The geohabnet package is designed to perform a geographically or spatially explicit risk analysis of habitat connectivity. Xing et al (2021) <doi:10.1093/biosci/biaa067> proposed the concept of cropland connectivity as a risk factor for plant pathogen or pest invasions. As the functions in geohabnet were initially developed thinking on cropland connectivity, users are recommended to first be familiar with the concept by looking at the Xing et al paper. In a nutshell, a habitat connectivity analysis combines information from maps of host density, estimates the relative likelihood of pathogen movement between habitat locations in the area of interest, and applies network analysis to calculate the connectivity of habitat locations. The functions of geohabnet are built to conduct a habitat connectivity analysis relying on geographic parameters (spatial resolution and spatial extent), dispersal parameters (in two commonly used dispersal kernels: inverse power law and negative exponential models), and network parameters (link weight thresholds and network metrics). The functionality and main extensions provided by the functions in geohabnet to habitat connectivity analysis are a) Capability to easily calculate the connectivity of locations in a landscape using a single function, such as sensitivity_analysis() or msean(). b) As backbone datasets, the geohabnet package supports the use of two publicly available global datasets to calculate cropland density. The backbone datasets in the geohabnet package include crop distribution maps from Monfreda, C., N. Ramankutty, and J. A. Foley (2008) <doi:10.1029/2007gb002947> "Farming the planet: 2. Geographic distribution of crop areas, yields, physiological types, and net primary production in the year 2000, Global Biogeochem. Cycles, 22, GB1022" and International Food Policy Research Institute (2019) <doi:10.7910/DVN/PRFF8V> "Global Spatially-Disaggregated Crop Production Statistics Data for 2010 Version 2.0, Harvard Dataverse, V4". Users can also provide any other geographic dataset that represents host density. c) Because the geohabnet package allows R users to provide maps of host density (as originally in Xing et al (2021)), host landscape density (representing the geographic distribution of either crops or wild species), or habitat distribution (such as host landscape density adjusted by climate suitability) as inputs, we propose the term habitat connectivity. d) The geohabnet package allows R users to customize parameter values in the habitat connectivity analysis, facilitating context-specific (pathogen- or pest-specific) analyses. e) The geohabnet package allows users to automatically visualize maps of the habitat connectivity of locations resulting from a sensitivity analysis across all customized parameter combinations. The primary functions are msean() and sensitivity analysis(). Most functions in geohabnet provide three main outcomes: i) A map of mean habitat connectivity across parameters selected by the user, ii) a map of variance of habitat connectivity across the selected parameters, and iii) a map of the difference between the ranks of habitat connectivity and habitat density. Each function can be used to generate these maps as final outcomes. Each function can also provide intermediate outcomes, such as the adjacency matrices built to perform the analysis, which can be used in other network analysis. Refer to article at <https://garrettlab.github.io/HabitatConnectivity/articles/analysis.html> to see examples of each function and how to access each of these outcome types. To change parameter values, the file called parameters.yaml stores the parameters and their values, can be accessed using get_parameters() and set new parameter values with set_parameters()'. Users can modify up to ten parameters.

