But what are Sildur's shaders?
Sildur's Shaders is an extension of the GLSL shader mod for Minecraft, now part of Optifine.
Vibrant shaders completly revamps the lighting system of minecraft and adds advanced effects like volumetric lighting, bloom, ambient occlusion and reflections. While still maintaining high performance.
Enhanced default does what the name indicates, instead of completly revamping the style of the game it maintains the vanilla look and adds shadows, reflections and even godrays. It's very lightweight and highly customizable, so even the worst potato is able to run it.
Fabulous shaders is a shaderpack/resourcepack that uses mojangs inbuild shaders function and require the fabulous setting from 1.16+ to work.
My shaderpacks are designed to work on all graphics cards and computers, including Macs, while still delivering a great graphical experience.
Aesthetic Shader Shaders Landscaping Voyager 1.14.2 1.14 1.13.1 1.12.2 1.12 1.11 1.14.4 1.16 1.16.1 1.16.2 This shaders work on every version of Minecraft if your Optifine is up to dateIf you encounter a problem or if you want to make a suggestion, do not hesitate to leave a comment and do not hesitate to offer me mods to which the shader can be supported. Downloads for Minecraft Forge for Minecraft 1.16.5 Latest: 36.1.6 Recommended: 36.1.0. Welcome to MCStacker for Minecraft 1.16. This website will help you generate many commands for Minecraft Java Edition. Many of the specific options available for items and blocks can be generated with this tool. MCStacker began in 2014 and many new features have been added regularly ever since. Minecraft 1.16 is a major update that is quite interesting and elaborated, as it concerns the Nether. Previously, the developers hinted that they planned to make changes to this dimension, but the players could not even imagine that it would be so large and interesting. Because now you can go to this dimension and re-explore everything. RT1 Realistic TexturePack (1.16 above) By M.Sameed C. 512x 1.16 Texture Pack. 512x Resolution Minecraft 1.16 Game Version. MSameed2006 04/14/21. posted. DJ's Zelda Botw Pack. 32x 1.16 Texture Pack. 32x Resolution Minecraft 1.
Optifine shaders, supported MC versions: 1.7.10 - 1.16.5
1. Download and install Minecraft, setup your game profile of the version you want to run.
2. Download and run the optifine jar file with java. It will install a new optifine profile.
3. Run the minecraft launcher and select the newly created optifine profile.
4. Start the game.
5. Goto options -> video setings -> shaders and press on the 'Shaders folders' button at the bottom of the screen.
6. Download my shaderpacks and place them in the newly opened window.
7. Select my shaderpack from the list ingame and enjoy your new visuals!
Fabulous shaders using mojangs inbuild shaders support, supported MC version 1.16.5
1. Download the resourcepack and move it into your resourcepack folder.
2. Launch Minecraft and select the resourcepack from the list.
3. Go to video settings - options and set graphics to fabulous.
4. That's it!
If you want to tweak effects to your liking, go into the resourcepack assetsminecraftshadersprogram' and edit sildurs_shaders.fsh with something like notepadd++. Example: //#define Bloom means Bloom is disabled, removing the two slashes - #Bloom enables it.
Vibrant Shaders (Latest update: January 20, 2021):
| Sildur's Vibrant shaders v1.283 Lite | Download |
| Sildur's Vibrant shaders v1.283 Medium | Download |
| Sildur's Vibrant shaders v1.283 High | Download |
| Sildur's Vibrant shaders v1.283 High-Motionblur | Download |
| Sildur's Vibrant shaders v1.283 Extreme | Download |
| Sildur's Vibrant shaders v1.283 Extreme-Volumetric lighting | Download |
| Changelog |
| Sildur's Enhanced Default v1.12 | Download |
| Changelog |
| Sildur's fabulous shaders v1.0 | Download |
| How to Install | |
| Changelog |
recipes, tidymodels
Max Kuhn
We’re tickled pink to announce the release of recipes 0.1.16. recipes is a package for preprocessing data for modeling and data analysis.
You can install it from CRAN with:
This blog post will discuss the several improvements to the package. Before discussing new features, please note that the package license was changed from GPL-2 to MIT.
You can see a full list of changes in the release notes.
New column selectors
We do our best to keep track of persistent issues that show up in our teaching, Stack Overflow posts, RStudio Community posts, the R4DS Tidy Modeling Book Club, and other venues. If there are persistent issues, we do our best to help make the programming interface better.
Mine Çetinkaya-Rundel had a good idea for one such persistent issue related to creating dummy variables. For classification data where one or more predictors are categorical, the users might accidentally capture the outcome and the predictors when creating dummy variables. For example:
Note that the outcome column (Species) was made into binary indicators. Most classification models prefer a factor vector and this would cause errors. The fix would be to remember to remove Species from the step selector.
Most selectors in recipes are used to capture predictor columns. The new version of recipes contains new selectors that combine the role and the data type: all_nominal_predictors() and all_numeric_predictors(). Using these:
1.16 1 Xray
The existing selectors will remain. We’ll be converting our documentation, books, and training to use these new selectors when we select predictors of a specific type.
New steps
A new selector was added to compliment step_rm() (which removes columns). The new step_select() declares which columns to retain and emulates dplyr::select().
In cases where there are missing data, some data analysis methods compliment the existing predictors with missing value indicators for the covariates that have incomplete values. Thanks to Konrad Semsch, step_indicate_na() can be used to create these. Using the previous example:
Speaking of missing data, we’ve decided to rename the current eight imputation steps:
step_impute_knn()is favored overstep_knnimpute()step_impute_median()is favored overstep_medianimpute()- and so on…
These are a lot better since they work well with tab-completion. The old steps will go through a gradual deprecation process before being removed at some point in the future.
Keeping columns used in other features
A fair number of steps take one or more columns of the data and convert them to artificial features. For example, principal component regression represents a set of columns as artificial features that are amalgamations of the original data. In some cases, users desired to be able to keep the original columns.
The following steps now have an option called keep_original_cols: step_date(), step_dummy(), step_holiday(), step_ica(), step_isomap(), step_kpca_poly(), step_kpca_rbf(), step_nnmf(), step_pca(), step_pls(), and step_ratio().
For example:
1.16 100 New Updates
Acknowledgements
1.16 1.8.9 Texture Pack
Thanks to everyone who contributed since the previous version: @AshesITR, @BenoitLondon, @CelloJuan, @dfalbel, @EmilHvitfeldt, @gregdenay, @gustavomodelli, @hfrick, @hsbadr, @jake-mason, @jjcurtin, @juliasilge, @konradsemsch, @kylegilde, @LePeti, @LordRudolf, @lukasal, @mattwarkentin, @mikemc, @mine-cetinkaya-rundel, @paudel-arjun, @renanxcortes, @rorynolan, @saadaslam, @schoonees, @topepo, @uriahf, @vadimus202, and @zenggyu.