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This package provides tools for linear, nonlinear and nonparametric regression and classification. Novel graphical methods for assessment of parametric models using nonparametric methods. One vs. All and All vs. All multiclass classification, optional class probabilities adjustment. Nonparametric regression (k-NN) for general dimension, local-linear option. Nonlinear regression with Eickert-White method for dealing with heteroscedasticity. Utilities for converting time series to rectangular form. Utilities for conversion between factors and indicator variables. Some code related to "Statistical Regression and Classification: from Linear Models to Machine Learning", N. Matloff, 2017, CRC, ISBN 9781498710916.
Describes a new procedure of reducing items in a rating scale called Rating Scale Reduction (RSR). The new stop criterion in RSR procedure is added (stop global max). The function order is replaced by sort.list.
Screens all .R', .Rmd', and .qmd files to extract the name of packages used in a project. This package detects packages called with library(foo)', require(foo)', foo::bar() and use("foo", "bar") and adds these dependencies in the DESCRIPTION file in the sections Depends, Imports, and Suggests.
This package provides methods for regression for functional data, including function-on-scalar, scalar-on-function, and function-on-function regression. Some of the functions are applicable to image data.
This package provides a method to download Department of Education College Scorecard data using the public API <https://collegescorecard.ed.gov/data/data-documentation/>. It is based on the dplyr model of piped commands to select and filter data in a single chained function call. An API key from the U.S. Department of Education is required.
Integrates population dynamics and dispersal into a mechanistic virtual species simulator. The package can be used to study the effects of environmental change on population growth and range shifts. It allows for simple and straightforward definition of population dynamics (including positive density dependence), extensive possibilities for defining dispersal kernels, and the ability to generate virtual ecologist data. Learn more about the rangr at <https://docs.ropensci.org/rangr/>. This work was supported by the National Science Centre, Poland, grant no. 2018/29/B/NZ8/00066 and the PoznaÅ Supercomputing and Networking Centre (grant no. pl0090-01).
Computing singular value decomposition with robustness is a challenging task. This package provides an implementation of computing robust SVD using density power divergence (<doi:10.48550/arXiv.2109.10680>). It combines the idea of robustness and efficiency in estimation based on a tuning parameter. It also provides utility functions to simulate various scenarios to compare performances of different algorithms.
External jars required for package RKEA.
This package provides an interface to the OAuth 1.0 specification allowing users to authenticate via OAuth to the server of their choice.
Rcpp reimplementation of the the Bayesian non-parametric Dirichlet Process Regression model for penalized regression first published in Zeng and Zhou (2017) <doi:10.1038/s41467-017-00470-2>. A full Bayesian version is implemented with Gibbs sampling, as well as a faster but less accurate variational Bayes approximation.
This package provides a data structure and toolkit for documenting and recoding categorical data that can be shared in other statistical software.
This package provides an interface to the Python package Geomstats authored by Miolane et al. (2020) <arXiv:2004.04667>.
Use JSON templates to create folders and files structure for data science projects. Includes customized templates and accepts your own as JSON files.
This package provides functions to generate plots and tables for comparing independently- sampled populations. Companion package to "A Primer on Visualizations for Comparing Populations, Including the Issue of Overlapping Confidence Intervals" by Wright, Klein, and Wieczorek (2019) <DOI:10.1080/00031305.2017.1392359> and "A Joint Confidence Region for an Overall Ranking of Populations" by Klein, Wright, and Wieczorek (2020) <DOI:10.1111/rssc.12402>.
This package provides a robust and powerful approach is developed for replicability analysis of two Genome-wide association studies (GWASs) accounting for the linkage disequilibrium (LD) among genetic variants. The LD structure in two GWASs is captured by a four-state hidden Markov model (HMM). The unknowns involved in the HMM are estimated by an efficient expectation-maximization (EM) algorithm in combination with a non-parametric estimation of functions. By incorporating information from adjacent locations via the HMM, this approach identifies the entire clusters of genotype-phenotype associated signals, improving the power of replicability analysis while effectively controlling the false discovery rate.
Utility functions to download data from the RESOURCECODE hindcast database of sea-states, time series of sea-state parameters and time series of 1D and 2D wave spectra. See <https://resourcecode.ifremer.fr> for more details about the available data. Also provides facilities to plot and analyse downloaded data, such as computing the sea-state parameters from both the 1D and 2D surface elevation variance spectral density.
Routines to select and visualize the maxima for a given strict partial order. This especially includes the computation of the Pareto frontier, also known as (Top-k) Skyline operator (see Börzsönyi, et al. (2001) <doi:10.1109/ICDE.2001.914855>), and some generalizations known as database preferences (see Kieà ling (2002) <doi:10.1016/B978-155860869-6/50035-4>).
This package provides several non parametric randomness tests for numeric sequences.
This package provides a suite of tools useful to read, visualize and export bivariate motion energy time-series. Lagged synchrony between subjects can be analyzed through windowed cross-correlation. Surrogate data generation allows an estimation of pseudosynchrony that helps to estimate the effect size of the observed synchronization. Kleinbub, J. R., & Ramseyer, F. T. (2020). rMEA: An R package to assess nonverbal synchronization in motion energy analysis time-series. Psychotherapy research, 1-14. <doi:10.1080/10503307.2020.1844334>.
Restricted Cubic Splines were performed to explore the shape of association form of "U, inverted U, L" shape and test linearity or non-linearity base on "Cox,Logistic,linear,quasipoisson" regression, and auto output Restricted Cubic Splines figures. rcssci package could automatically draw RCS graphics with Y-axis "OR,HR,RR,beta". The Restricted Cubic Splines method were based on Suli Huang (2022) <doi:10.1016/j.ecoenv.2022.113183>,Amit Kaura (2019) <doi:10.1136/bmj.l6055>, and Harrell Jr (2015, ISBN:978-3-319-19424-0 (Print) 978-3-319-19425-7 (Online)).
Download and import agricultural data from the Australian Bureau of Agricultural and Resource Economics and Sciences (ABARES) <https://www.agriculture.gov.au/abares> and Australian Bureau of Statistics (ABS) <https://www.abs.gov.au>. Data types serviced include spreadsheets, comma separated value (CSV) files, geospatial data including shape files and geotiffs covering topics including broadacre crops, livestock, soil data, commodities and more. Unifies field names and formats for data interoperability making analysis easier by standardising names between data formats. Also simplifies importing geospatial data as well as correcting issues in the geospatial data upon import.
Create custom keyboard shortcuts to examine code selected in the Rstudio editor. F3 can for example yield str(selection) and F7 open the source code of CRAN and base package functions on github'.
R functions for generating and/or displaying random Chuck Norris facts. Based on data from the Internet Chuck Norris database ('ICNDb').
Generates disease-specific drug-response profiles that are independent of time, concentration, and cell-line. Based on the cell lines used as surrogates, the returned profiles represent the unique transcriptional changes induced by a compound in a given disease.