The continuous wavelet transform enables the observation of transient/non-stationary cyclicity in time-series. The goal of cyclostratigraphic studies is to define frequency/period in the depth/time domain. By conducting the continuous wavelet transform on cyclostratigraphic data series one can observe and extract cyclic signals/signatures from signals. These results can then be visualized and interpreted enabling one to identify/interpret cyclicity in the geological record, which can be used to construct astrochronological age-models and identify and interpret cyclicity in past and present climate systems. The WaverideR R package builds upon existing literature and existing codebase. The list of articles which are relevant can be grouped in four subjects; cyclostratigraphic data analysis,example data sets,the (continuous) wavelet transform and astronomical solutions. References for the cyclostratigraphic data analysis articles are: Stephen Meyers (2019) <doi:10.1016/j.earscirev.2018.11.015>. Mingsong Li, Linda Hinnov, Lee Kump (2019) <doi:10.1016/j.cageo.2019.02.011> Stephen Meyers (2012)<doi:10.1029/2012PA002307> Mingsong Li, Lee R. Kump, Linda A. Hinnov, Michael E. Mann (2018) <doi:10.1016/j.epsl.2018.08.041>. Wouters, S., Crucifix, M., Sinnesael, M., Da Silva, A.C., Zeeden, C., Zivanovic, M., Boulvain, F., Devleeschouwer, X. (2022) <doi:10.1016/j.earscirev.2021.103894>. Wouters, S., Da Silva, A.-C., Boulvain, F., and Devleeschouwer, X. (2021) <doi:10.32614/RJ-2021-039>. Huang, Norden E., Zhaohua Wu, Steven R. Long, Kenneth C. Arnold, Xianyao Chen, and Karin Blank (2009) <doi:10.1142/S1793536909000096>. Cleveland, W. S. (1979)<doi:10.1080/01621459.1979.10481038> Hurvich, C.M., Simonoff, J.S., and Tsai, C.L. (1998) <doi:10.1111/1467-9868.00125>, Golub, G., Heath, M. and Wahba, G. (1979) <doi:10.2307/1268518>. References for the example data articles are: Damien Pas, Linda Hinnov, James E. (Jed) Day, Kenneth Kodama, Matthias Sinnesael, Wei Liu (2018) <doi:10.1016/j.epsl.2018.02.010>. Steinhilber, Friedhelm, Abreu, Jacksiel, Beer, Juerg , Brunner, Irene, Christl, Marcus, Fischer, Hubertus, HeikkilA, U., Kubik, Peter, Mann, Mathias, Mccracken, K. , Miller, Heinrich, Miyahara, Hiroko, Oerter, Hans , Wilhelms, Frank. (2012 <doi:10.1073/pnas.1118965109>. Christian Zeeden, Frederik Hilgen, Thomas Westerhold, Lucas Lourens, Ursula Röhl, Torsten Bickert (2013) <doi:10.1016/j.palaeo.2012.11.009>. References for the (continuous) wavelet transform articles are: Morlet, Jean, Georges Arens, Eliane Fourgeau, and Dominique Glard (1982a) <doi:10.1190/1.1441328>. J. Morlet, G. Arens, E. Fourgeau, D. Giard (1982b) <doi:10.1190/1.1441329>. Torrence, C., and G. P. Compo (1998)<https://paos.colorado.edu/research/wavelets/bams_79_01_0061.pdf>, Gouhier TC, Grinsted A, Simko V (2021) <https://github.com/tgouhier/biwavelet>. Angi Roesch and Harald Schmidbauer (2018) <https://CRAN.R-project.org/package=WaveletComp>. Russell, Brian, and Jiajun Han (2016)<https://www.crewes.org/Documents/ResearchReports/2016/CRR201668.pdf>. Gabor, Dennis (1946) <http://genesis.eecg.toronto.edu/gabor1946.pdf>. J. Laskar, P. Robutel, F. Joutel, M. Gastineau, A.C.M. Correia, and B. Levrard, B. (2004) <doi:10.1051/0004-6361:20041335>. Laskar, J., Fienga, A., Gastineau, M., Manche, H. (2011a) <doi:10.1051/0004-6361/201116836>. References for the astronomical solutions articles are: Laskar, J., Gastineau, M., Delisle, J.-B., Farres, A., Fienga, A. (2011b <doi:10.1051/0004-6361/201117504>. J. Laskar (2019) <doi:10.1016/B978-0-12-824360-2.00004-8>. Zeebe, Richard E (2017) <doi:10.3847/1538-3881/aa8cce>. Zeebe, R. E. and Lourens, L. J. (2019) <doi:10.1016/j.epsl.2022.117595>. Richard E. Zeebe Lucas J. Lourens (2022) <doi:10.1126/science.aax0612>.

