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Beginner’s Guide To Use WordPress Theme Customizer (Ultimate Guide 2021) › Servo Node

This tutorial or guide to use WordPress Theme Customizer is for those who’s a newbie to WordPress and just finished installing WordPress for his website. Selecting a suitable theme for your brand and requirement, is the very first step to create an attractive website. But even after the theme is selected, how further customization and website designing should be performed? Here what the WordPress Theme Customizer and its usage can be helpful.

What is WordPress Theme Customizer?
WordPress Theme Customizer is WordPress in-built feature that allows users to design and style their WordPress website. Also, it offers to preview any changes before publishing the design changes to front end. So, no matters you have selected the best theme for your brand, you need to do some refinements as well to meet your proper purposes. And in order to do so, you should aware of how to use WordPress theme customizer.

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WordPress Theme Customizer Options Explained:
Typography: Used for changing font settings and typography styles
Site Identity: Here, users can assign a site title, tagline, and upload site logo, icon, and so on.
Colors: Used for changing color preferences like background color, link color, etc.
Header Image: Under this section, user can set a header image.
Background Image: Allows to set a background image for WordPress website.
Menus: This section is used for creating new site menus, assigning its positions, and many more.
Widgets: Under this section, users can add different widgets in sidebar, footer, homepage, etc
Homepage Settings: Mostly, this section is used for assigning homepage as either a static page or blog page.
Theme Settings: This section is dependent on certain themes, allows more additional features offered by Theme authors.
Additional CSS: Custom or additional CSS can be assigned here. If you are expert to deal with CSS, HTML, and core designing skills, this section can be very helpful.

How To Use WordPress Theme Customizer?
After looking through various available features under theme customizer in WordPress, obviously there’s many thing you can customize within your website or blog. So let;s learn each above discussed options one by one.

1: Changing Default Website Fonts (Typography)
2: Site Title, Logo, and Favicon Customization (Site Identity)
3: Website Color Scheme Customization (Colors)
4: Header Image Customization (Header Image)
5: Customizing Background Image
6: Customizing Navigation Menus
7: Customizing WordPress Widgets
8: Customizing Homepage Settings
9: Customizing Theme Settings
10: Customizing Additional CSS

Need More Customizations In WordPress site?
The standard customizer settings is all here discussed in this article, but in case if you need more modifications which are not here specified, you can do so using WordPress Plugin. You can easily search and find more customization plugins which can add more tweaking options to your WordPress blog easily.

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20 Surprisingly Free WordPress Themes 2021

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• Yoome is a WooCommerce WordPress theme designed for shopping online stores. Yoome includes a lot of pre-designed layouts for home page, product page to give you best selections in customization.
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RobustStats.jl: A Collection Of Robust Statistical Tests in Julia

RobustStats

This package contains a variety of functions from the field robust statistical methods. Many are estimators of location or dispersion; others estimate the standard error or the confidence intervals for the location or dispresion estimators, generally computed by the bootstrap method.

Many functions in this package are based on the R package WRS (an R-Forge repository) by Rand Wilcox. Others were contributed by users as needed. References to the statistics literature can be found below.

This package requires Compat, Rmath, Dataframes, and Distributions. They can be installed automatically, or by invoking Pkg.add("packagename").

Estimators

Location estimators:

• tmean(x, tr=0.2) - Trimmed mean: mean of data with the lowest and highest fraction tr of values omitted.
• winmean(x, tr=0.2)- Winsorized mean: mean of data with the lowest and highest fraction tr of values squashed to the 20%ile or 80%ile value, respectively.
• tauloc(x) - Tau measure of location by Yohai and Zamar.
• onestep(x) - One-step M-estimator of location using Huber's ψ
• mom(x) - Modified one-step M-estimator of location (MOM)
• bisquareWM(x) - Mean with weights given by the bisquare rho function.
• huberWM(x) - Mean with weights given by Huber's rho function.
• trimean(x) - Tukey's trimean, the average of the median and the midhinge.

Dispersion estimators:

• winvar(x, tr=0.2) - Winsorized variance.
• wincov(x, y, tr=0.2) - Winsorized covariance.
• pbvar(x) - Percentage bend midvariance.
• bivar(x) - Biweight midvariance.
• tauvar(x) - Tau measure of scale by Yohai and Zamar.
• iqrn(x) - Normalized inter-quartile range (normalized to equal σ for Gaussians).
• shorthrange(x) - Length of the shortest closed interval containing at least half the data.
• scaleQ(x) - Normalized Rousseeuw & Croux Q statistic, from the 25%ile of all 2-point distances.
• scaleS(x) - Normalized Rousseeuw & Croux S statistic, from the median of the median of all 2-point distances.
• shorthrange!(x), scaleQ!(x), and scaleS!(x) are non-copying (that is, x-modifying) forms of the above.

