab can for instance be used in the framework of multicriteria decision making or cooperative game theory.
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2025-04-22 |
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r-objectproperties
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Supports the definition of sets of properties on objects. Observers can listen to changes on individual properties or the set as a whole. The properties are meant to be fully self-describing. In support of this, there is a framework for defining enumerated types, as well as other bounded types, as S4 classes.
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2025-04-22 |
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r-visdat
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Create preliminary exploratory data visualisations of an entire dataset to identify problems or unexpected features using 'ggplot2'.
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2025-04-22 |
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r-ggalluvial
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Alluvial diagrams use x-splines, sometimes augmented with stacked histograms, to visualize multi-dimensional or repeated-measures data with categorical or ordinal variables. They can be viewed as simplified and standardized Sankey diagrams; see Riehmann, Hanfler, and Froehlich (2005) [doi:10.1109/INFVIS.2005.1532152](https://doi.org/10.1109/INFVIS.2005.1532152) and Rosvall and Bergstrom (2010) [doi:10.1371/journal.pone.0008694](https://doi.org/10.1371/journal.pone.0008694). This package provides ggplot2 layers to produce alluvial diagrams from tidy data.
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2025-04-22 |
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r-ineq
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public |
Inequality, concentration, and poverty measures. Lorenz curves (empirical and theoretical).
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2025-04-22 |
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r-modes
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Designed with a dual purpose of accurately estimating the mode (or modes) as well as characterizing the modality of data. The specific application area includes complex or mixture distributions particularly in a big data environment. The heterogeneous nature of (big) data may require deep introspective statistical and machine learning techniques, but these statistical tools often fail when applied without first understanding the data. In small datasets, this often isn't a big issue, but when dealing with large scale data analysis or big data thoroughly inspecting each dimension typically yields an O(n^n-1) problem. As such, dealing with big data require an alternative toolkit. This package not only identifies the mode or modes for various data types, it also provides a programmatic way of understanding the modality (i.e. unimodal, bimodal, etc.) of a dataset (whether it's big data or not). See <http://www.sdeevi.com/modes_package> for examples and discussion.
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2025-04-22 |
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xtensor-r
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public |
R bindings for xtensor, the C++ tensor algebra library
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2025-04-22 |
