vegIndexCalc - Vegetation Indices (VIs) Calculation for Remote Sensing Analysis

It provides a comprehensive toolkit for calculating a suite of common vegetation indices (VIs) derived from remote sensing imagery. VIs are essential tools used to quantify vegetation characteristics, such as biomass, leaf area index (LAI) and photosynthetic activity, which are essential parameters in various ecological, agricultural, and environmental studies. Applications of this package include biomass estimation, crop monitoring, forest management, land use and land cover change analysis and climate change studies. For method details see, Deb,D.,Deb,S.,Chakraborty,D.,Singh,J.P.,Singh,A.K.,Dutta,P.and Choudhury,A.(2020)<doi:10.1080/10106049.2020.1756461>. Utilizing this R package, users can effectively extract and analyze critical information from remote sensing imagery, enhancing their comprehension of vegetation dynamics and their importance in global ecosystems. The package includes the function vegetation_indices().

Last updated 2 months ago

1.00 score 214 downloads

minFactorial - All Possible Minimally Changed Factorial Run Orders

In many agricultural, engineering, industrial, post-harvest and processing experiments, the number of factor level changes and hence the total number of changes is of serious concern as such experiments may consists of hard-to-change factors where it is physically very difficult to change levels of some factors or sometime such experiments may require normalization time to obtain adequate operating condition. For this reason, run orders that offer the minimum number of factor level changes and at the same time minimize the possible influence of systematic trend effects on the experimentation have been sought. Factorial designs with minimum changes in factors level may be preferred for such situations as these minimally changed run orders will minimize the cost of the experiments. For method details see, Bhowmik, A.,Varghese, E., Jaggi, S. and Varghese, C. (2017)<doi:10.1080/03610926.2016.1152490>.This package used to construct all possible minimally changed factorial run orders for different experimental set ups along with different statistical criteria to measure the performance of these designs. It consist of the function minFactDesign().

Last updated 4 months ago

1.00 score 509 downloads

equiBSPD - Equivalent Estimation Balanced Split Plot Designs

In agricultural, post-harvest and processing, engineering and industrial experiments factors are often differentiated with ease with which they can change from experimental run to experimental run. This is due to the fact that one or more factors may be expensive or time consuming to change i.e. hard-to-change factors. These factors restrict the use of complete randomization as it may make the experiment expensive and time consuming. Split plot designs can be used for such situations. In general model estimation of split plot designs require the use of generalized least squares (GLS). However for some split-plot designs ordinary least squares (OLS) estimates are equivalent to generalized least squares (GLS) estimates. These types of designs are known in literature as equivalent-estimation split-plot design. For method details see, Macharia, H. and Goos, P.(2010) <doi:10.1080/00224065.2010.11917833>.Balanced split plot designs are designs which have an equal number of subplots within every whole plot. This package used to construct equivalent estimation balanced split plot designs for different experimental set ups along with different statistical criteria to measure the performance of these designs. It consist of the function equivalent_BSPD().

Last updated 6 months ago

1.00 score 250 downloads

hrtlFMC - Half Replicate of Two Level Factorial Run Order with Minimum Level Changes

It is used to construct run sequences with minimum changes for half replicate of two level factorial run order. Experimenter can save time and resources by minimizing the number of changes in levels of individual factor and therefore the total number of changes. It consists of the function minimal_hrtlf(). This technique can be employed to any half replicate of two level factorial run order where the number of factors are greater than two. In Design of Experiments (DOE) theory, two level of a factor can be represented as integers e.g. - 1 for low and 1 for high. User is expected to enter total number of factors to be considered in the experiment. minimal_hrtlf() provides the required run sequences for the input number of factors. The output also gives the number of changes of each factor along with total number of changes in the run sequence. Due to restricted randomization the minimally changed run sequences of half replicate of two level factorial run order will be affected by trend effect. The output also provides the Trend Factor value of the run order. Trend factor value will lies between 0 to 1. Higher the values, lesser the influence of trend effects on the run order.

Last updated 6 months ago

1.00 score 249 downloads