Confidence interval or standard error estimates:

• trimse(x) - Standard error of the trimmed mean.
• trimci(x) - Confidence interval for the trimmed mean.
• msmedse(x) - Standard error of the median.
• binomci(s,n) - Binomial confidence interval (Pratt's method).
• acbinomci(s,n) - Binomial confidence interval (Agresti-Coull method).
• sint(x) - Confidence interval for the median (with optional p-value).
• momci(x) - Confidence interval of the modified one-step M-estimator of location (MOM).
• trimpb(x) - Confidence interval for trimmed mean.
• pcorb(x) - Confidence intervale for Pearson's correlation coefficient.
• yuend - Compare the trimmed means of two dependent random variables.
• bootstrapci(x, est=f) - Compute a confidence interval for estimator f(x) by bootstrap methods.
• bootstrapse(x, est=f) - Compute a standard error of estimator f(x) by bootstrap methods.

Utility functions:

• winval(x, tr=0.2) - Return a Winsorized copy of the data.
• idealf(x) - Ideal fourths, interpolated 1st and 3rd quartiles.
• outbox(x) - Outlier detection.
• hpsi(x) - Huber's ψ function.
• contam_randn - Contaminated normal distribution (generates random deviates).
• _weightedhighmedian(x) - Weighted median (breaks ties by rounding up). Used in scaleQ.

Recommendations:

For location, consider the bisquareWM with k=3.9σ, if you can make any reasonable guess as to the "Gaussian-like width" σ (see dispersion estimators for this). If not, trimean is a good second choice, though less efficient. Also, though the author personally has no experience with them, tauloc, onestep, and mom might be useful.

For dispersion, the scaleS is a good general choice, though scaleQ is very efficient for nearly Gaussian data. The MAD is the most robust though less efficient. If scaleS doesn't work, then shorthrange is a good second choice.

The first reference on scaleQ and scaleS (below) is a lengthy discussion of the tradeoffs among scaleQ, scaleS, shortest half, and median absolute deviation (MAD, see BaseStats.mad for Julia implementation). All four have the virtue of having the maximum possible breakdown point, 50%. This means that replacing up to 50% of the data with unbounded bad values leaves the statistic still bounded. The efficiency of Q is better than S and S is better than MAD (for Gaussian distributions), and the influence of a single bad point and the bias due to a fraction of bad points is only slightly larger on Q or S than on MAD. Unlike MAD, the other three do not implicitly assume a symmetric distribution.

To choose between Q and S, the authors note that Q has higher statistical efficiency, but S is typically twice as fast to compute and has lower gross-error sensitivity. An interesting advantage of Q over the others is that its influence function is continuous. For a rough idea about the efficiency, the large-N limit of the standardized variance of each quantity is 2.722 for MAD, 1.714 for S, and 1.216 for Q, relative to 1.000 for the standard deviation (given Gaussian data). The paper gives the ratios for Cauchy and exponential distributions, too; the efficiency advantages of Q are less for Cauchy than for the other distributions.

Examples

#Set up a sample dataset:
x=[1.672064, 0.7876588, 0.317322, 0.9721646, 0.4004206, 1.665123, 3.059971, 0.09459603, 1.27424, 3.522148,
   0.8211308, 1.328767, 2.825956, 0.1102891, 0.06314285, 2.59152, 8.624108, 0.6516885, 5.770285, 0.5154299]

julia> mean(x)     #the mean of this dataset
1.853401259

tmean: trimmed mean

julia> tmean(x)            #20% trimming by default
1.2921802666666669

julia> tmean(x, tr=0)      #no trimming; the same as the output of mean()
1.853401259

julia> tmean(x, tr=0.3)    #30% trimming
1.1466045875000002

julia> tmean(x, tr=0.5)    #50% trimming, which gives you the median of the dataset.
1.1232023

winval: winsorize data

That is, return a copy of the input array, with the extreme low or high values replaced by the lowest or highest non-extreme value, repectively. The fraction considered extreme can be between 0 and 0.5, with 0.2 as the default.

julia> winval(x)           #20% winsorization; can be changed via the named argument `tr`.
20-element Any Array:
 1.67206
 0.787659
 0.400421
 0.972165
 ...
 0.651689
 2.82596
 0.51543

winmean, winvar, wincov: winsorized mean, variance, and covariance

julia> winmean(x)          #20% winsorization; can be changed via the named argument `tr`.
1.4205834800000001
julia> winvar(x)
0.998659015947531
julia> wincov(x, x)
0.998659015947531
julia> wincov(x, x.^2)
3.2819238397424004

trimse: estimated standard error of the trimmed mean

julia> trimse(x)           #20% winsorization; can be changed via the named argument `tr`.
0.3724280347984342

trimci: (1-α) confidence interval for the trimmed mean

Can be used for paired groups if x consists of the difference scores of two paired groups.

