Found 123 packages in 0.09 seconds
Geostatistics Methods and Klovan Data
A comprehensive set of geostatistical, visual, and analytical methods, in conjunction with the expanded version of the acclaimed J.E. Klovan's mining dataset, are included in 'klovan'. This makes the package an excellent learning resource for Principal Component Analysis (PCA), Factor Analysis (FA), kriging, and other geostatistical techniques. Originally published in the 1976 book 'Geological Factor Analysis', the included mining dataset was assembled by Professor J. E. Klovan of the University of Calgary. Being one of the first applications of FA in the geosciences, this dataset has significant historical importance. As a well-regarded and published dataset, it is an excellent resource for demonstrating the capabilities of PCA, FA, kriging, and other geostatistical techniques in geosciences. For those interested in these methods, the 'klovan' datasets provide a valuable and illustrative resource. Note that some methods require the 'RGeostats' package. Please refer to the README or Additional_repositories for installation instructions. This material is based upon research in the Materials Data Science for Stockpile Stewardship Center of Excellence (MDS3-COE), and supported by the Department of Energy's National Nuclear Security Administration under Award Number DE-NA0004104.
Facilities for Simulating from ODE-Based Models
Facilities for running simulations from ordinary differential equation ('ODE') models, such as pharmacometrics and other compartmental models. A compilation manager translates the ODE model into C, compiles it, and dynamically loads the object code into R for improved computational efficiency. An event table object facilitates the specification of complex dosing regimens (optional) and sampling schedules. NB: The use of this package requires both C and Fortran compilers, for details on their use with R please see Section 6.3, Appendix A, and Appendix D in the "R Administration and Installation" manual. Also the code is mostly released under GPL. The 'VODE' and 'LSODA' are in the public domain. The information is available in the inst/COPYRIGHTS.
General Polygon Clipping Library for R
General polygon clipping routines for R based on Alan Murta's C library.
Acceptance Sampling Plans Design
Provides tools for designing and analyzing acceptance sampling plans. Supports both attribute-based (Binomial and Poisson) and variable-based (Normal and Beta) sampling, enabling quality control for fractional and compositional data. Uses nonlinear programming for sampling plan optimization, minimizing sample size while balancing producer's and consumer's risks. Operating Characteristic curves are available for plan visualization.
Mixed Models for Repeated Measures
Mixed models for repeated measures (MMRM) are a popular
choice for analyzing longitudinal continuous outcomes in randomized
clinical trials and beyond; see Cnaan, Laird and Slasor (1997)
General Package for Meta-Analysis
User-friendly general package providing standard methods for meta-analysis and supporting Schwarzer, Carpenter, and Rücker
CONCOR for Structural- And Regular-Equivalence Blockmodeling
The four functions svdcp() ('cp' for column partitioned), svdbip() or svdbip2() ('bip' for bipartitioned), and svdbips() ('s' for a simultaneous optimization of a set of 'r' solutions), correspond to a singular value decomposition (SVD) by blocks notion, by supposing each block depending on relative subspaces, rather than on two whole spaces as usual SVD does. The other functions, based on this notion, are relative to two column partitioned data matrices x and y defining two sets of subsets x_i and y_j of variables and amount to estimate a link between x_i and y_j for the pair (x_i, y_j) relatively to the links associated to all the other pairs. These methods were first presented in: Lafosse R. & Hanafi M.,(1997) < https://eudml.org/doc/106424> and Hanafi M. & Lafosse, R. (2001) < https://eudml.org/doc/106494>.
Bayesian Analysis of the Network Autocorrelation Model
The network autocorrelation model (NAM) can be used for studying the degree of social influence
regarding an outcome variable based on one or more known networks.
The degree of social influence is quantified via the network autocorrelation parameters. In case of a single
network, the Bayesian methods of Dittrich, Leenders, and Mulder
(2017)
R Wrappers for EXPOKIT; Other Matrix Functions
Wraps some of the matrix exponentiation utilities from EXPOKIT (< http://www.maths.uq.edu.au/expokit/>), a FORTRAN library that is widely recommended for matrix exponentiation (Sidje RB, 1998. "Expokit: A Software Package for Computing Matrix Exponentials." ACM Trans. Math. Softw. 24(1): 130-156). EXPOKIT includes functions for exponentiating both small, dense matrices, and large, sparse matrices (in sparse matrices, most of the cells have value 0). Rapid matrix exponentiation is useful in phylogenetics when we have a large number of states (as we do when we are inferring the history of transitions between the possible geographic ranges of a species), but is probably useful in other ways as well. NOTE: In case FORTRAN checks temporarily get rexpokit archived on CRAN, see archived binaries at GitHub in: nmatzke/Matzke_R_binaries (binaries install without compilation of source code).
Joint Mean and Covariance Estimation for Matrix-Variate Data
Jointly estimates two-group means and covariances
for matrix-variate data and calculates test statistics.
This package implements the algorithms defined in
Hornstein, Fan, Shedden, and Zhou (2018)