|
r-alphavantager
|
public |
Alpha Vantage has free historical financial information. All you need to do is get a free API key at <https://www.alphavantage.co>. Then you can use the R interface to retrieve free equity information. Refer to the Alpha Vantage website for more information.
|
2025-04-22 |
|
r-alr4
|
public |
Datasets to Accompany S. Weisberg (2014, ISBN: 978-1-118-38608-8), "Applied Linear Regression," 4th edition. Many data files in this package are included in the `alr3` package as well, so only one of them should be used.
|
2025-04-22 |
|
r-aleplot
|
public |
Visualizes the main effects of individual predictor variables and their second-order interaction effects in black-box supervised learning models. The package creates either Accumulated Local Effects (ALE) plots and/or Partial Dependence (PD) plots, given a fitted supervised learning model.
|
2025-04-22 |
|
r-airgrteaching
|
public |
Add-on package to the 'airGR' package that simplifies its use and is aimed at being used for teaching hydrology. The package provides 1) three functions that allow to complete very simply a hydrological modelling exercise 2) plotting functions to help students to explore observed data and to interpret the results of calibration and simulation of the GR ('Génie rural') models 3) a 'Shiny' graphical interface that allows for displaying the impact of model parameters on hydrographs and models internal variables.
|
2025-04-22 |
|
r-airr
|
public |
Schema definitions and read, write and validation tools for data formatted in accordance with the AIRR Data Representation schemas defined by the AIRR Community <http://docs.airr-community.org>.
|
2025-04-22 |
|
r-aiccmodavg
|
public |
Functions to implement model selection and multimodel inference based on Akaike's information criterion (AIC) and the second-order AIC (AICc), as well as their quasi-likelihood counterparts (QAIC, QAICc) from various model object classes. The package implements classic model averaging for a given parameter of interest or predicted values, as well as a shrinkage version of model averaging parameter estimates or effect sizes. The package includes diagnostics and goodness-of-fit statistics for certain model types including those of 'unmarkedFit' classes estimating demographic parameters after accounting for imperfect detection probabilities. Some functions also allow the creation of model selection tables for Bayesian models of the 'bugs', 'rjags', and 'jagsUI' classes. Functions also implement model selection using BIC. Objects following model selection and multimodel inference can be formatted to LaTeX using 'xtable' methods included in the package.
|
2025-04-22 |
|
r-airgriwrm
|
public |
Semi-distributed Precipitation-Runoff Modelling based on 'airGR' package models integrating human infrastructures and their managements.
|
2025-04-22 |
|
r-aid
|
public |
Performs Box-Cox power transformation for different purposes, graphical approaches, assesses the success of the transformation via tests and plots, computes mean and confidence interval for back transformed data.
|
2025-04-22 |
|
r-agua
|
public |
Create and evaluate models using 'tidymodels' and 'h2o' <https://h2o.ai/>. The package enables users to specify 'h2o' as an engine for several modeling methods.
|
2025-04-22 |
|
r-agricolae
|
public |
Original idea was presented in the thesis "A statistical analysis tool for agricultural research" to obtain the degree of Master on science, National Engineering University (UNI), Lima-Peru. Some experimental data for the examples come from the CIP and others research. Agricolae offers extensive functionality on experimental design especially for agricultural and plant breeding experiments, which can also be useful for other purposes. It supports planning of lattice, Alpha, Cyclic, Complete Block, Latin Square, Graeco-Latin Squares, augmented block, factorial, split and strip plot designs. There are also various analysis facilities for experimental data, e.g. treatment comparison procedures and several non-parametric tests comparison, biodiversity indexes and consensus cluster.
|
2025-04-22 |
|
r-agror
|
public |
Performs the analysis of completely randomized experimental designs (CRD), randomized blocks (RBD) and Latin square (LSD), experiments in double and triple factorial scheme (in CRD and RBD), experiments in subdivided plot scheme (in CRD and RBD), subdivided and joint analysis of experiments in CRD and RBD, linear regression analysis, test for two samples. The package performs analysis of variance, ANOVA assumptions and multiple comparison test of means or regression, according to Pimentel-Gomes (2009, ISBN: 978-85-7133-055-9), nonparametric test (Conover, 1999, ISBN: 0471160687), test for two samples, joint analysis of experiments according to Ferreira (2018, ISBN: 978-85-7269-566-4) and generalized linear model (glm) for binomial and Poisson family in CRD and RBD (Carvalho, FJ (2019), <doi:10.14393/ufu.te.2019.1244>). It can also be used to obtain descriptive measures and graphics, in addition to correlations and creative graphics used in agricultural sciences (Agronomy, Zootechnics, Food Science and related areas).
|
2025-04-22 |
|
r-admiraldev
|
public |
Utility functions to check data, variables and conditions for functions used in 'admiral' and 'admiral' extension packages. Additional utility helper functions to assist developers with maintaining documentation, testing and general upkeep of 'admiral' and 'admiral' extension packages.