julia> trimci(x)                 #20% winsorization; can be changed via the named argument `tr`.
(1-α) confidence interval for the trimmed mean

Degrees of freedom:   11
Estimate:             1.292180
Statistic:            3.469611
Confidence interval:  0.472472       2.111889
p value:              0.005244

idealf: the ideal fourths:

Returns (q1,q3), the 1st and 3rd quartiles. These will be a weighted sum of the values that bracket the exact quartiles, analogous to how we handle the median of an even-length array.

julia> idealf(x)
(0.4483411416666667,2.7282743333333332)

pbvar: percentage bend midvariance

A robust estimator of scale (dispersion). See NIST ITL webpage for more.

julia> pbvar(x)
2.0009575278957623

bivar: biweight midvariance

A robust estimator of scale (dispersion). See NIST ITL webpage for more.

julia> bivar(x)
1.5885279811329132

tauloc, tauvar: tau measure of location and scale

Robust estimators of location and scale, with breakdown points of 50%.

See Yohai and Zamar JASA, vol 83 (1988), pp 406-413 and Maronna and Zamar Technometrics, vol 44 (2002), pp. 307-317.

julia> tauloc(x)       #the named argument `cval` is 4.5 by default.
1.2696652567510853
julia> tauvar(x)
1.53008203090696

outbox: outlier detection

Use a modified boxplot rule based on the ideal fourths; when the named argument mbox is set to true, a modification of the boxplot rule suggested by Carling (2000) is used.

julia> outbox(x)
Outlier detection method using
the ideal-fourths based boxplot rule

Outlier ID:         17
Outlier value:      8.62411
Number of outliers: 1
Non-outlier ID:     1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 19, 20

msmedse: Standard error of the median

Return the standard error of the median, computed through the method recommended by McKean and Schrader (1984).

julia> msmedse(x)
0.4708261134886094

binomci(), acbinomci(): Binomial confidence interval

Compute the (1-α) confidence interval for p, the binomial probability of success, given s successes in n trials. Instead of s and n, can use a vector x whose values are all 0 and 1, recording failure/success one trial at a time. Returns an object.

binomci uses Pratt's method; acbinomci uses a generalization of the Agresti-Coull method that was studied by Brown, Cai, & DasGupta.

julia> binomci(2, 10)           # # of success and # of total trials are provided. By default alpha=.05
p_hat:               0.2000
confidence interval: 0.0274   0.5562
Sample size          10


julia> trials=[1, 0, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0]
julia> binomci(trials, alpha=0.01)    #trial results are provided in array form consisting of 1's and 0's.
 p_hat:               0.5000
 confidence interval: 0.1768   0.8495
 Sample size          12

julia> acbinomci(2, 10)           # # of success and # of total trials are provided. By default alpha=.05
p_hat:               0.2000
confidence interval: 0.0459   0.5206
Sample size          10

sint()

Compute the confidence interval for the median. Optionally, uses the Hettmansperger-Sheather interpolation method to also estimate a p-value.

julia> sint(x)
Confidence interval for the median

 Confidence interval:  0.547483       2.375232

julia> sint(x, 0.6)
Confidence interval for the median with p-val

 Confidence interval:  0.547483       2.375232
 p value:              0.071861

hpsi

Compute Huber's ψ. The default bending constant is 1.28.

julia> hpsi(x)
20-element Array{Float64,1}:
1.28
0.787659
0.317322
0.972165
0.400421
...

onestep

Compute one-step M-estimator of location using Huber's ψ. The default bending constant is 1.28.

julia> onestep(x)
1.3058109021286803

bootstrapci, bootstrapse

Compute a bootstrap, (1-α) confidence interval (bootstrapci) or a standard error (bootstrapse) for the measure of location corresponding to the argument est. By default, the median is used. Default α=0.05.

julia> ci = bootstrapci(x, est=onestep, nullvalue=0.6)
 Estimate:             1.305811
 Confidence interval:  0.687723       2.259071
 p value:              0.026000


julia> se = bootstrapse(x, est=onestep)
0.41956761772722817

mom and mom!