This package implements numerous methods for testing for, modelling, and correcting for heteroskedasticity in the classical linear regression model. The most novel contribution of the package is found in the functions that implement the as-yet-unpublished auxiliary linear variance models and auxiliary nonlinear variance models that are designed to estimate error variances in a heteroskedastic linear regression model. These models follow principles of statistical learning described in Hastie (2009) <doi:10.1007/978-0-387-21606-5>. The nonlinear version of the model is estimated using quasi-likelihood methods as described in Seber and Wild (2003, ISBN: 0-471-47135-6). Bootstrap methods for approximate confidence intervals for error variances are implemented as described in Efron and Tibshirani (1993, ISBN: 978-1-4899-4541-9), including also the expansion technique described in Hesterberg (2014) <doi:10.1080/00031305.2015.1089789>. The wild bootstrap employed here follows the description in Davidson and Flachaire (2008) <doi:10.1016/j.jeconom.2008.08.003>. Tuning of hyper-parameters makes use of a golden section search function that is modelled after the MATLAB function of Zarnowiec (2022) <https://www.mathworks.com/matlabcentral/fileexchange/25919-golden-section-method-algorithm>. A methodological description of the algorithm can be found in Fox (2021, ISBN: 978-1-003-00957-3). There are 25 different functions that implement hypothesis tests for heteroskedasticity. These include a test based on Anscombe (1961) <https://projecteuclid.org/euclid.bsmsp/1200512155>, Ramsey's (1969) BAMSET Test <doi:10.1111/j.2517-6161.1969.tb00796.x>, the tests of Bickel (1978) <doi:10.1214/aos/1176344124>, Breusch and Pagan (1979) <doi:10.2307/1911963> with and without the modification proposed by Koenker (1981) <doi:10.1016/0304-4076(81)90062-2>, Carapeto and Holt (2003) <doi:10.1080/0266476022000018475>, Cook and Weisberg (1983) <doi:10.1093/biomet/70.1.1> (including their graphical methods), Diblasi and Bowman (1997) <doi:10.1016/S0167-7152(96)00115-0>, Dufour, Khalaf, Bernard, and Genest (2004) <doi:10.1016/j.jeconom.2003.10.024>, Evans and King (1985) <doi:10.1016/0304-4076(85)90085-5> and Evans and King (1988) <doi:10.1016/0304-4076(88)90006-1>, Glejser (1969) <doi:10.1080/01621459.1969.10500976> as formulated by Mittelhammer, Judge and Miller (2000, ISBN: 0-521-62394-4), Godfrey and Orme (1999) <doi:10.1080/07474939908800438>, Goldfeld and Quandt (1965) <doi:10.1080/01621459.1965.10480811>, Harrison and McCabe (1979) <doi:10.1080/01621459.1979.10482544>, Harvey (1976) <doi:10.2307/1913974>, Honda (1989) <doi:10.1111/j.2517-6161.1989.tb01749.x>, Horn (1981) <doi:10.1080/03610928108828074>, Li and Yao (2019) <doi:10.1016/j.ecosta.2018.01.001> with and without the modification of Bai, Pan, and Yin (2016) <doi:10.1007/s11749-017-0575-x>, Rackauskas and Zuokas (2007) <doi:10.1007/s10986-007-0018-6>, Simonoff and Tsai (1994) <doi:10.2307/2986026> with and without the modification of Ferrari, Cysneiros, and Cribari-Neto (2004) <doi:10.1016/S0378-3758(03)00210-6>, Szroeter (1978) <doi:10.2307/1913831>, Verbyla (1993) <doi:10.1111/j.2517-6161.1993.tb01918.x>, White (1980) <doi:10.2307/1912934>, Wilcox and Keselman (2006) <doi:10.1080/10629360500107923>, Yuce (2008) <https://dergipark.org.tr/en/pub/iuekois/issue/8989/112070>, and Zhou, Song, and Thompson (2015) <doi:10.1002/cjs.11252>. Besides these heteroskedasticity tests, there are supporting functions that compute the BLUS residuals of Theil (1965) <doi:10.1080/01621459.1965.10480851>, the conditional two-sided p-values of Kulinskaya (2008) <doi:10.48550/arXiv.0810.2124>, and probabilities for the nonparametric trend statistic of Lehmann (1975, ISBN: 0-816-24996-1). For handling heteroskedasticity, in addition to the new auxiliary variance model methods, there is a function to implement various existing Heteroskedasticity-Consistent Covariance Matrix Estimators from the literature, such as those of White (1980) <doi:10.2307/1912934>, MacKinnon and White (1985) <doi:10.1016/0304-4076(85)90158-7>, Cribari-Neto (2004) <doi:10.1016/S0167-9473(02)00366-3>, Cribari-Neto et al. (2007) <doi:10.1080/03610920601126589>, Cribari-Neto and da Silva (2011) <doi:10.1007/s10182-010-0141-2>, Aftab and Chang (2016) <doi:10.18187/pjsor.v12i2.983>, and Li et al. (2017) <doi:10.1080/00949655.2016.1198906>.

Suite of tools for functional analysis.

Converts elements of roxygen documentation to markdown'.

This package provides JSON parsing capability through the Rapidjson library.

Base S4-classes and functions for robust asymptotic statistics.

Platform Design Info for The Manufacturer's Name RAE230B.

Platform Design Info for The Manufacturer's Name RAE230A.

The rubyzip module provides ways to read from and create zip files.

Platform Design Info for Affymetrix RTA-1_0.

Search R files for not installed packages and run install.packages.

ReadWriter is a set of R functions to read and write files conveniently.

Platform Design Info for The Manufacturer's Name RG_U34C.

Platform Design Info for The Manufacturer's Name RG_U34A.

Platform Design Info for The Manufacturer's Name RG_U34B.

Parser for SQL statements. Currently, it supports parsing of only SELECT statements.

rbtrace shows you method calls happening inside another ruby process in real time.

This package provides a package containing an environment representing the RAE230B.CDF file.

This package provides a package containing an environment representing the RAE230A.CDF file.

The rkafkajars package collects all the external jars required for the rkafka package.

This package provides a package containing an environment representing the Rat230_2.cdf file.

Get information (boards, pins and users) from the Pinterest <http://www.pinterest.com> API.