|
2025-04-22 |
|
r-admiral
|
public |
A toolbox for programming Clinical Data Interchange Standards Consortium (CDISC) compliant Analysis Data Model (ADaM) datasets in R. ADaM datasets are a mandatory part of any New Drug or Biologics License Application submitted to the United States Food and Drug Administration (FDA). Analysis derivations are implemented in accordance with the "Analysis Data Model Implementation Guide" (CDISC Analysis Data Model Team, 2021, <https://www.cdisc.org/standards/foundational/adam>).
|
2025-04-22 |
|
r-activity
|
public |
Provides functions to express clock time data relative to anchor points (typically solar); fit kernel density functions to animal activity time data; plot activity distributions; quantify overall levels of activity; statistically compare activity metrics through bootstrapping; evaluate variation in linear variables with time (or other circular variables).
|
2025-04-22 |
|
r-adabag
|
public |
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. Version 5.0 includes the Boosting and Bagging algorithms for label ranking (Albano, Sciandra and Plaia, 2023).
|
2025-04-22 |
|
r-activepathways
|
public |
Framework for analysing multiple omics datasets in the context of molecular pathways, biological processes and other types of gene sets. The package uses p-value merging to combine gene- or protein-level signals, followed by ranked hypergeometric tests to determine enriched pathways and processes. This approach allows researchers to interpret a series of omics datasets in the context of known biology and gene function, and discover associations that are only apparent when several datasets are combined. The first version of the package is part of the following publication: Integrative Pathway Enrichment Analysis of Multivariate Omics Data. Paczkowska M^, Barenboim J^, Sintupisut N, Fox NS, Zhu H, Abd-Rabbo D, Mee MW, Boutros PC, PCAWG Drivers and Functional Interpretation Working Group; Reimand J, PCAWG Consortium. Nature Communications (2020) <doi:10.1038/s41467-019-13983-9>.
|
2025-04-22 |
|
r-accessibility
|
public |
A set of fast and convenient functions to calculate multiple transport accessibility measures. Given a pre-computed travel cost matrix and a land use dataset (containing the location of jobs, healthcare and population, for example), the package allows one to calculate active and passive accessibility levels using multiple accessibility measures, such as: cumulative opportunities (using either travel cost cutoffs or intervals), minimum travel cost to closest N number of activities, gravity-based (with different decay functions) and different floating catchment area methods.
|
2025-04-22 |
|
r-acid
|
public |
Functions for the analysis of income distributions for subgroups of the population as defined by a set of variables like age, gender, region, etc. This entails a Kolmogorov-Smirnov test for a mixture distribution as well as functions for moments, inequality measures, entropy measures and polarisation measures of income distributions. This package thus aides the analysis of income inequality by offering tools for the exploratory analysis of income distributions at the disaggregated level.
|
2025-04-22 |
|
r-academictwitter
|
public |
Package to query the Twitter Academic Research Product Track, providing access to full-archive search and other v2 API endpoints. Functions are written with academic research in mind. They provide flexibility in how the user wishes to store collected data, and encourage regular storage of data to mitigate loss when collecting large volumes of tweets. They also provide workarounds to manage and reshape the format in which data is provided on the client side.
|
2025-04-22 |
|
r-abcanalysis
|
public |
For a given data set, the package provides a novel method of computing precise limits to acquire subsets which are easily interpreted. Closely related to the Lorenz curve, the ABC curve visualizes the data by graphically representing the cumulative distribution function. Based on an ABC analysis the algorithm calculates, with the help of the ABC curve, the optimal limits by exploiting the mathematical properties pertaining to distribution of analyzed items. The data containing positive values is divided into three disjoint subsets A, B and C, with subset A comprising very profitable values, i.e. largest data values ("the important few"), subset B comprising values where the yield equals to the effort required to obtain it, and the subset C comprising of non-profitable values, i.e., the smallest data sets ("the trivial many"). Package is based on "Computed ABC Analysis for rational Selection of most informative Variables in multivariate Data", PLoS One. Ultsch. A., Lotsch J. (2015) <DOI:10.1371/journal.pone.0129767>.
|
2025-04-22 |
|
r-abd
|
public |
The abd package contains data sets and sample code for The Analysis of Biological Data by Michael Whitlock and Dolph Schluter (2009; Roberts & Company Publishers).
|
2025-04-22 |
|
r-abc
|
public |
Implements several ABC algorithms for performing parameter estimation, model selection, and goodness-of-fit. Cross-validation tools are also available for measuring the accuracy of ABC estimates, and to calculate the misclassification probabilities of different models.
|
2025-04-22 |
|
r-a3
|
public |
Supplies tools for tabulating and analyzing the results of predictive models. The methods employed are applicable to virtually any predictive model and make comparisons between different methodologies straightforward.
|
2025-04-22 |
|
r-yardstick
|
public |
Tidy tools for quantifying how well model fits to a data set such as confusion matrices, class probability curve summaries, and regression metrics (e.g., RMSE).
|
2025-04-22 |
|
r-wordspace
|
public |
An interactive laboratory for research on distributional semantic models ('DSM', see <https://en.wikipedia.org/wiki/Distributional_semantics> for more information).
|
2025-04-22 |