Returns a modified one-step M-estimator of location (MOM), which is the unweighted mean of all values not more than (bend times the mad(x)) away from the data median.

julia> mom(x)
1.2596462322222222

momci

Compute the bootstrap (1-α) confidence interval for the MOM-estimator of location based on Huber's ψ. Default α=0.05.

julia> momci(x, seed=2, nboot=2000, nullvalue=0.6)
Estimate:             1.259646
Confidence interval:  0.504223       2.120979
p value:              0.131000

contam_randn

Create contaminated normal distributions. Most values will by from a N(0,1) zero-mean unit-variance normal distribution. A fraction epsilon of all values will have k times the standard devation of the others. Default: epsilon=0.1 and k=10.

julia> srand(1);
julia> std(contam_randn(2000))
3.516722458797104

trimpb

Compute a (1-α) confidence interval for a trimmed mean by bootstrap methods.

julia> trimpb(x, nullvalue=0.75)
 Estimate:             1.292180
 Confidence interval:  0.690539       2.196381
 p value:              0.086000

pcorb

Compute a .95 confidence interval for Pearson's correlation coefficient. This function uses an adjusted percentile bootstrap method that gives good results when the error term is heteroscedastic.

julia> pcorb(x, x.^5)
 Estimate:             0.802639
 Confidence interval:  0.683700       0.963478

yuend

Compare the trimmed means of two dependent random variables using the data in x and y. The default amount of trimming is 20%.

julia> srand(3)
julia> y2 = randn(20)+3;
julia> yuend(x, y2)

Comparing the trimmed means of two dependent variables.

Sample size:          20
Degrees of freedom:   11
Estimate:            -1.547776
Standard error:       0.460304
Statistic:           -3.362507
Confidence interval: -2.560898      -0.534653
p value:              0.006336

Unmaintained functions

See UNMAINTAINED.md for information about functions that the maintainers have not yet understood but also not yet deleted entirely.

References

Percentage bend and related estimators come from L.H. Shoemaker and T.P. Hettmansperger "Robust estimates and tests for the one- and two-sample scale models" in Biometrika Vol 69 (1982) pp. 47-53.

Tau measures of location and scale are from V.J. Yohai and R.H. Zamar "High Breakdown-Point Estimates of Regression by Means of the Minimization of an Efficient Scale" in J. American Statistical Assoc. vol 83 (1988) pp. 406-413.

The outbox(..., mbox=true) modification was suggested in K. Carling, "Resistant outlier rules and the non-Gaussian case" in Computational Statistics and Data Analysis vol 33 (2000), pp. 249-258. doi:10.1016/S0167-9473(99)00057-2

The estimate of the standard error of the median, msmedse(x), is computed by the method of J.W. McKean and R.M. Schrader, "A comparison of methods for studentizing the sample median" in Communications in Statistics: Simulation and Computation vol 13 (1984) pp. 751-773. doi:10.1080/03610918408812413

For Pratt's method of computing binomial confidence intervals, see J.W. Pratt (1968) "A normal approximation for binomial, F, Beta, and other common, related tail probabilities, II" J. American Statistical Assoc., vol 63, pp. 1457- 1483, doi:10.1080/01621459.1968.10480939. Also R.G. Newcombe "Confidence Intervals for a binomial proportion" Stat. in Medicine vol 13 (1994) pp 1283-1285, doi:10.1002/sim.4780131209.

For the Agresti-Coull method of computing binomial confidence intervals, see L.D. Brown, T.T. Cai, & A. DasGupta "Confidence Intervals for a Binomial Proportion and Asymptotic Expansions" in Annals of Statistics, vol 30 (2002), pp. 160-201.

Shortest Half-range comes from P.J. Rousseeuw and A.M. Leroy, "A Robust Scale Estimator Based on the Shortest Half" in Statistica Neerlandica Vol 42 (1988), pp. 103-116. doi:10.1111/j.1467-9574.1988.tb01224.x . See also R.D. Martin and R. H. Zamar, "Bias-Robust Estimation of Scale" in Annals of Statistics Vol 21 (1993) pp. 991-1017. doi:10.1214/aoe/1176349161

Scale-Q and Scale-S statistics are described in P.J. Rousseeuw and C. Croux "Alternatives to the Median Absolute Deviation" in J. American Statistical Assoc. Vo 88 (1993) pp 1273-1283. The time-efficient algorithms for computing them appear in C. Croux and P.J. Rousseeuw, "Time-Efficient Algorithms for Two Highly Robust Estimators of Scale" in Computational Statistics, Vol I (1992), Y. Dodge and J. Whittaker editors, Heidelberg, Physica-Verlag, pp 411-428. If link fails, see ftp://ftp.win.ua.ac.be/pub/preprints/92/Timeff92.pdf

---

Download Details:

Author: Mrxiaohe
Source Code: https://github.com/mrxiaohe/RobustStats.jl 
License: MIT license

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